On May 2022, the offices of the Asset Management Company DWS and its majority owner Deutsche Bank in Frankfurt were raided based on allegations raised in the media that the company was resorting to a practice called Greenwashing, which simply means that their products or services were not as green as marketed or classified.

Greenwashing could happen either inadvertently or deliberately. But ever since Sustainable Finance Disclosure Regulation (SFDR) came into effect in March 2021, Morningstar estimated that 1800 funds have been upgraded or reclassified by their managers from Article 6 to Article 8, or from Article 8 to 9 without articulating why they have been redefined. Simultaneously, global regulatory agencies have also turned their attention to the ESG rating agencies.

Simplified, the European Union Sustainable Finance Disclosure Regulation (SFDR) is a set of rules. These rules apply to the sustainability profile of funds so that end investors are able to decide better which fund to invest in by considering its adherence to the pre-defined metrics for measuring environmental, social, and governance (ESG) outcomes/factors. Central to the SFDR disclosure is Articles 6, 8, and 9.

Demystifying SFDR Articles 6, 8, 9

Article 6: Integration of Sustainability Risk

Financial market participants shall include descriptions of the following in pre-contractual disclosures:

  • How the sustainability risks are integrated into their investment decisions; and
  • The results of the assessment of the likely impacts of sustainability risks on the returns of the financial products they make available

Article 8: Promotion of Environmental or Social Characteristics

Financial product promotes, among other characteristics, environmental or social characteristics, or a combination of those characteristics, provided that the companies in which the investments are made follow good governance practices, the information to be disclosed pursuant to article 6 shall include the following:

  • Information on how those characteristics is met.
  • If an index has been designated as a reference benchmark, information on whether and how this index is consistent with those characteristics.”

Article 9: Transparency of Sustainable Investments

Where a financial product has sustainable investment as its objective (all assets be sustainable investments (with certain exceptions related to hedging or liquidity)) and an index has been designated as a reference benchmark, the information to be disclosed pursuant to article 6 shall be accompanied by the following:

  • Information on how the designated index is aligned with that objective.
  • An explanation as to why and how the designated index aligned with that objective differs from a broad market index.”

As a pension funds, sovereign funds, and family offices, the aim is to be as ethical as possible and invest only in Article 9 funds. Pension funds, for example, are associated with a social objective and hence these voluntarily choose not to invest in organizations investing in arms, tobacco, cannabis, etc., because the last thing that they would like to do is profit from such kinds of anti-social activities.

Therein lies the catch because sometimes it is difficult to ascertain/validate what happens in the supply chain by depending only on the quantitative data appearing on public websites. You could be investing in a company that is engaged in activities that are pursuant to the social and environmental good, but what if somewhere in the supply chain you are inadvertently investing in organizations associated with objectional activities both fiduciary like shell funds as well social such as drug trade, terrorism, etc.

So simply relying on structured information or data is simply not enough. In order to be complete and thorough, there is need to shift through tons of other data, top 10 holdings for example which could provide inputs on how sustainable a fund is. The onus, after all is on the investor to validate whether the funds are indeed article 9, and then decide whether to invest or not.

The Essence of SFDR

The ultimate priority of the European Union (EU) when it comes to Sustainable Finance is to develop a more sustainable economy, integrating environmental, social, and governance (ESG) factors across its core activities and enhancing the importance of sustainability risks.

“Sustainability risk’ on the other hand defines an ESG factor i.e., environmental, social or governance event or condition that in the event it occurs could cause an actual or a potential material negative impact on the value of the investment.

For sustainable economic growth, the European Union’s Legal team came up with the Regulatory and Disclosure framework SFDR that requires:

  • As per EU SFDR regulation, review and validation of marketing material, pre-contractual disclosure, periodic contractual disclosure, and website disclosure
  • Transparency of remuneration policies w.r.t integration of sustainability risks
  • Consideration of Principal Adverse Impacts (PAI)

Through SFDR, transparency at both levels: Entity (Investment advisors, Asset Managers, etc) and Product (Fund) can be facilitated.

Entity Level

Integration of sustainability risks into its investment decision-making processes.Website DisclosureArticle 3
Disclosure of Principal Adverse impacts (PAI) of investment decisions on sustainability factorsWebsite DisclosureArticle 4
Integration of sustainability risks into Remuneration PolicyWebsite DisclosureArticle 5

Product Level

Incorporation of sustainability risks into investment decision making and the impact of sustainability risks on fund returns. In this need to publish a PAI statement.Pre-Contractual
Article 6
Transparency of PAI at financial product levelPeriodic Contractual DisclosureArticle 7
Transparency of the promotion of environmental or social characteristics. Scope of Fund is Light Green fundPre-Contractual
Article 8
Disclosure ESG fund objective aligns with EU Taxonomy. Scope of the Fund is Dark Green fundsPre-Contractual
Article 9
All Marketing material align with SFDR & EU Taxonomy for eligible fund (ESG)Marketing contentArticle 13

Principal Adverse Impacts (PAI) Indicators:

As per SFDR’s stipulations, there are 18 core indicators that must be mandatorily disclosed, and further 46 additional indicators that need to be reported. These are further classified into Investee Companies, Sovereigns and Supranational, and Real Estate Assets.

The 18 Mandatory Indicators:

Magic FinServ – Going the Extra Mile to Validate and Verify SFDR Disclosures and Elevate Due Diligence Process

It is evident that a lot more data must be evaluated to extricate the genuineness of a fund with respect to Articles 6, 8, and 9. As an investor, you cannot rely solely on what data is available with Morningstar.

For a complete and thorough due diligence, to see if indeed the fund is sustainable, if it indeed has no major investment in a country that is blacklisted or associated with organizations involved or with exposure to controversial activities like weapons, one must dig deeper.

Magic FinServ’s DeepSightTM makes extraction of data from documents such as Form 13 F – a quarterly report filed by all institutional investment managers with at least $100 million in assets under management which discloses their equity holdings and provides insights into what the smart money is doing in the market, fund disclosure documents, fund strategies, investor documents, beneficial ownership that is a person/ group of individuals who enjoys benefits of ownership though the asset might be legally held by another person smoother and seamless and highly relevant. With DeepSightTM it is possible to parse through mountainous amounts of unstructured data and truly validate whether funds adhere to sustainability indices or are inadvertently Greenwashing.

Here’s how Magic FinServ Data and Technology Offering elevates the whole SFDR disclosure and ensures due diligence.


ESG Capability & Center of Excellence (CoE)

We have two different types of implemented Use case for SFDR workflow:

Fully Implemented of SFDR Ecosystem with Reg-Tech Partner:  For a Global Reg-Tech product pertaining to Pre-Contractual and Periodic Disclosure, Magic FinServ implemented the SFDR workflow as per the regulatory deadline of Jan 2023, while also facilitating their business operation and data management services. Our service covered the entire end-to-end workflow including Managed Services, Customer Onboarding/Implementation services, Data Analysis & Management, Support of Change Management services and on demand application development (if feasible using Cognitive Technology).

Data Extraction, Transformation & Integration (Support both Unstructured & Structure Data source)-For an Asset Management company we aided the customer in the extraction of relevant information related to Customer Static Data, Benchmark Index, Indicator Value, Measurement and Calculated value, etc., all of which are closely associated with ESG Data. The data was extracted from their financial and regulatory disclosure document in an unstructured format. Eventually, this data was consumed by the SFDR disclosure workflow

So, if you too would like us to demonstrate how we can add value to your ESG journey, reach out to us at mail@magicfinserv.com.

Now that we are entering 2023, and the 2022 roller coaster has officially ended, it is time to deal with the after-effects. Despite the pandemic, 2022 started off as a good year for fintechs and capital markets on the back of a robust 2021. They rallied due to government concessions, buoyant investor confidence, and customer preference for digital. But the party has now ended. It ended when the markets slumped, and some of the biggest tech firms lost valuation and investor confidence overnight. The everyday layoffs are also a brutal reminder of what lies ahead in 2023 as organizations are cutting expenses drastically. Now it is time to deal with that nagging hangover.

So, here is how you can get rid of the post-party hangover both in fintech and literal sense!

Say No to Binging!

According to the CDC, binge drinking is 5 or more drinks on a single occasion for men or 4 or more drinks on a single occasion for women, within about 2 hours. (Source: CDC)

Now that the party time is over, organizations will have to be mindful of the excesses, and ensure that they do not yield to these in 2023. Here are some of the excesses of 2022.

  • Incurring expenses in moonshot projects
  • Leaving data in disarray that have become messy data swamps
  • Investing in multiple applications and no process optimization
  • Excessive reliance on manual when automation can do just as fine

Take Stock! Keep an Open Mind

Now here is how to deal with the hangover if you have not been careful enough. Hangovers do not go away on its own, you have to deal with it. How can you do that? The most failsafe way to avoid hangover blues is to increase the consumption of vitamins and nutrients and stay hydrated in the days leading to the holiday season and pre and post the party. This will not get rid of the hangover, but the effect will be less severe.

When it comes to the fintechs and financial services, despite the prophecy of grim times, due to the many excesses we indulged in, in the last couple of years, Wall Street Colossus, Warren Buffet, believes that there is opportunity to make money because “Money will always flow toward opportunity, and there is an abundance of that in America.”

The fintech and financial services market is vast and varied and is unlikely to stop growing. Niche domains like payments, stock trading, alternative marketplaces, money transfer and remittances, mortgage, and lending, and robo-advisory, despite the looming recession, have multiple avenues for growth. But first you must take stock of your strengths and weaknesses:

  • What problem are you looking forward to solve?
  • Do you have the skill set and qualified management?
  • How big is the market opportunity?
  • What positive early traction has the company achieved? Are there early or pilot customers?
  • Does the business understand the key financials and metrics of their business?
  • What are the potential risks to the business, especially regulatory risk? (Source: Forbes)

Up the Stakes! Look at What the Competition is Doing

If you have problem with the alcohol, check the alternatives. There are some plant-based like the GABA Labs’ Sentia, an all-natural herbal blend that recreates the feeling of a drink without alcohol or hangovers. It is always better to be safe than sorry.

How does one look for opportunity or seize the day “carpe diem” even in these unprecedented times, particularly the fintechs that have fared worse than the rest in 2022? The answer is giving up on the excesses, reflecting on internal restructuring, and brainstorming about how to move ahead into 2023 with our greatest strengths – technology, people, and process, without going over budget. You must be willing to up the stakes and your competitiveness and so, look at what we believe to be the top trends of 2023, where a lot of innovation is already happening.

Top 2023 Trends!

AI for automation and innovation: Artificial Intelligence will be the allrounder responsible for spurring growth and innovation.

Robotic Process Automation: Another key player robotic process automation will level the playing field by synchronizing back and middle office with front office and ensuring streamlined and optimized processes.

Microservices: Microservices have already become the next big thing when it comes to testing.

Big Data and Data Quality Management: There will be more stress on cleaning data as we have elaborated upon in this blog whether it be for meeting regulatory requirements or ensuring sustainability in the supply chain with ESG (Environmental, Social and Governance), or onboarding, compliance, and due diligence.

ESG: As the COP27 summit has vowed to revive global efforts in combating climate change, sustainability and diversity, equity, and inclusion your Environment, Social, and Governance (ESG) scores will play a key role when it comes to attracting investment opportunities. As a fintech company with ESG mandate, you are uniquely poised for rapid and exponential growth.

When in Doubt, Outsource! Realizing the Benefits

Holiday guilt trip is not just about alcohol, it is about food as well, as it is common to overeat and gain weight in the holiday season, but a strong support system can keep all the “weighty” problems at bay.

For fintechs and financial institutions, third-party vendors and partners are akin to a support system that can keep rising costs at bay.

As fintechs and financial institutions cater to stakeholders with differing interests or agendas like the regulators, stakeholders, customers, etc., while pursuing growth, profits, and revenue generation, it makes sense to delegate tasks that are not a part of the revenue generation cycle to a third-party vendor.

Likewise, if you are considering launching a new product or managing operations while remaining risk-averse, there are many benefits of using the services of an experienced partner like Magic FinServ which has been listed below.

Source: (jeff bullas)

Increase Reliance on Technology

There are hangover products that advertise quick relief, but the experts are not sure. Meanwhile for fintechs and financial institutions, the scenario is quite the opposite as technology is the key to success.

Navigating a Tough Year with Process Optimization, Automation (RPA), and Data Centricity

Like that last glass of wine, some of your pet projects might just have to wait. A suitable time instead to retrospect on what went wrong and ensure process optimization and last mile integration. With process optimization, changes in a system, process, or product result in a manifold increase in operational efficiency and effectiveness.

Whether it is growing in valuation or aligning your ESG efforts with demands of the investors and the regulators, here’s how Magic FinServ and our Fintech Accelerator program can be your partner in need.

How Magic FinServ can Help?

As a fintech or a financial services operator, we can help you locate not just the pain points but the areas where slight improvements or optimization can result in exponential gains. Process optimization makes processes sounder by eliminating time and resource wastage, minimizing unnecessary costs, and the bottlenecks and mistakes that get in the way of organizational and process efficiency.

  1. Streamlining processes with our experience in leveraging the right infusion of Technology
  2. Streamlining application landscapes with our knowledge of Agile and Dev Ops
  3. Ensuring that workflows are more efficient
  4. Resolving the tech debt – using microservices and APIs
  5. Leveraging the power of Magic FinServ’s DeepSight TM to reduce manual effort by up to 70% and accomplish task with higher speed and lower error rate, thereby reducing cost.
  6. Automation: Another powerful tool when it comes to streamlining processes and reducing excessive human dependency which in turn makes time to market faster, eliminates errors, and drives efficiency is automation. As a fintech, you can drive a competitive edge with Magic FinServ’s powerful automation fabric for process optimization and last-mile integration. We help you identify where it can result in better gains – i.e., in highly data-intensive, cumbersome, manual-labor oriented and mission- critical activities that are core to fintech functioning.

For millennial businesses, data is that powerful tool that opens the door to many opportunities. Mitigate the data hangover (the disarray and chaos) and whether it is ESG (Environmental, Social & Governance), or onboarding, AML, due diligence, or any other activity, you can find in Magic FinServ’s DeepSightTM a steady partner and a sure cure to many of your organizational data woes.

Magic DeepSight is a one-stop solution for comprehensive data extraction, transformation, and delivery from a wide range of unstructured data sources leveraging cognitive technologies of artificial intelligence and machine learning for the buy-side.

The Perks of Growing in Valuation with Magic FinServ’s Fintech Accelerator Program

Magic FinServ’s Fintech Accelerator Program is designed to suit your purpose and needs. You can choose between three models depending on the level of engagement required.

  • Model 1 – In-house Fintech: For building from scratch: If your requirement is comprehensive including core product development, Magic Augmented (in-house Fintech) approach is tailored for your unique needs.
  • Model 2 – Magic Owned (Outsourced to Magic): Our talent and skill sets are engaged for your activities from our premises and the end-to-end ownership lies with Magic FinServ as well. With services such as a shared service desk with customers, automated QA, release and deployment to the cloud, and cloud infrastructure including build and QA for quicker releases and smoother management.
  • Model 3 – Magic Assisted (Co-Source): Where your requirement is integrated with Fintech Ecosystem – Dev, QA, Support Production, and Distribution of Content.

With Magic FinServ’s Accelerator Program, you get the best in terms of consultation, and you can profit from the experiences of our consultants to navigate the complex global digital landscape with our rich pool of experienced enterprise technologists executing tasks creatively and achieving zero latency for our clients.

We can help organizations identify areas for improvement within their organization, allowing them to optimize their processes and maximize efficiency with the right tools and approach.

You can also benefit from our partnerships with the best including DCAM (Data Management Capability Assessment Model) and Magic FinServ’s distinguished network of global senior executives and venture capitalists across the financial services technology industry.

In conclusion, the times might indeed be tough, but a recession does not last forever and a bit of restraint and restructuring, we might all still sail through. Or as Warren Buffet indicates if history is any indication of the future, the economy always recovers. For more on how we can be of help, write to us mail@magicfinserv.com.

Santa Claus and Mrs. Claus are in a quandary.

Up at the North Pole where they have been working overtime, it is crunch time for Mr. and Mrs. Claus and their team. They know children across the world are counting the days when Santa will climb down their chimney and secretly leave them their favorite toys in the secrecy of the night. Children have been good the whole year in anticipation of the gifts they will receive. But as Christmas nears, Santa and Missus are not sure if they will be able to fulfill every child’s request anymore. A glitch has been unearthed.

Thanks to the five Naughty Elves, quality levels and deadlines have not been met. With the quality checkers and regulators breathing down their necks, Santa is worried that there would be more toys in the Island of Misfit Toys and not enough for his reindeer sleighs that would be racing overtime on the eve of Christmas.

Exposing Santa’s Naughty Elves

Bad Boy Billy and Bad Data Throwing a Spanner during Xmas Merriness

Bad Boy Billy: Bad Boy Billy, the naughtiest elf in Elfland has been up to no good. He is most likely to be shelved by Santa. Due to his carelessness, the toy-making factory missed several deadlines and Santa is likely to face a bad rap as children across the world question his credibility when they do not get their favorite toy. There is no one who can make Bad Boy Billy realize that when you use poor-quality raw materials you will only get low-grade toys. No matter, the best 3D technologies are at your disposal.

Similarly, for Financial Institutions and Fintechs, Poor Data Quality is the Bad Boy Billy. Too often, they have not bothered to improve their data quality which is bad for their businesses.

Source: @DiegoKuonen 

Here’s what counts as poor-quality data

  • Data that is entered wrongly at the entry-level primarily due to oversight and overwork
  • Incomplete data or data missing certain details in forms and invoices
  • Data coming from websites and other third-party sources can also be fallacious
  • Poorly labeled or unlabeled data – data that is lacking in metadata
  • Data that is unstructured and difficult to organize – requires digitization
  • Data inconsistency and disjointedness due to its existence in multiple silos or emanating from different channels and sources.

If you retain bad-quality data, you cannot create Magic. So as an organization if you are not doing something about your bad data, you will make poor decisions, business inefficiencies will continue no matter the cutting-edge technologies incorporated and implemented, you will miss out on opportunities, and ultimately you will not meet the expected revenue targets. As today’s business demands last-mile process automation, integrated processes, and a cleaner data fabric that democratizes data access and use across a broad spectrum of processes, good-data quality is imperative that you can ill afford to ignore.

Another Naughty Elf, Trixie Trouble, makes Santa Less Jolly this Christmas by not following Process Automation

While 3D is a major disrupting trend in toy making, Trixie Trouble with her penchant for carelessness has gone ahead and made Santa less jolly by not enabling 3D – the process automation of toy making.

Many organizations resort to automation without realizing that without reaching the critical mass of process automation, they cannot achieve the desired results. Today, process automation has become quintessentially associated with Banks, FinTechs, Hedge Funds, Asset Managers, Brokers, and others dealing with a massive and steady flow of financial, business, and operational data that must be parsed on a day-to-day basis.

Here are some of the common mistakes that organizations make when it comes to automation and which must be rectified at the earliest for desired results:

Financial Institutions and FinTech that have not optimized their critical processes, most of which exist in silos, are like Father Christmas less likely to be jolly this Christmas as well because they have squandered away the opportunities.

If trying to become a Duplicate Santa to steal gifts was not enough, Grinchy Grinch Face tried stealing the Christmas Spirit as Well

Grinchy Grinch Face tried stealing Santa’s identity. He tried to become a duplicate Santa and steal the gifts and the spirit of Christmas, but he failed.

Duplicate Data is any data shared with other data in the database. Apart from increasing storage costs, duplicate data results in missed opportunities and comes in the way of the delivery of personalized content to consumers.

Not just duplicate data, fintech or financial institution you must be able to leverage the power of unique customized solutions, and not cut corners with a one-size fits all solution (which unfortunately is a common experience). However, Magic FinServ’s advisory and fintech accelerator program facilitates customized solutions tailored to varied business and organizational needs.

Bah Humbug nixes Christmas once again because of incorrect deliveries

True to his ways, Bah Humbug has once nixed the Christmas spirit. Santa is worried that the delivery of the wrong toys would result in unhappiness and spoil his name. All because Humbug did not cross-check which gifts had to be delivered to whom. Same in case of FIs, they should be thorough with the testing – whether of the APIs, the infra and the environment. When you do not test APIs, the environment and the infrastructure for efficiency and performance, the costs rack up higher, the user experience is poorer, and you get a rejection or an extremely disappointed child by delivering wrong gift to them

Nuisance Nelly fails to follow DevOps practices in its right spirit, and angers Santa.

Nelly has failed to avail the benefits of cloud and platform engineering and so scaling up the production of toys will take time – resulting in unmet demands.

Santa is angry at her because she has the habit of saying Done when she is not actually ready. In DevOps and Project Management Methodology, being ‘Ready’ to start a project/sprint is vital as indicating you are ‘Done’.

You need to test your requirements and validate that not only can you write test cases against each requirement but also ensure that the definitions are clear and without ambiguity for good code, and a defect-free production environment. Get it wrong and you will face large costs due to rework and possible system outages and damage to your company’s reputation and be burdened by tech debt.

Also, when it comes to installing a production ready environment for quicker and more efficient work, cloud-native approach and platform engineering facilitates the capacity to easily grow horizontally, which in the case of FinTechs and financial organizations might even lead to more revenue generation.

Don’t Worry! Good Elves still hold up the Christmas Spirit…

As Santa frets, few good elves hold up the Christmas spirit. Like the three wise men who bore with them gifts for baby Jesus, the good elves have been working overtime to ensure that there is a smile on every child’s face.

Some of the good Elves who have been working overtime are:

Holly: This good elf is responsible for last mile process automation and RPA (Robotic Process Automation) to ensure that customer experience, in this case, millions of children are blessed with benefits realization (the toy they desire for being good).

Happy: This elf works closely with Holly to enable the building of frictionless CI/CD pipelines. At Magic FinServ too we have a proprietary automation fabric framework to build frictionless CI/CD and automated testing pipelines, using custom or open-source tools; thus, enhancing the time-to-market and a happier client experience.

Merry: Merry is at the helm has the responsibility to ensure things (fall in line) without fail – a proactive SLA-driven support / maintenance of applications, environments, and infrastructure (cloud) to ensure scalability, stability, and availability. She is also making it easier for Holly and Happy by cleaning the back and middle office operations by automating the acquisition, consumption, and distribution of data.

The happy elves will not allow duplicate data to get in the way of Christmas Cheer. Here is how they find and merge duplicates using an AI and ML powered tool (DeepSight TM ) and applying data governance best practices when it comes to fulfilling their requests on a worldwide scale without glitches.

The good elves know that collaboration in the right spirit can get the work done – the toys made, packaged, and delivered to their rightful owners, with ease impossible otherwise – hence they follow the DevOps approach with testing involved right from the outset along with other best practices for Product Backlog refinement and sprint planning sessions, and much more.

In a Nutshell: Long Story in Short

The above tale is of course a work of friction. But we guarantee that should Santa ever be in dire need of automation support for scaling up his toy-making factory, we would be delighted to help, just as we would be glad to be of help to any financial organization or fintech planning to scale their digital transformation initiatives with automation, artificial intelligence, cloud, and robust DevOps practices without costs spiraling out of control in these troubled times. For more on how our good elves – our experts in finance and technology, and our Fintech Accelerator program can help reach desired potential reach out to us at mail@magicfinserv.com.

We all at Magic FinServ wish you and your family Merry Christmas and a very Happy New Year! Cheers!

“The second vice is lying, and the first is running in debt.” Benjamin Franklin, The Way to Wealth

While no organization wants to run into debts, lack of foresight, poor coding practices, and equally poor-quality code, patchwork, existing legacy architecture and increasing pressure to remain competitive means that unerringly they end up in the black hole of tech debt.

According to American programmer Ward Cunningham, “Accumulating technical debt is similar to the accumulation of monetary debt. It simply escalates if you do not act in time.”

What Causes Tech Debt?

Even today, many organizations are still relying on applications that were written several decades ago. There is no question that these have become obsolete. The application’s lifecycle has run its course and both from a technology perspective as well as from a business perspective where the organization must assimilate dynamically fluctuating business needs, the application’s utility, or its ability to match pace with changing needs has eroded. However, as the cost of migrating to the tech stack is extremely prohibitive, and because there is a chronic shortage of skills required for implementing that migration, organizations have stayed manifested with redundant tech stacks. To make matters worse, employees who had knowledge about the intricacies of the platform are either leaving or retiring and so over a period of time organizations have fewer and fewer people to understand what is was going on. They were also operating on very limited information.

How does Tech Debt Build Up?

Two industry examples to illustrate how tech debt builds up to the point of crashing application or making any new feature or value addition prohibitive

  1. A large commercial bank involved in payments had hardcoded some counters which are affected by the volume of payments transactions going through. Typically, during times of uncertainty or upheaval such as an election where the incumbent could get defeated or let’s suppose a scenario such as the one that happened in Great Britain where a sudden shock announcement of a mini-budget sent the economy into a tailspin and the currency crashing, resulting in a lot of payment transfers, a tech debt build-up due to some variables hardcoded in the system, could cause the system to crash as it is unable to handle the increased workload. In the process, people will not be able to transfer money to meet the liquidity or safety requirements.
  2. While some technical debt is manageable, as perfection even in the technology realm is near impossible, the accumulated weight of maintenance costs when things do not work as planned can be a constraint as observed in the case of a prominent insurance company. The company was utilizing an extremely old and archaic policy administration platform for processing. Problems cropped up every time the insurer came up with a new insurance product or feature addition primarily because the product was managed on the archaic administrative platform. As the platform was saddled with tons of tech debt, extensive changes had to be made every time a new product or new features were added making it an unsustainable exercise.

Tech Debt more than a Manifestation of an Engineering Problem – A Pictorial Depiction

Primarily a manifestation of an engineering problem, but there’s a cultural angle to it as well as multiple teams and stakeholders are involved often holding diametrically opposite views in the decision-making process. Technical debt arises when a project takes much longer than expected to ship. That is to say, it metamorphizes from a sound-looking plan on paper to a source of never-ending expenditure (a black hole of spend), with project delivery nowhere in sight.

So, here’s a question for you. Can you spot how one can end up accumulating technical debt taking cues from the illustration (courtesy: vincentdnl) below?

Clearly, there is a dissonance between what is expected and the end result.

  • The leaky roof cannot be just patched up. That’s an example of poor engineering strategy. A band-aid or patchwork approach is only useful in the initial stages, when projects scale, it is simply not enough.
  • Someone has clearly not paid attention to the foundation. Alignment problems exist. (In software it translates into code defects) In software it translates into code defects. Not enough attention is paid to the testing of code, and inconsistencies, bugs, and legacy code defects have spiraled into a humongous never-ending problem.
  • There are pillars but clearly not functional.
  • There is an obvious lack of responsibility and poor technical stewardship. The pressure of early release has resulted in quick fix testing.
  • The team has been using a band-aid or patchwork approach to getting the work done.
  • We also think that the definition of “done” is clearly not understood or ambiguous. Standards evidently do not exist.
  • End result: Delayed timelines, Spiraling costs, technical debt Black Hole

Reasons for Tech Debt

  1. Technical debt is generally thought of as an application code problem. Sometimes either deliberately or inadvertently, some gaps are either introduced or allowed to remain as is as it is more important to release a product than make it picture perfect. If the underlying code issues are not addressed in time, teams could end up confronting a “code smell” which is a signal that things would have to be worked all over again.
  2. Documentation debt: This is yet another significant yet underestimated cause of technical debt. This occurs when teams create code but forget to pay attention to supporting internal documentation, such as code comments that explain how the program works and what was the intent behind it. Also, code comments should be in a form that dispels confusion instead of creating new ones. There are long-term repercussions and costs arising due to poor documentation. Each new member would have to start all over again in the absence of proper documentation.
  3. With new age born-in-the-cloud institutions like Monzo, Starling, Robinhood, etc., giving stiff competition to the bigger players there is increasing pressure to revamp the crumbling legacy architecture and give it a new look by hosting their legacy applications in the cloud. However, if you simply rehost the application on the cloud as-is without considering other factors that might affect its performance and functionality you might end up with elevated costs and significant operational issues.

Our Magic FinServ’s Accelerator to the Rescue

We understand the complexities of the Fintech and the Capital Markets business and have a thorough understanding of the current scenario. We go through source code and whatever limited documentation is available to understand what is happening and the gaps that exist therein.

Our strength happens to be our very strong QA automation practice. And of the key aspects of QA automation practice is to follow the functional feature from start to end.

  • Adopting an Agile Approach to and following a continuous cycle through design, coding, testing, and analysis.
  • Catching the bugs early with automated regression tests whenever the code is altered
  • Introducing test automation incrementally through the development cycle.
  • Having room for quick ramp-ups

We can use all these techniques to understand what is happening.

We can take care of documentation debt and once it has been identified and is under resolution, we then take care of tech debt.

We also take care of technical debt, for instance by building code on microservices. Microservices are ideal for developing, testing, deploying, and updating services independently, resulting in faster deployment.

Replacing front-end UI/UX modules with better UI/UX modules using microservices and connecting it to the backend platform using APIs.

Gartner has defined three application categories, or “layers,” to distinguish application types and help organizations develop more-appropriate strategies for each: Systems of Record, Systems of Differentiation, and Systems of Innovation. This is ideal for applying what to retain or which is most valuable in the existing application, and simultaneously carving out and replacing things that add to the tech debt by replacing it with microservices and connecting to APIs.


Technical debt is more than house cleaning. It causes damage in more ways than one. It demotivates teams, creates chaos, and most importantly slows down the modernization process. The best way to deal with it is to quickly underwrite a strategy to cut technical debt costs before a stink is raised. For more on how we can help, write to us at mail@magicfinserv.com.

Money is changing form! In the UK, it has almost been a month since paper £20 and £50 banknotes have ceased to be legal tender – in its place, there will be polymer bills featuring J.M.W. Turner and Alan Turing. An unprecedented move by the UK government since for ages banknotes have been typically paper, the new ruling serves a dual purpose – enforces the use of plastic instead of paper, and also promotes digital currency.

Money is usually thought of as sovereign currencies, physical like banknotes and coins. But, increasingly electronic money (e-money), digital financial services, virtual currencies, and mobile wallets have taken the space of physical money. The conversion of money from paper to bits and bytes has been a gradual process aided by the growing popularity of digital financial services and the emergence of innovative technologies like Artificial Intelligence.

When it comes to the Financial Services – Banking, Insurance, and Investment, the ecosystem is flooded with paper. A similar disruptive step is unthinkable considering that the reliance on paper for financial services has only grown.

  • Estimates point out that a financial service could be used anywhere between 60 boxes of paper every hour to keep track of its clients’ finances. Paper is used for printing statements, invoices, and other documents
  • As businesses increasingly move to the cloud, and data becomes all pervasive and available, data coming in diverse types of offline unstructured forms must also be incorporated.
  • The solution obviously then is to recognize the fact that you have to live with the data coming in a very unstructured offline manner, and yet find ways to prevent the flood of manual labor it would cause by using a tool like Magic FinServ’s DeepSightTM .

Unstructured Data has Enormous Potential – The Challenge is How to Tap it

Data is of two types – structured and unstructured.

Structured Data: Structured data is online data, such as the information and databases available on public and private websites. (For most of the software applications in use today, such as Spotify, these databases are the core working at the backend. The databases in use today for structure data have scaled from DB2 and Oracle which were single machine databases, to clustered databases and distributed scale out databases like Snowflake and RedShift.)

Unstructured Data: While unstructured data is the data that is available offline like pdfs, spreadsheets, email attachments and more. There is a stockpile of it – experts estimating some 2.5 quintillion bytes of data being generated each day – unstructured and structured. The biggest challenge is how to use the data in the best possible manner. The pandemic has proved without doubt that paper is cumbersome. It is not easily accessible when required; can be easily damaged, takes enormous storage space, and making edits to it is difficult.

The data existing in our emails, pdf documents, social media posts, live chats transcripts, text files running in pages, word documents, financial reports, webpage content, and not to forget the IOT (Internet of Things) sensor data from our smartphones and watches, and satellite imagery, and is best managed in non-relational NoSQL databases or data lakes where it is kept in its native form. The challenge with this data is that it is unrefined. We cannot derive insights from this kind of data. It lacks metadata.

It would be pointless for banks and financial institutions to wait for months (or years) to plough through this information. By that time, they would have lost the competitive advantage of a new product launch or a new incentive to provide personalized content to customers. Hence the need for unstructured data processing solutions such as automation and intelligent document processing (IDP).

Unstructured data processing (UDP) technologies are not new. Some of the UDP technologies such as ICR date back to the 1990s and have been used to minimize the reliance on paper while speeding things up. Others, such as Deep Learning and Machine Learning have enormous potential but in the absence of trained data are constrained when it comes to ensuring desired levels of accuracy. Nevertheless, we have identified here a few UDP technologies of that solo or in combination with others are being used by bankers, FIs, and buy-side firms for deriving insights from unstructured data in Loans Processing, KYC (Know Your Customer), Accounts Payable, AML (Anti Money Laundering), Digital Asset Management, and IT (Information Technology) help desk.

The financial services sector has been making changes in the direction of reducing paper use. As a result, breakthrough technologies powered by AI, ML, NLP, and IOCR – infinitely improved versions of the machines used by Alan Turing to break the Enigma code – are slowly taking over. These are no longer standalone systems like the WWII Bombe machine but smarter apps that work remotely on your laptops and the cloud and process or ingest unimaginable quantities of data. We only have to look at something as every day as the paperless billing system to realize how it has cut down the use of paper and increased customer-centricity by giving them the comfort of making payments from home.

Integrating Technology for Biggest Gains

1)Intelligent Character Recognition (ICR): ICR relies on OCR (Optical Character Recognition) and pattern recognition to automate the extraction of data in machine-readable format from documents using pattern recognition. It can also be used for capturing sensitive information for loan processing, mortgage, pay slips, etc. With quicker access, decision-making and forecasting will be easier.

2)Optical Character Recognition: The basic difference between OCR and ICR is that while OCR extract data in text form, ICR extracts data in machine readable form. OCR makes it possible to identify and input relevant data. For example, an OCR will scan a cheque thoroughly and identify the different sections such as the serial code, IFSC (International Financial Services Centre) Code, amount, signature, much quicker than the front desk executive.

3)Deep Learning: The level of automation that can be incorporated with Deep learning-based solution is inordinately high. Deep Learning algorithms can be used for improving the customer experience and for predicting customer churn – both of which are vital for promoting growth.

4)Real-time Stock Prediction and Algorithmic Trading: The unblinking and unbiased eye of AI can be used for integrating news about stock from news and social media and coupling it with historical data and current price movements to predict stock values more accurately.

Yet another area where deep learning and machine learning algorithms have immense potential is checking fraud and insurance underwriting. Using historical data (health records, income, loan repayment, as well as smartphone and wearable information) to train the algorithms, insurance companies can set suitable premium and access the risks

5)Computer Vision: With computer vision, banks and FIs can visualize and analyze images, pdfs, invoices, videos, etc. This is enormously handy for KYC, onboarding, loan origination tasks as most are paper-heavy and prone to errors and duplication of efforts if done manually. With computer vision aided technology, banks and financial institutions can easily scan, store, tag or classify, and extract relevant information from documentation. Automating classification and extraction of relevant data elements introduces process efficiency and higher levels of accuracy. By leveraging computer vision and OCR technologies, banks and FIs can ensure higher levels of accuracy than plain OCR where rules and templates must be adjusted for each variation.

6)Natural Language Processing: In IT, NLP can help in remediating help desk tickets using pattern recognition. Another area where NLP is being used is virtual assistants and chatbots. Named Entry Recognition (NER), machine learning, natural language processing (NLP) service that helps create structure from unstructured textual documents by finding and extracting entities within the document. When it comes to loans processing, FIs use NER to tag and classify relevant data to extract information to accelerate the process of assessing profitability and credit risk.

Automation and DeepSightTM

The thing is that you cannot ignore unstructured data anymore. And this is where the real challenge arises, because most of the AI and ML-powered tools for data extraction are still built to deal with structured data only.

But for machine learning and training of unstructured data there are many limitations – For example just to stat a file which gives information about the file and filesystem, like the size of the file, access permissions and the user ID and group ID, birth time access time of the file would take a few minutes and if there were many unwieldy files in the data lake that would take ages to gain an understanding of what is there in the data lake.

While there are vendors promising exceptional results, Magic FinServ’s DeepSightTM advantage comes from its being purpose-built for the financial domain. DeepSight’sTM sophisticated training tool addresses the specific needs of banks, FIs, and buy-side firms. Coupling UDP technologies the ones that we have mentioned earlier – computer vision, NLP, machine learning, neural networks, and optical character recognition for greater benefits and reducing time, money, and effort for processing unstructured data from transaction emails, invoices, pdfs, and KYCs, contracts, and compliance documents to derive insights with minimum inputs.

To conclude, paper is not going to go away soon, but we can certainly take steps to ensure minimize use and ensure more efficiency by digitizing data and finding ways to deal with the mountainous amounts of it. After all, that goes a long way to building a sustainable world, while also ensuring ease and transparency in operations.

If you are interested in learning more or have a specialized use case where we can pitch in, reach out to us at mail@magicfinserv.com.

In the good old days, an organization’s ability to close its books in time at the end of the financial year was a test of its data maturity. The mere presence of a standard accounting platform was not sufficient to close books in time. As CFOs struggled to reduce the time to close from months to weeks and finally days, they realized the importance of clean, consolidated data that was managed and handled by a robust Data Execution framework. This lengthy, tiresome and complex task was essentially an exercise of data consolidation – the “closing of the records” or setting the records straight. Data as per the Oxford Dictionary of Accounting is quite simply a “procedure for confirming the reliability of a company’s accounting records by regularly comparing (balances of transactions).”

From the business and financial perspective, the closing of records was critical for understanding how the company was faring in real-time. Therefore, data had to be accurate and consolidated. While CFOs were busy claiming victory, the Financial Institutions continued to struggle with areas such as Client Reporting, Fund Accounting, Reg Reporting and the latest frontier, ESG Reporting. This is another reason why organizations must be extremely careful while carrying out data consolidation. The regulators are not just looking more closely into your records. They are increasingly turning vigilant and digging into the details and questioning omissions and errors. And most importantly, they are asking for an ability to access and extract data themselves, rather than wait for lengthy reports.

However, if there are multiple repositories where you have stored data, with no easy way to figure out what that data means – no standardization and no means to improve the workflows where the transactions are recorded, and no established risk policy – how will you effectively manage data consolidation (a daily, monthly, or annual exercise) let alone ensure transparency and visibility.

In this blog, we will argue the importance of data governance and data control environment for facilitating the data consolidation process.

Data governance and the DCAM framework

By 2025, 80% of data and analytics governance initiatives focused on business outcomes, rather than data standards, will be considered essential business capabilities.

Through 2025, 80% of organizations seeking to scale digital business will fail because they do not take a modern approach to data and analytics governance. (Source: Gartner)

In some of our earlier blogs, we have emphasized the importance of data governance, data quality, and data management for overall organizational efficiency. Though these terms sound similar, they are not quite the same.

As per the DCAM framework – a reliable tool for assessment and benchmarking of an organization’s data management capabilities, Data Management, Data Quality, and Data Governance are distinctly separate components. While Data Management Program and Funding forms the core – the foundation; Data Quality Management and Data Governance are the execution components with Data Control Environment as a common thread running between the other core execution elements. (See: DCAM framework)

For high levels of data maturity, something that is highly sought by financial institutions and banks, democratization and harmonization or consolidation of the data elements are necessary. This quite simply means that there must be one single data element that is appropriately categorized/classified and tagged, instead of the same existing in several different silos. Currently, the state of data in a majority of banks and financial institutions is such that it inspires little trust from key stakeholders and leading executives. When surveyed, not many asserted confidences in the state of their organization’s data.

For ensuring high levels of trust and reliability, robust data governance practices must be observed.

DCAM Framework

Getting started with Data Control

Decoding data governance, data quality, and data control

So, let’s begin with the basics and by decoding the three…

Data Governance: According to the DCAM framework – the Data Governance (DG) component is a set of capabilities to codify the structure, lines of authority, roles & responsibilities, escalation protocol, policy & standards, compliance, and routines to execute processes across the data control environment.

Data Quality: Data Quality refers to the fitment of data for its intended purpose. When it comes to Data Quality and Data Governance, there’s always the question of what came first – data quality or data governance. We’ll go with data governance. But before that, we would need a controlled environment.

A robust data control environment is critical for measuring up to the defined standards of data governance, and for ensuring trust and confidence amongst all the stakeholders involved that the data they are using for fueling their business processes and for decision making is of the highest quality. Also, there is no duplication of data, the data is complete, error-free and verified, and accessible to the appropriate stakeholder.

For a robust data control environment:

  • Organizations must ensure that there is no ambiguity when it comes to defining key data elements.
  • Data is precisely defined. It must have a meaning – described with metadata (business, operations, descriptive, administrative, technical) to ensure that there is no ambiguity organization-wide.
  • Secondly, data, whether it is of clients, legal entities, transactions, etc., must be real in the strictest sense of the term. It must also be complete – definable, for example AAA does not represent a name.
  • Lastly, data must be well-managed across the lifecycle as changes/upgrades are incorporated. This is necessary as consolidation is a daily, monthly, or annual exercise and hence the incorporation of the changes or improvements in the workflows is necessary for real-time updates.

But what if a data control environment is lacking? Here are the multiple challenges that the organization will face during data consolidation:

  • As there are multiple departments with their own systems, there are multiple spreadsheets as well.
  • Due to the inconsistencies and inability to update workflows – operational and financial data might differ.
  • Mapping and cross-referencing of data will be tedious as the data exists in silos.
  • If there are inaccuracies that must be sorted, they will be reflected in standalone worksheets…no single source of truth will prevail.
  • Quite likely that ambiguities will still exist even after the consilidation exercise is over.
  • Meeting compliance and regulatory requirements would require expending manpower again as there is little to no transparency and visibility.
  • Now compare this with what happens when you rely on robust governance and data control environment practices.

    • The focus will not be as much on the process as on ensuring high levels of data quality and elimination of waste.
    • Data nomenclature: data defined against predefined requirements, so it is easier to extract relevant data.
    • With automation and standardization, data owners and consumers get the benefit of targeted information – Variances are recorded and made available to the right people.
    • Information is shared/accessible to everyone who needs to know. Does not exist in silos anymore.
    • Auditing becomes easy as there is visibility and transparency.
    • With consolidation expediated, speedier decision-making ensues

    In short, with a robust data control environment and data governance practices, banks and FIs, can minimize consolidation efforts, time, and manpower, resulting in enhanced business opportunities and a greater degree of trust in the data amongst stakeholders.

    Staying in control

    Magic FinServ is a DCAM EDMC partner, its areas of specialization being the ability to manage offline and online data sources, the understanding of the business rules in financial services organizations and the ability to leverage APIs RPAs, allowing data to be moved across siloed application and business units, overcoming other gaps that could have led to data issues. Magic FinServ can bring in some of these techniques and ensure data control and data governance.

    The DCAM framework is both an assessment tool and an industry benchmark. Whether it is the identification of gaps in data management practices or ensuring data readiness for minimizing data consolidation efforts, as an EDMC’s DCAM Authorized Partner (DAP) for providing a standardized process of analyzing and assessing your Data Architecture and overall Data Management Program, we’ll aid you in getting control of data with a prioritized roadmap in alignment with the DCAM framework.

    Further, when it comes to data – automation cannot be far behind. For smooth and consistent data consolidation that generates greater control over your processes while ensuring the reliability of the numbers, you can depend on Magic FinServ’s DeepSightTM . For more information on the same contact us today at mail@magicfinserv.com

In the competitive world that we are living in today, organizations set high expectations to deliver more in quality assurance despite a decline in budgets and shorter timelines and due to this, many firms struggle to manage the cost of innovation and business demands in parallel.

Quality assurance is a critical area where neither speed nor quality of an expected behavior can be compromised as this leads to adverse business impact. Furthermore, most software tests appear to reside in an isolated manner making integration, collaboration and automation challenging. Thus, companies need innovative testing, solutions, and strategies to balance Speed, Cost, and quality.

This blog explains some cost saving business strategies that enable quality assurance/testing companies to improve ability within their teams while assuring high-quality output with less testing.

Testing Strategies and Proposed solutions – Entrust cross functional teams

What is a Cross-Functional Team?

Cross-functional teams are groups consisting of people from different functional/technical areas of the company – for example, PM, BA, DEV, QA, DBAs, RELEASE Engineers, etc. They can be working groups, where each member belongs to their functional team as well as the cross-functional team, or they can be the primary structure of your organization.

When teams of varied talent ally together, innovation, collaboration, and learning magnify. Cross-functional synergy helps advance relationships amongst teams who otherwise would have never crossed paths, creating a collaborative culture that benefits all levels of an organization and work towards a common goal.

Cross-functional Teams – Benefits

  • Heightened innovation in process and product
    • When companies operate in isolation, it becomes very painful to identify and implement improvements across the value stream. Cross-functional teams can work to identify best practices for different processes, then cross-train other cross-functional groups to promote coherence and competence across the organization. Working together to find solutions for common problems, cross-functional teams can find more innovative, more comprehensive solutions than each functional group could develop on its own.
  • Curtain cycle times
    • Cross-functional teams help companies identify their inefficiencies, while improving their ability to find solutions that work. In this way, using cross functional teams can knock off cycle times for any deep-rooted painful area.
  • Client first
    • Cross functional teams help organizations put their client first, by inspiring effective communication across teams.
  • Gain a superior wisdom
    • Towards delivering a draught of creative ideas, cross-functional association is a viable choice. Creativity is a group process. When the leaders, like Project Manager (PM), put together people who are experts in different subjects, each with niche and unique skills sets, it will bring out some new viewpoint. This method of collaboration will bring new insights to the team to bring up creative solutions and enhance development. With each team member bringing their skills and knowledge to the table, the work will progress and thrive, bringing solutions very fast.

Smart Automation – Automating the right set of tests

  • Delivering QA services at the right time has become critical for businesses. Rapidly changing global business environments need special focus for the testing teams to provide testing at speed with minimal cost. Therefore, automating the right set of tests, particularly business scenarios and frequently used workflows by users will enhance quality with less cost.
  • QA teams should focus more on integrating various components that may change continuously and need to be regressed frequently.
  • Have a robust framework to curtail business risk. We have seen that the cost to fix defects discovered in beta version or in production can be many times the cost to fix them earlier. Failures in production can leave users sitting idle resulting in lost productivity, or they may result in lost revenue, lost customers, and compliance liabilities.

Use End-User’s Mindset while Testing

The major value of a QA is to test the applications to improve customer experience and provide quality. Also, the assurance process tends to verify if all the rules and regulations are met.

But the major question here comes to all the QA organizations, does the QA process truly pay for its duty? We all must think about bringing additional business values beyond our regular testing.

We all know that the business users are the only ones who will be able to define quality, as they are the only ones who know about the expectation and need. And sometimes even business users have a tough time knowing what it is that they want or need.

So, QAs must evaluate products by undertaking real customer journeys across the system and test frequently used customer workflows in shorter testing window. By impersonating real user scenarios such testers identify a higher number of critical production bugs.

So as a QA, get involved in customer-facing activities: calls, visits, and support interactions. Take notes. Try to take part in these meetings.


More focus on quality and less on testing does not mean that testing will not be done or work in testing is going to disappear. What it does mean is that the focus will be changed from finding bugs to enabling the achievement of quality products while supporting the company’s goals and values.

Today we are in the midst of an app economy aided by the rise of open banking and embedded finance, and with shifting consumer choices – there are many applications that are revolutionizing the banking and the financial and capital markets ecosystem but ensuring more customer centricity.

But despite the buzz, many fail to live up to the expectations. This leads to the question – why do unforeseen complications crop up even in a perfectly good high-quality app?

Considering that speed and accuracy are the holy grail of software testing, software developers and QA (quality analysts) leave no stone unturned to validate results and ensure that the performance of the application is top-of-class, the application is free of bugs, and the safety and security of the software is not compromised.

But if they are not testing the networks, hardware, and communicating interfaces – the APIs (Application Programming Interfaces) or the environment thoroughly, they leave behind inadmissible gaps that could have unforeseen consequences in the way the app functions. With the cloud gaining prominence, one needs to be more vigilant when it comes to testing.

Environmental Testing – Key Elements and Dependencies

Unlike earlier, the complexity of an application has increased manifold times. There are multiple dependencies that must be considered, especially with the cloud in the picture. While earlier, software testing could be carried out in isolation, that is simply not enough today because how the application functions in real-time cannot be assuaged by looking at the deployment environment and its components and carrying out tests in isolation. We need to take the entire environment into consideration. So, the following dependencies/components must be tested as comprehensively and thoroughly for ensuring smooth functioning, reliability, and compatibility.

  • Operating system: Windows, Linux, Android, etc.
  • Database or data management and storage: As database instability can have far- reaching consequences, organizations must be thorough with the testing. Today, organizations are predominantly using cloud databases along with Oracle, IBM DB2, SQL Server, and MySQL for the same purpose.
  • Hardware dependency is another critical component that must be tested.
  • The APIs (Application programming interfaces) and networking interfaces, and end-user computing – or the user experience.

Some Common Use Cases of Environmental Testing

Some of the common use cases for environmental testing are as follows:

  • Implementing new tools or memory upgrades for servers,
  • Testing new patches or updates in the system
  • Security fixes and software updates

Overall, comprehensive testing of the environment would be required in the following instances.

Test Environment Infrastructure: Test environment is one extremely crucial aspect of Environmental testing which must not be overlooked at any cost as it plays a vital role in ensuring quick go-to-market.

  • Planning tools: IT (Information Technology) development teams set up the test environment for regular and quick releases, they also finalize test tools for testing planning, design, and execution, and also for monitoring, eliminating, and reporting the bugs.
  • Documentation: Testing documentation is also a good best practice for keeping everyone in the same loop and for better understanding of what the team is trying to achieve.

Server/Client Infrastructure: testing the functionality of servers – virtual, proxy, mail, file, web whether in-prem or cloud – and the client’s Environmental performance.

Network Testing: Managing resource usage, server downtime, appropriateness of system configuration, Operating system patches.

Installation and Uninstallation Testing: ensure no issues during installation, uninstallation, and deployment.

Data Migration Testing: Data migration testing is the testing of data when it has been migrated from the old system to a new system, say from on-prem to the cloud with minimal disruption or downtime, while guaranteeing data integrity and no loss of data. It also means that all the specified functional and non-functional features of the application function as-is post- migration. Pre- and post-migration testing are essential constituents of data migration testing as well as rollback testing and backward compatibility testing.

Infrastructure Testing in Cloud: When moving to the cloud for optimization of effort, time and resources, it is absolutely necessary to ensure that no loose ends remain.

Challenges of Environmental Testing

A rapidly evolving IT landscape, changing firmware, OS, browser, etc., are the biggest pain points in infra testing.

  • Installer packages for building the application
  • Additional libraries and the build packages
  • Time taken in installing and uninstalling the application
  • Checking for space in disk testing
  • Finding out if all files are deleted or removed after the application has been uninstalled
  • Lack of standardization when it comes to defining Environmental testing
  • Manual infra testing is mundane and repetitive and error prone
  • Results in low level of code as it is not always possible to scale according to the market
  • Results in poor user experience, and does not subscribe to the principles of AGILE and DevSecOps
  • Failure to investigate the test environment issues and follow-up
  • Maintaining a storehouse of test environments and the versions in one place poses a concern in the absence of proper documentation.
  • With test environments and teams remotely located, it is difficult to have a clear picture of the difficulties that arise w.r.t the various dependencies
  • Siloed work culture which results in code testing at the end of lifecycle.

What will you achieve if you do Environmental Testing?

  • Environmental testing prevents bugs and issues from slipping through which can later escalate into matters beyond control.
  • Makes defect identification better before production execution. Enhances the quality of infrastructure by ensuring zero defect slippage to production.
  • Minimizes the risks of production failures and ensuing downtime and poor customer experience.
  • Infra testing confirms that the dependencies are sound, and the app is functioning as expected in a systematic and controlled manner.

Environmental testing on the whole is a niche domain requiring multiple levels of testing.

Magic FinServ Client Success Story

For one of our clients, a leading investment solutions provider, which was facing an all-too- familiar problem – lack of documentation or zero documentation, we improved documentation by defining goals with minimum disruption and downtime. There were other challenges as well such as frequent changes in the application, but these were managed successfully as well. From setting up agile processes from scratch and R&D on tool selection and carrying out testing from day 1 as there were frequent changes in the application and integrating it (the application) with CI/CD for a noticeably faster go-to-market. The tools that we used included Java, Selenium WebDriver, TestNG, and Maven.

By considering the operational environment first, and leveraging an environment-based approach to testing, software testers can make sure that everything is in place to make testing productive and efficient. Rather than spending time trying to test applications in isolation or with a pseudo-realistic environment, testers can spend more time on the actual testing itself.

Testing is vital for ensuring how an application performs in the long run and hence it is always advisable to dedicate a good amount of effort to ensure that all possible aspects such as functionality, security, performance, availability, and compatibility, are tested as thoroughly as possible. For more information on how you can optimize the performance of your application and reduce downtime and disruption, you can visit our website, or write to us at mail@magicfinserv.com so that we can set up a call or guide you through.

On the 24th of November, Americans would be partaking in the traditional thanksgiving dinner of a stuffed roast turkey, mashed potatoes, greens, and cranberry sauce among others – an American tradition that has been carried down for generations. A day or two earlier, if the turkey is lucky enough, it would have received the presidential pardon. As Thanksgiving nears, we have developed our thanksgiving menu based on the foundation of our data expertise and prepared with a DevOps and AGILE approach and with generous sprinklings of AI, ML, Cloud, Managed Services, and Automation.

1) Starting with the Centerpiece or Showstopper – The Roasted Turkey.

The showstopper of any thanksgiving meal is of course the turkey. The recipe and flavorings are generally a secret and passed down from one generation to the next or developed with a lot of passion and enthusiasm. Nonetheless, there is no second-guessing that for a crackling turkey roast you have to do a lot of groundwork before you put it in the oven for a roast – thawing it completely, soaking it in brine for long enough to bring out the flavors, and being generous with the baste of butter and maple syrup for ensuring a lovely golden crisp roast.

Magic FinServ’s showstopper for FinTechs and Financial Institutions – DeepSightTM

Whether it is reconciliations, invoice management, trade and loan instructions, structured and semi/unstructured structured data extraction for Shareholding and Voting Rights, Financial Forecasting, Day-to-Day trading, AML, ESG compliance, etc., there’s a lot of groundwork that modern enterprises have to engage in to ensure that data is accurate and up-to-date. For a seamless transition from excel sheets, pdf files, paper documents, social media, etc., to a single source of truth, last-mile process automation, integrated processes, ready accessibility, and transparency act as key differentiators for any financial organization.

Magic FinServ’s USP is that we understand the needs of the financial markets – Asset Managers, Hedge Funds, Banks, FinTechs, Fund Advisors, etc., better than the others. We speak the same language and understand the business ecosystem better having carried out several successful transitions. Our bespoke tool DeepSightTM for data extraction, transformation, and delivery in standardized formats that can be easily integrated to algorithms and platforms to make a critical difference in terms of saving working hours and dollars and enhancing revenue opportunities. To know more: Magic DeepSight

2) Green Bean Casserole: The Easy and Convenient Thanksgiving Staple

Simple and inexpensive, the green bean casserole is also known as the “jiffy casserole” because it could be quickly made and stored for the dinner in advance. However, there’s a history to the green casserole. According to Cathy Kaufman, president of the Culinary Historians of New York , “Casseroles bound with white sauces became especially prevalent during the Depression as a way of stretching ingredients.”

Administrative AI: The Staple response for greater productivity and cost efficiency

When it comes to financial institutions and fintechs, moonshot projects are good to have, but the results are inconclusive if the back and middle offices struggle under piles of siloed and poor-quality data, manual processes, and legacy systems. Fintechs and Financial Institutions must clean their processes first – by organizing and streamlining back, middle, and front office operations with the most modern means available such as artificial intelligence, machine learning, RPA, and the cloud. To know more you can check our blog: Improve Administrative Processes with AI first before aiming for Moonshot.

3) The Crowd Pleaser: Mashed Potatoes

Turkey is incomplete without the customary sides of mashed potatoes and greens. It is difficult to go wrong with mashed potatoes. Mashed potatoes are the crowd-pleaser, but it must be light and fluffy not gummy and gloopy. Choose the right quality of potatoes – starchy ones like Russet potatoes that just wonderfully soak up the butter and cream. On the other hand, if flavor is what you require, Russet only will not suffice, potatoes that are flavorful such as Yukon Golds are perfect for the buttery flavor.

Magic’s FinServ’s Cloud Services: Cloud Assessment, Cloud Migration, Cloud DevOps, and Cloud Support

The cloud is certainly a crowd-pleaser. However, for most financial organizations and fintechs, the trouble is that the costs exacerbate, and the results are not satisfying, whether you are moving data, applications, or infrastructure if you simply decide to move to the cloud without proper preparation. The risk of not fixing it is higher. Some of the common problems that financial organizations and fintechs face in a nutshell when it comes to the cloud are:

  • Choosing the cloud as a panacea instead of
  • Choosing the wrong supplier or the wrong service plan
  • Losing control over your data, and lacking bandwidth

You could read more about what could go wrong with your cloud journey in detail in our blog: Saving Costs with the cloud: best practices for banks and financial institutions.

We ensure that you get the best that the cloud promises – agility, scalability, and above all cost optimization. Our multidisciplinary team is well-versed in cloud economics, and we take immense pride in developing a clear set of agreed success criteria for optimized and cost-effective cloud journeys.

4) Turkey is incomplete without Gravy: Here’s what is required to make sans the lumps

Gravy is an essential part of the Thanksgiving menu. It’s a must-have for turkey. However, if you are a novice, you could end up messing up this simple dish. The trick for making a good gravy is ensuring that the cornstarch/thickener is dissolved well. Also, you could reserve the turkey drippings to give it the distinctive flavor. It is these little things that matter, but obviously you would know unless there’s an expert to give you the helping hand.

Magic FinServ’s Advisory Services: A little help from friends for a transition minus the lumps

While it is all good to be independent and start from scratch. Some journeys require the expert’s advice. When you need to scale up quickly or remain competitive, our consultancy services help you decipher where exactly you can save costs and ensure productivity. Magic FinServ’s team of advisors combining the best of technology and finance understand the challenges associated with digital transformation in a disruptive time and help their clients pilot and optimize their transformative journeys with the appropriate mix of new technologies and processes for a delicious add-on.

5) Mac and Cheese: Nothing beats this Rocking Combo

Mac and Cheese are a quintessential part of Thanksgiving dinner. Nothing beats this rocking combo. Likewise, our partnership with EDMC for DCAM.

For if there is anything that gives an organization an edge – it is data.

Data is what empowers wealth managers to carry out their fiduciary duties effectively.

Data is at the center of incisive and accurate financial forecasting that saves the day from another

Data has the capability to recession-proof the organization in these troubled times.

As a FinTech or Financial Organization, you rely on Magic FinServ to facilitate highly accurate and incisive forecasts by regulating the data pool. With our DCAM strategy and our bespoke tool – DeepSightTM , you can get better and better at predicting market outcomes and making timely adjustments.

6) Cranberry sauce: Original or Out of the Can as you like it

When it comes to cranberry sauce, you can either get it canned or make it from a scratch. It is basically a case of as-you-like-it. But canned cranberry sauce is nowhere as delicious and wholesome as sauce made from fresh cranberries. Hence, cranberry sauce made from a scratch is our clear frontrunner compared to the readymade ones.

The same is true for automation — when it’s built to meet the needs of your organization it will create significant ROI.

We have other interesting items too in our spread for thanksgiving such as Test Automation and Product and Platform testing which our Thanksgiving spread would be incomplete without because doing business in these testing times would require continuous innovation to deliver superior quality services and products to consumers while keeping the operation costs optimized.

Reach out to us. Soon!

Hoping that you have enjoyed the spread. Happy Thanksgiving and Happy Hannukah! And a super binge Black Friday! For more on our thanksgiving menu and Black Friday Super Binge reach out to us at mail@magicfinserv.com

Trick or Treat! How FinTechs and FIs can overcome their biggest fears as Halloween nears?

Just about everybody in the now iconic Game of Thrones (GOT) series – Ned Stark to Jon Snow and Arya Stark, talked in hushed whispers about the coming of winter as a forewarning of harsher times in the months ahead and the need to be prepared.

With Winter around the corner, Halloween’s almost here. Honoring the Celtic tradition of Samhain, Halloween in ancient times was meant to be a preparation for the harsh months ahead. Bonfires were built in honor of the saints and for protection from the ghosts and other evil spirits – who supposedly walked the earth on this day. Carving out a pumpkin lantern and going trick-or-treating were all meant to ward off evil spirits, and the tradition continues to date.

We have decided to carry forth time-honored rituals of scaring your guts out this Halloween. So be prepared! Here’s a listing of the biggest scares for fintechs and financial institutions as Halloween approaches.

Heading towards a Recession: Winter is Coming!

There’s a foreboding that this winter will be difficult with no resolution to the Ukraine-Russia conflict in sight and Europe in the midst of a massive power shortage crisis that could soon escalate into the coldest winter ever for Europe with households across the continent being asked to exercise Thrift and Caution. In America as well, things are looking none too bright.

  • According to the latest Economist/YouGov Poll 3 in 5 Americans believe the US is heading for an ugly downturn. (Source: BBC) The once-roaring housing market is showing signs of slowing down. Is it a replication of the 2008 downturn? We do not know yet, but the similarities are hard to ignore.
  • It is tough times ahead for Fintechs as the Financial Times estimates that an astronomical half a trillion dollars have been wiped from the valuation of the same fintechs that benefited the most from the IPO boom in 2020 as “everyone was stuck at home and buying stuff online.
  • Recently listed FinTechs have fared the worst, with cumulative market capitalization falling by $156bn in 2022, and if each stock is measured from its all-time high, around $460bn has been lost.
  • Buy Now, Pay Later platform Klarna stocks plunged by 85% to $ 6.7 bn in July. Even well- established players like PayPal, Block, Robinhood, and Upstart have not fared any better. Robinhood has been under the Securities and Exchange Commission scanner for perceived conflicts of interest. Klarna too has had run-ins with the regulators. In short, fintechs are having a tough time convincing key stakeholders – investors, end customers, and regulators that their business model is what is best needed for the current times.

Beating the Scare!

Magic FinServ’s advisory team combines the best of both – finance and technology – for a comprehensive understanding of the complex business requirements of the fintechs – be it security, meeting regulatory requirements, or meeting customer expectations. Even in these recessionary times, we can help you drive ideas to execution with both speed and ROI.

Rise of the White Walkers: Untested Business Models and Escalating Costs

The white walkers are icy-eyed, sword-wielding undead that constituted the biggest threat to the existence of Jon Snow, Daenerys Targaryen, and the collected forces at Winterfell.

In fintech terms, untested business models and lack of profits coming from moonshot projects are the biggest threats to Fintech existence in 2022 and beyond as investor confidence in projects that have failed to take off, or are beset by regulatory issues, or have not reaped the expected results, has slumped.

This is evident in the findings of the research firm CB Insights, which has indicated that there is an 18% drop in fintech funding between the last quarter of 2021 and the first of 2022. It is also likely that with the Feds hiking the interest rates in the third quarter, business loans will get harder to repay, hence there is an overarching need to relook strategy and prioritize areas where automation and emerging technologies can do the heavy lifting and curb costs. Here is how Magic FinServ can help you find a middle ground between innovation and ROI.

  1. Begin with a robust data governance and management strategy

    Good data opens up many new opportunities, but bad data is stressful and can take you back by the ages. Data is the deal-breaker for almost all financial organizations. A sound data governance and management strategy can redress many of the red-herrings of modern financial organizations – data security, regulatory compliance, and application launch and performance.

  2. Becoming compliant

    Not just the Fintechs, even reputed banks and financial institutions run the risk of running foul with the regulators due to their aging and siloed IT systems which is a ticking bomb for data breaches. With proper data governance and management mechanism issues related to access of data, identifying how sensitive and identifiable it is, tracking access and ensuring that it is legitimate, and also ensuring that access is guided by regulatory requirements can be easily addressed.

  3. Identify areas where AI and automation can do heavy lifting

    Resources are scarce. Though employees are increasingly being prevailed upon to come back-to-office, the cloud has made it possible to work remotely as well in times of crisis. In the capital markets and financial services where data has extrapolated over the years ensuring a single source of truth and identifying areas where automation be implemented not just for streamlining processes and but for ensuring deeper insights as well.

Magic FinServ’s ready-made solutions for Fintechs lower ROI and ups Innovation

With our years of experience in capital markets and finance and several successful implementations over the years, we enable a custom-fit solution to all your concerns. We firmly believe that it is essential to set the house in order first – and by that we mean the back-end and middle office where massive amounts of data existing in silos create chaos and clog down workloads and pipelines.

Our reusable frameworks and technology IPs are just what you need to exercise thrift in these uncertain times. After all, the costs of rework and duplicity are humongous. We have also come up with new and innovative ideas and solutions for providing transparency and real-time information for improving trading outcomes in the financial services industry.

The Wicked Queen of the House of Lannister: Poor Data Quality

There have been plenty of wicked Queens in our fairy tales – from the Red Queen in Alice in Wonderland who goes “off with her head” every time her wishes are unmet to Snow White’s evil stepmother who gave her the poisoned apple and put her to sleep for years to come, but none as evil as Cersei Lannister in the Game of Thrones. She redefined evil.

While it would be misplaced to compare bad or poor-quality data to the Evil Queens, they are indeed a source of misery for many financial services organizations. The overall impact of poor-quality data on the economy is huge. IBM indicated that poor data quality wipes away $3.1 trillion from the U.S. economy annually.

Bad quality data is undesirable because:

  • It lowers employee morale
  • Productivity is low
  • Results in system outages and high maintenance costs
  • Biased and inaccurate outcomes despite the use of high-end AI engines

Unfortunately, with no means to measure the impact of bad data on businesses, a majority of organizations are still clueless as to how they can do things better.

On the other hand, sound data management and robust data practices could reap untold benefits. For example, if 1000 businesses were able to increase data accessibility by just 10%, it would generate more than $65 million in additional net income.

Treat? Getting your Data in order with Magic FinServ

We address all the data woes of organizations – poor data quality, spiraling data management costs, and cost-effective data governance strategy. And it all begins with the pre-processing of data at the back- end, aggregating, consolidating, tagging, and validating it.

With organizations hard-pressed for time, data quality takes the back seat. Now no more.

  • Our experts are well versed in data management technologies and databases like MongoDB, Redis Cache, MySQL, Oracle, Prometheus, Rocks dB, Postgres, and MS SQL Serve.
  • Partnerships with the industry leaders like DMAC for ensuring a cost-effective data governance strategy in sync with the best in the trade.
  • Magic FinServ’s DeepSightTM for data extraction and transformation and deep insight from a varied range of sources in a plethora of formats in standardized formats that can be easily integrated into analysis, algorithms, or platforms.
  • Our machine-learning-based tools optimize operational costs by using Al to automate exception management and decision-making and deliver 30% – 70% cost savings in most cases.

Tightening the Belt

2022 and 2023 will be tough. No doubt about that but many still predict that there will be a soft landing, not a full-fledged recession. The source of that optimism is the American job market which in August added 315,000 new workers in August. The US Federal Reserve Governor Christopher Waller recently reiterated that the robust US labor market was giving America and Americans the flexibility to be aggressive in their fight against inflation. Nevertheless, fintechs still need to go aggressive with digital transformation and data democratization strategies to reign in the costs with AI, ML, and the Cloud. So, if there is more that you would like to know contact us today at mail@magicfinserv.com.

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