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

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

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 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

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

QA teams are struggling to maintain the balance between Time to Market and First Time Right. Time windows for QA are shrinking as release cycles become more frequent and On Demand. The move towards Digital Transformation is making this even more acute. Enter Risk-Based Testing.

The idea of risk-based testing is to focus on testing and spend more time on critical functions. By combining the focused process with metrics, it is possible to manage the test process by intelligent assessment and to communicate the expected consequences of decisions taken. Most projects go through extreme pressure and tight timescales coupled with a risky project foundation. With all these limitations, there’s simply no room for settlement on quality and stability in today’s challenging world, especially in the case of highly critical applications. So, instead of doing more with less and risking late projects, increased costs, or low quality, we need to find ways to achieve better with less. The focus of testing must be placed on aspects of the software that matter most to reduce the risk of failure as well as ensure the quality and stability of the business applications. This can be achieved by risk-based testing. The pressure to deliver may override the pressure to get it right. As a result, the testers of modern systems face many challenges. They are required to-

  1. Calculate software product risks. Identify and calculate, through consultation, the major product risks of concern and propose tests to address those risks.
  2. Plan and judge the overall test effort. Judge, based on the nature and scope of the proposed tests and experience, how expensive and time-consuming the testing will be.
  3. Obtain consensus on the amount of testing. Achieve, through consensus, the right coverage, balance, and emphasis on testing.
  4. Supply information for a risk-based decision on release. Perhaps the most important task of all is to provide information as the major deliverable of all testing.

The Association of Testing and Risk

There are three types of software risk:
  1. Product risk– A product risk is a chance that the product fails in relation to the expected outcome. These types of risks are related to the product definition, the product complexity, the lack of stability of requirements, and the potential defect-proneness of the concerned technology that can fail meeting requirements. Product risk is indeed the major part of concern of the tester.
  2. Process risk– process risk is the potential loss resulting from an improper execution of processes and procedures in conducting a Financial Institution’s day-to-day operations. These risks relate primarily to the internal aspects of the project including- its planning and scrutinizing. Generally, risks in this area involve the testers underestimating the complexity of the project and therefore not putting in the effort or expertise needed. The project’s internal management including efficient planning, controlling, and progress monitoring is the project management concern.
  3. Project risk– A project risk is an uncertain event that may or may not occur during a project. Contrary to our everyday idea of what “risk” means, a project risk could have either a negative or a positive effect on progress towards project objectives Such types of risk are related to the context of the concerned project as a whole.

The purpose of structured test methodologies tailored to the development activities in risk-based testing is to reduce risk by detecting faults in project deliverables as early as possible. Finding faults early, rather than late, in a project reduces the reworking necessary, costs, and amount of time lost.

Risk-based Testing Strategy

Risk-based testing – Objectives
  • To issue relevant evidence showing that all the business advantages required from the systems can be achieved.
  • To give relevant data about the potential risks involved in the release (as well as use) of the concerned system undergoing the test.
  • To find defects in the software products (software as well as documentation) to make necessary corrections.
  • To highlight and build the impression that the stated (as well as unstated) needs have been successfully met.

Risk-based test process – Stages

Stage 1: Risk Identification

Risk Identification is the activity that examines each element of the program to identify associated root causes that can cause These are derived from existing checklists of failure modes (most commonly) and generic risk lists that can be used to seed the discussions in a risk workshop. Developers, users, technical support staff, and testers are probably best placed to generate the initial list of failure modes. The tester should compile the inventory of risks from practitioners and input schedule the risk workshop, and copy the risk inventory to the attendees. Ensuring that adequate and timely risk identification is performed is the responsibility of the test manager or product owner is the first participant in the project.

Stage 2: Risk Analysis

Define levels of uncertainty. Once you have identified the potential sources of risk, the next step is to understand how much uncertainty surrounds each one. At this stage, the risk workshop is convened. This should involve application architecture from the business, development, technical support, and testing communities. The workshop should involve some more senior managers who can see the bigger picture. Ideally, the project manager, development manager, and business manager should be present.

Stage 3: Risk Response

The risk response planning involves determining ways to reduce or eliminate any threats to the project, and also the opportunities to increase their impact. When the candidate risks have been agreed on and the workshop is over, the tester takes each risk in turn and considers whether it is testable. If possible, the tester then specifies a test activity or technique that should meet the test objective. Typical techniques include requirements or design reviews, inspections or static analysis of code or components, or integration, system, or acceptance tests.

Stage 4: Test Scoping

A test scope shows the software testing teams the exact paths they need to cover while performing their application testing operations Scoping the test process is the review activity that requires the involvement of all stakeholders. At this point, the major decisions about what is in and out of scope for testing are made; it is, therefore, essential that the staff in the meeting have the authority to make these decisions on behalf of the business, the project management, and technical support.

Stage 5: Test Process

The process of evaluating a product by learning about it through experiencing, exploring, and experimenting, includes to some degree: questioning, study, modeling, observation, inference, etc. At this point, the scope of the testing has been agreed on, with test objectives, responsibilities, and stages in the overall project plan decided. It is now possible to compile the test objectives, assumptions, dependencies, and estimates for each test stage and publish a definition for each stage in the test process.


When done effectively, risk-based assessment and testing can quickly deliver important outcomes for an organization. Because skilled specialists assess risk at each stage of delivery, the quality of deliverables starts with the requirements.

Know how Magic FinServ can help you or reach out to us at

All of a sudden there has been an increasing consensus that wealth management advisory services are something that we all need – not just for utilizing our corpus better, but also for gaining more accurate insights about what to do with our monies – now that there are so many options available. This has been partly due to the proliferation of platforms including the Robo- advisory services that deliver financial information on the fly. And partly due to psychological reasons. We all have heard stories of how investing “smart” in stocks, bonds, and securities resulted in a financial windfall and ludicrous amounts of wealth coming into the hand of the lucky ones while with our fixed income and assets we only ended up with steady gains over the years. So yes, we all want to be that “lucky one” and want our money to be invested better!

Carrying out the Fiduciary Duties!

But this blog is not about how to invest “smart.” Rather the focus is on wealth managers, asset managers, brokers, Registered Investment Advisors (RIA), etc., and the challenges they face while executing their fiduciary duties.

As per the Standard of Conduct for Investment Advisers, there are certain fiduciary duties that the financial advisors/ investment advisors are obligated to adhere to, for example, there’s the Duty of Care which makes it obligatory for investment advisors to ensure the best interests of the client and:

  • Provide advice that is in the clients’ best interests
  • Seek best execution
  • Provide advice and monitoring over the course of the relationship

However, due to multiple challenges – primarily related to the assimilation of data, that makes it difficult to fulfil the fiduciary obligations. The question then is how wealth managers can successfully operate in complex situations and with clients with large portfolios and retain the personal touch.

The challenges enroute

Investors today desire, apart from omnichannel access, integration of banking and wealth management services, and personalized offerings, and are looking at wealth advisors that can deliver all three. In fact, fully 50 percent of high-net-worth (HNW) and affluent clients say their primary wealth manager should improve digital capabilities across the board. (Source: McKinsey)

Lack of integration between different systems: The lack of integration between different systems is a major roadblock for the wealth manager, as is the lack of appropriate tools for cleaning and structuring data. As a result, wealth management and advisory end up generating a lot of noise for the client.

Multiple assets and lack of visibility: As a financial advisor, the client’s best interests are paramount. Visibility into the various assets the client possesses is essential. But what if the advisor does not see everything? As the client has multiple assets – retirement plan, stock and bond allocations, insurance policy, private equity investments, hedge funds, and others, without visibility how can you execute your fiduciary duties to the best of your ability.

Data existing in silos: The problem of data existing in silos is a huge problem in the financial services sector. Wealth managers, asset managers, banks, and the RIAs require a consolidated position of the clients’ portfolios, so that no matter the type of asset class, the data is continually updated and made available. Let’s take the example of the 401K – the most popular retirement plan in America. Ideally, all the retirement plan accounts should be integrated. However, when this is not the case, it becomes difficult to take care of the client’s best interests.

Delivering personalized experience: One of the imperatives when it comes to financial advice is to ensure that insights or conversations are customized as per the customer’s requirements. While someone might desire inputs in a pie chart form, others might require inputs in text form. So apart from analyzing and visualizing portfolio data, and communicating relevant insights, it is also essential to personalize reporting so that there is less noise.

Understanding of the customer’s risk appetite: A comprehensive and complete view of the client’s wealth – which includes the multiple asset classes in the portfolio – fixed income, alternative, equity, real assets, directly owned, is essential for an understanding of the risk appetite.

The epicenter of the problem is of course poor-quality data. Poor quality or incomplete data, or data existing in silos and not aggregated is the reason why wealth advisory firms falter when it comes to delivering sound fiduciary advice. They are unable to ascertain the risk appetite, or fix incomes, or access the risk profile of the basket (for portfolio trading). More importantly, they are unable to retain the customer. And that’s a huge loss. Not to mention the woeful loss of resources and money when instead of acquiring new customers or advising clients, highly paid professionals spend their time in time-intensive portfolio management and compliance tasks and end up downloading tons of data in multiple formats for aggregation and then for analytics and wealth management.

Smart Wealth Management = Data Consolidation and Aggregation + Analytics for Smart Reporting

Data consolidation and aggregation is at the heart of wealth management practice. is undeniable.

  • A complete view of all the customer’s assets is essential – retirement plan, stock and bond allocations, insurance policy, private equity investments, hedge funds, and others.
  • Aggregate all the assets. Bring together all multiple data sources/ custodians involved
  • Automate the data aggregation and verification in the back office. Build the client relationships instead of manually going through data
  • In-trend trading such as portfolio trading wherein a bundle of bonds of varying duration and credit quality are traded in one transaction. It requires sophisticated tools to access the risk profile of the whole basket (in the portfolio trade) (Source: Euromoney)
  • Ensure enhanced reporting or sharing the data in the form that the customer requires – pie charts, text, etc., using sophisticated analytics tools for an uplifting client experience using a combination of business intelligence and analytics.

How can we help?

Leverage Magic DeepSightTM for data aggregation and empower your customers with insightful information

Magic FinServ’s AI Optimization framework utilizing structured and unstructured data build tailored solutions for every kind of financial institution delivering investment advice – banks, wealth managers, brokers, RIAs, etc.

Here’s one example of how our bespoke tool can accelerate and elevate the client experience.

Data aggregation: Earlier we talked about data consolidation and aggregation. Here we have an example of how we can deliver on when it comes to clarity, speed, and meaningful insights from data. Every fund is obligated to publish its investment strategy quarterly. Magic FinServ’s AI optimization framework can potentially provide the capability to read these details from public websites. Bringing together data from disparate sources and data stores and consolidating it by combining our bespoke technology – DeepSightTM – that has a proven capability to extract insights from data in public websites such as 401K, 10K as well as from unstructured sources such as emails and aggregate them to ensure a single source of truth, which provides intelligence and insights to carry out portfolio trading and balancing exercise, scenario balancing and forecasting among others.

Business Intelligence: Our expertise in building digital solutions that leverage content digitization and unstructured / alternative data using automation frameworks and tools improve trading outcomes in the financial services industry.

DCAM authorized partners: As DCAM authorized partners, leverage the best-in-class data management practices for evaluating and accessing data management programs, based on core data management principles.

Keeping up with the times:

The traditional world of Wealth Management Firms is going through a sea change. Partly due to the emergence of tech-savvy high-net-worth individuals (HNWI) who demand more in terms of content, and partly due to increasing role played by Artificial Intelligence, Machine Learning and natural language processing. Though, it is still the early days of AI, it is evident that in wealth management, technology is increasingly taking on a larger role in delivering content to the client while taking of aspects like cybersecurity, costs, back-office efficiency and automation, data analysis and personalized insights, forecasting and improving the overall customer experience.

To know more about how Magic FinServ can amplify your client experience, you can write to us

Jim Cramer famously predicted, “Bear Stearns is fine. Do not take your money out. “

He said this on an episode of Mad Money on 11 March 2008.

The stock was then trading at $62 per share.

Five days later, on 16 March 2008, Bear Stearns collapsed. JPMorgan bailed the bank out for a paltry $2 per share.

This collapse was one of the biggest financial debacles in American history. Surprisingly nobody saw it coming (except Peter, who voiced his concerns in the now infamous Mad Money episode). Sold at a fraction of what it was worth – from $20 billion capitalization to all-stock deal values of $ 236 million, approximately 1% of what it was worth earlier, there are many lessons from the Bear Stearns fall from grace.

Learnings from Bear Stearns and Lehman Brothers debacle

Bear Stearns did not fold up in a day. Sadly, the build-up to the catastrophic event began much earlier in 2007. But no one heeded the warning signs. Not the Bear Stearns Fund Managers, not Jim Cramer.

Had the Bear Stearns Fund Managers ensured ample liquidity to cover their debt obligations; had they been a little careful and understood and accurately been able to predict how the subprime bond market would behave under extreme circumstances as homeowner delinquencies increased; they would have saved the company from being sold for a pittance.

Or this and indeed the entire economic crisis of 2008, was the rarest of rare events, beyond the scope of human prediction – a Black Swan event, an event characterized by rarity, extreme impact, and retrospective predictability. (Nassim Nicholas Taleb)

What are the chances of the occurrence of another Black Swan event now that powerful recommendation engines, predictive analytics algorithms, and AI and ML parse through data?

In 2008, the cloud was still in its infancy.

Today, cloud computing is a powerful technology with an infinite capacity to make information available and accessible to all.

Not just the cloud, financial organizations are using powerful recommendation engines and analytical models for predicting the market tailwinds. Hence, the likelihood of a Black Swan event like the fall of Bear Stearns and Lehman Brothers seems remote or distant.

But faulty predictions and errors of judgment are not impossible.

Given the human preoccupation with minutiae, instead of possible significant large deviations, even when it is out there like an eyesore, black swan events are possible (the Ukraine war and subsequent disruption of the supply chain were all unthinkable before the pandemic).

Hence the focus on acing the data game.

Focus on data (structured and unstructured) before analytics and recommendation engines

  • The focus is on staying sharp with data – structured and unstructured.
  • Also, the focal point should be on aggregating and consolidating data and ensuring high-level data maturity.
  • Ensuring availability and accessibility of the “right” or clean data.
  • Feeding the “right” data into the powerful AI, ML, and NLP-powered engines.
  • Using analytics tools and AI and ML for better quality data.

Data Governance and maturity

Ultimately financial forecasting – traditional or rolling is all about data from annual reports, 10-K reports, financial reports, emails, online transactions, contracts, and financials. As a financial institution, you must ensure high-level data maturity and governance within the organization. For eliciting that kind of change, you must first build a robust data foundation for financial processes, as advanced algorithmic models or analytics tools that organizations use for prediction and forecasting require high-quality data.

Garbage in would only result in Garbage out.

Consolidating data – Creating a Single Source of Truth

Source: Deloitte
  • The data used for financial forecasting comes primarily from three sources:
    • Data embedded within the organization – historical data, customer data, alternative data – or data from emails and operational processes
    • External: external sources and benchmarks and market dynamics
    • Third-party data: from ratings, scores, and benchmarks
  • This data must be clean and high-quality to ensure accurate results downstream.
  • Collecting data from all the disparate sources, cleaning it up, and keeping it in a single location, such as a cloud data warehouse or lake house – or ensuring a single source of truth for integration with downstream elements.
  • As underlined earlier, bad-quality data impairs the learning of even the most powerful of recommendation engines, and a robust data management strategy is a must.
  • Analytics capabilities are enhanced when data is categorized, named, tagged, and managed
  • Collating data from different sources – this is what it was and what is – historical trend analysis.

Opportunities lost and penalties incurred when data is not of high quality or consolidated

Liquidity assumption:

As an investment house, manager, or custodian, it is mandatory to maintain a certain level of liquidity for regulatory compliance. However, due to the lack of data, lack of consolidated data, or lack of analytics and forecasting, organizations end up making assumptions for liquidity.

Let’s take the example of a bank that uses multiple systems for different portfolio segments or asset classes. Now consider a scenario where these systems are not integrated. What happens? As the organization fails to get a holistic view of the current position, they just assume the liquidity requirements. Sometimes they end up placing more money than required for liquidity, which results in the opportunity being lost. Other times, they place less money and become liable for penalties.

If we combine the costs of the opportunity lost and the penalties, the organization would have been better off investing in better data management and analytics.

Net Asset Value (NAV) estimation:

Now let’s consider another scenario – NAV estimation. Net Asset Value is the net value of an investment fund’s assets less its liabilities. NAV is the price at which the shares of the funds registered with the U.S. Securities and Exchange Commission (SEC) are traded. For calculation of month-end NAV, the organization would require the sum of all expenses. Unfortunately, as all the expenses incurred are not declared on time, only a NAV estimate is provided. Later, after a month or two, once all the inputs regarding expenses are made available, the organization restates the NAV. This is not only embarrassing for the organization as they have to issue a lengthy explanation of what went wrong but are also liable for penalties. Not to mention the loss of credibility when investors lose money as the share price is incorrectly stated.

DCAM Strategy and DeepSightTM Strategy – making up for lost time

Even today, when we have extremely intelligent new age technologies at our disposal – incorrect predictions are not unusual. Largely because large swathes of data are extremely difficult to process – especially if you aim to do it manually or lack data maturity or have not invested in robust data governance practices.

But you can make up for the lost time. You can 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.

Here’s our DCAM strategy for it:

  • Ensure data is clean and consolidated
  • Use APIs and ensure that data is consolidated in one common source – key to our DCAM strategy
  • Supplement structured data with alternative data sources
  • Ensuring that data is available for slicing and dicing.

To conclude, the revenue and profits of the organization and associated customers depend on accurate predictions. And if predictions or forecasts go wrong, there is an unavoidable domino effect. Investors lose money, share value slumps, hiring freezes, people lose jobs, and willingness to trust the organization goes for a nosedive.

So, invest wisely and get your data in shape. For more information about what we do, email us at

APIs are driving innovation and change in the Fintech landscape with Plaid, Circle, Stripe, or Marqueta, facilitating cheaper, faster, and more accessible financial services to the customer. However, while the APIs are the driving force in the fintech economy, there is not much relief for the software developers and quality analysts (QAs). Their workloads are not automated and there is increasing pressure to release products to the market. Experts like Tyler Jewell, managing director of Dell Technologies Capital, have predicted that there will be a Trillion programmable endpoints soon. It would be inconceivable then to carry out manual testing of APIs as is done by most organizations today. An API conundrum will be inevitable. Organizations will be forced to choose between quick releases and complete testing of APIs. If you choose a quick release, you might have to deal with technical lags in the future and rework. Failure to launch a product in time could lead to a loss of business value.

Not anymore. For business-critical APIs that demand quick releases and foolproof testing, Automation saves time and money and ensures quicker releases. To know more read on.

What are APIs and the importance of API testing

API is the acronym for Application Programming Interface, which is a software intermediary that allows two applications to talk to each other. APIs lie between the application and the web server, acting as an intermediary layer that processes data transfer between systems.

Visual representation of API orientation

Is manual testing of APIs enough? API performance challenges

With the rise in cloud applications and interconnected platforms, there’s a huge surge in the API-driven economy.

Today, many of the services that are being used daily rely on hundreds and thousands of different interconnected APIs – as discussed earlier, APIs occupy a unique space between core application microservices and the underlying infrastructure.

If any of the APIs fails the entire service will be rendered ineffective. Therefore, API testing is mandatory. When testing for APIs, the key tests are as depicted in the graphic below:

So, we must make sure that API tests are comprehensive and inclusive enough to measure the quality and viability of the business applications. Which is not possible manually.

The API performance challenges stem primarily due to the following factors:

  • Non-functional requirements during the dev stage quite often do not incorporate the API payload parameters
  • Performance testing for APIs happens only towards the end of the development cycle
  • Adding more infrastructure resources like more CPU or Memory will help, but will not solve the root cause

The answer then is automation.

Hence the case for automating API testing early in the development lifecycle and including it in the DevSecOps pipeline. The application development and the testing teams must also make an effort to monitor API performance the way monitor the application (from Postman and Manage Engine right up to AppDynamics) and also design the core applications and services with API performance in mind – questioning how much historical data a request carries and whether the data sources are monolith or federated.

Automation of APIs – A new approach to API testing

Eases the workload: As the number of programmable endpoints reaches a trillion (in the near future), the complexity of API testing would grow astronomically. Manually testing APIs using home-grown scripts and tools and open-source testing tools would be a mammoth exercise. Automation of APIs then would be the only answer.

Ensures true AGILE and DevOps enablement: Today AGILE and the ‘Shift Left’ approach have become synonymous with the changing organizational culture that focuses on quality and security. For true DevOps enablement, CI/CD integration, and AGILE, an automation framework, that can quickly configure and test APIs is desired instead of manual testing of APIs.

Automation simplifies testing: While defining and executing a test scenario, the developer or tester must keep in mind the protocols, the technology used, and the layers that would be involved in a single business transaction. Generally, there are several APIs working behind an application which increases the complexity of testing. With automation, even complex testing can be carried out easily.

Detects bugs and flaws earlier in the SDLC: Automation reduces technical work and associated costs by identifying vulnerabilities and flaws quickly saving monetary losses, rework, and embarrassment.

Decreases the scope of security lapses: Manual testing increased the risk of bugs going undetected and security lapses occurring every time the application is updated. However, with automation, it is easier to validate if any update in software elicits a change in the critical business layer.

Win-win solution for developers and business leaders: It expedites the release to market, as the API tests can validate business logic and functioning even before the complete application is ready with the UI. Resolving thereby the API conundrum.

Magic FinServ’s experience in API engineering, monitoring, and automated QA

Magic FinServ team with its capital markets domain knowledge and QA automation expertise along with industry experience helps its clients with:

  • Extraction of data from various crypto exchanges using opensource APIs to common unified data model covering the attributes for various blockchains which helps in:
    • Improved stability of the downstream applications and data warehouses
    • Eliminates the need for web scraping for inconsistent/protected data – web scraping is prevented by 2FA often
    • Use of monitored API platform improved data access and throughput and enabled the client to emerge as a key competitor in the crypto asset data-mart space
  • Extraction of data from various types of documents using Machine/AI learning algorithms and exposing this data to various downstream systems via a monitored and managed API platform
  • Use of AI to automate Smart Contract based interfaces and then later repurpose these capabilities to build an Automated API test bed and reusable framework
We also have other engineering capabilities as:
  • New generation platforms for availability, scalability and reliability for various stacks (Java/.NET/Python/js) using Microservices and Kubernates
    • Our products built uses the latest technology stack in the industry in terms of SPA (Single Page Application) (Automated pipelines/Kubernetes Cluster/Ingres controller/Azure Cloud Hosted) etc.
  • Full stack products in full managed capacity covering all the aspects of products (BA/Development/QA)

APIs are the future, API testing must be future-ready

There’s an app for that – Apple

APIs are decidedly the future of the financial ecosystem. Businesses are coming up with innovative ideas to ease payments, banking, and other financial transactions. For Banks and FinTechs, API tests are not mere tests, these are an important value add as they bring business and instill customer confidence, by ensuring desired outcomes always.

In this blog, part 1 in the series of blogs on Automation in API testing, we have detailed the importance of Automation in API testing. In the blogs that follow, we will have a comprehensive account of how to carry out tests, and the customer success stories where Magic FinServ’s API Automation Suite has provided superlative results. Keep looking out in this space for more! You can also write to us

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