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

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