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

Get Insights Straight Into Your Inbox!