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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 DeepSight^TM 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 – DeepSight^TM – 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 mail@magicfinserv.com.

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 DeepSight^TM 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 – DeepSight^TM , 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 mail@magicfinserv.com

Any talk about Data Governance is incomplete without Data Onboarding. Data onboarding is the process of uploading the customer’s data to a SaaS product often involving ad hoc manual data processes. Data Onboarding is the best use case of Intelligent Automation (IA).

If done correctly, data onboarding can result in high-quality data fabric (the golden key or the single source of truth (SSOT)) for use across back, middle, and front office for improving organizational performance, meeting regulatory compliance, and ensuring real-time, accurate and consistent data for trading.

Data Onboarding is critical for Data Governance. But what happens when Data Onboarding goes wrong?

• Many firms struggle to automate data onboarding. Many continue with the conventional means of data onboarding such as manual data entry, spreadsheets, and explainer documents. In such a scenario, the benefits are not visible. Worse, inconsistencies during data onboarding results in erroneous reporting, leading to non- compliance.
• Poor quality data onboarding could also be responsible for reputational damage, heavy penalties, loss of customers, etc., when systemic failures become evident.
• Further we cannot ignore that a tectonic shift is underway in the capital markets – trading bots and crypto currency trading are becoming more common, and they require accurate and reliable data. Any inconsistency during data onboarding can have far- reaching consequences for the hedge fund or asset manager.
• From the customer’s perspective, the longer it takes to onboard, the more frustrating it becomes as they cannot avail the benefits until the data is fully onboarded. End result – customer dissatisfaction! Prolonged onboarding processing is also a loss for the vendor as they cannot initiate the revenue cycle until all data is onboarded. This leads to needless revenue loss as they wait for months before they receive revenue from new customers.

Given the consequences of Data Onboarding going wrong, it is important to understand why data onboarding is so difficult and how it can be simplified with proper use cases.

Why is Data Onboarding so difficult?

When we talk about Data Governance, we are simply not talking about Data Quality Management, we are also talking about Reference and Master Data Management, Data Security Management, Data Development, Document and Content Management. In each of the instances mentioned, data onboarding poses a challenge because of messy data, clerical errors, duplication of data, and dynamic nature of data exchanges.

Data onboarding is all about collecting, validating, uploading, consolidating, cleansing, modeling, updating and transforming data so that it meets the collective need of the business – in our case the asset manager, fintech, bank, FI, or hedge funds engaged trading and portfolio investment.

Some of the typical challenges faced during data acquisition, data loading, and data transformation are underlined below:

Data Acquisition and Extraction

• Constraints in extracting heavy datasets, availability of good APIs
• Suboptimal solutions like dynamic scrapping in case API are not easily accessible
• Delay in source data delivery from vendor/client
• Receiving revised data sets and resolving data discrepancies across different versions
• Formatting variations across source files like missing/ additional rows and columns
• Missing important fields/ corrupt data
• Filename changes

There are different formats in which data is shared – CSV files, ADI files, spreadsheets. It is cumbersome to onboard data in these varied formats.

Data Transformation

Converting data into a form that can be easily integrated with workflow or pipeline can be a time-taking exercise in the absence of standard taxonomy. There’s also the issue of creating a unique identifier for securities amongst multiple identifiers (CUISP, ISIN etc.). In many instances, developers end up cleaning messy files, which is not at all worthwhile.

Data Mapping

With data structures and formats different for Source and Target systems, data onboarding becomes difficult as data mapping – mapping the data coming in with the relevant fields in the target system poses a huge challenge for organizations.

Data Distribution/Loading

With many firms resorting to the use of spreadsheets and explainer documents, data uploading is not as seamless as it could be. File formatting discrepancies with the downstream systems and data reconciliation issues between different systems could easily be avoided with Intelligent Automation or Administrative AI.

Data Onboarding builds a bridge for better Data Governance

“Without a data infrastructure of well-understood, high-quality, well-modeled, secure, and accessible data, there is little chance for BI success.” Hugh J Watson

When we talk about the business-driven approach to Data Governance, the importance of early wins cannot be negated and hence the need for streamlining Data Onboarding with the right tools and technologies for ensuring scalability, accuracy, and transparency while keeping in mind affordability.

As the volume of data grows, data onboarding challenges will persist, unless a cohesive approach that relies on people, technology, and data is employed. We have provided here two use cases where businesses were able to mitigate their data onboarding challenges with Magic FinServ’s solutions:

After all Comprehensive Data Governance requires Crisper Data Onboarding.

Case 1: Investment monitoring platform data onboarding – enabling real-time view of positions data

Investment Monitoring Platform automates and simplifies shareholder disclosure, sensitive industries and position limit monitoring and is a notification system for filing threshold violations based on market enriched customer holding, security, portfolio, and trade files. Whenever a new client is onboarded into the application, the client’s implementation team takes care of the Initiation, Planning, Analysis, Implementation and Testing of Regulatory filings. We analyzed customer’s data during the Planning phase. Data such as the Fund and Reporting structure, Holdings, Trading Regimes, and Asset Types etc., were analyzed from the Reference Data perspective. As a part of the solution, after the analysis, the reference data is set up and source data loaded with the requisite transformation, followed by a quality vetting and completeness check. As a result of which our client was able to have a real-time view of the positions data which keeps flowing into the application in real time.

Case 2: Optimizing product capabilities with streamlined onboarding for regulatory filings

The requirement was for process improvement while configuring jurisdiction rules in the application. The client was also facing challenges in the report analysis that their client required for comparing the regulatory filings. Streamlining the product and optimizing its performance required a partner with know-how in collecting, uploading, matching, and validating customer data. Magic FinServ’s solution consisted of updating the product data point document – referred to by clients for field definitions, multiple field mapping, translations, code definitions, report requirements, etc. This paved the way for vastly improved data reconciliation issues between different systems.

The client’s application had features for loading different data files related to Security, Position, Transactions, etc., for customizing regulatory rule configuration, pre-processing data files, creating customized compliance warnings, direct or indirect jurisdiction filings, etc. We were able to maximize productivity by streamlining these complex features and documenting it. By enabling the sharing of valuable inputs across teams, the errors and omissions in data/customer were minimized while product’s capabilities were enhanced manifold times.

The importance of Data Governance and Management be ascertained from the success stories of Hedge Funds like Bridgewater Associates, Jana Partners, and Tiger Global. By implementing a robust Data Governance Approach, they have been able to direct their focus on high value stocks (as is the case with Jana Partners) or ensure high capitalization (Tiger Global).

So, it’s your turn now to strategize and revamp your data onboarding!

Paying heed to data onboarding pays enormous dividends

If you have not revamped your data onboarding strategy, it is time to do so now. As a critical element of the Data Governance approach, it is imperative that data onboarding should be done properly and without needless human intervention and the shortest span of time to meet the competitive needs of capital markets. Magic FinServ with its expertise in Client Data Processing/Onboarding with proficiency in Data Acquisition, Cleansing, Transformation, Modeling and Distribution can guide you through the journey. A professionally and systematically supervised data onboarding results in detailed documentation of data lineage, something very critical during data governance audits and subsequent changes. What better way to prevent data problems from cascading into a major event than doing data onboarding right. A stitch in time after all saves nine!

For more information about how we can be of help write to us mail@magicfinserv.com