Global Regulatory and Tax Accounting- Point Solution

A point solution which has limited adoption time and that can be integrated with the downstream and upstream application in the business. Instead of created a platform or a product for all the global regulatory changes made by various Global regulators like the IRS, FINRA, SEC, FCA, BASEL, etc. creating a point solution which can be then customized based on the customer technology, business requirements and sophistication of the users and technical capabilities within the organisation

End to End Solution


Advantages of having point solution over platform/products

  1. One size does not fit all
  2. Customer technology culture – Some companies can handle outsourced services and are not interested in customizing platforms. Some IT departments, even in the largest of companies, are more focused on user experience than control and support departments in their efforts match their innovative processes
  3. Easy to get going and straightforward – client can make a decision whether to outsource the entire element to a service provider. Also, reduces the technology complexity when using multiple technologies for the same line of business the diagnosis of the problem is always challenging
  4. Low cost compared to the big enterprise platforms, as this may minimize the data complexity as each client tool would have its database with its structure and integrating these databases is hard
  5. The point solutions can be converted to enterprise platforms through M&A.

Key Success Indicators

  1. Regulatory risk reduction
  2. Cost Optimization through automated workflows
  3. Business Change adaptability with minimum business impact
  4. Robust control framework



Artificial Intelligence and Risk Management

How Can Artificial Intelligence help Investment Banking Risk Management?

Beyond Just Calculations….

The risk management algorithm has always been more about complex calculations. There are various models such as the binomial model, VaR (Value at Risk), Black-Sholes Morten model. These models are put into different simulation modeling algorithms such as Monte Carlo simulation, GARCH (1, 1) also known as Generalized autoregressive conditional heteroskedasticity model, EWMA, which are currently used by the banks. CME (Chicago Mercantile Exchange) developed risk management algorithms PC-SPAN for portfolio margining

  • SPAN has been reviewed and approved by market regulators and participants worldwide.
  • SPAN is the official Performance Bond mechanism of over 50 exchanges and clearing organizations worldwide, making it the global standard for portfolio margining

Risk management is the application of the risk management process which consists in:


The future challenge to integrate Risk management in every area of a company means “operational,” “economical”, and “strategically, Enterprise Risk Management (ERM) will be a need for the future management processes.

The latest developments that are shaping up in the Fintech focused on the Risk Management is an application of Artificial Intelligence in the Financial Risk Management.

How different methodology of a quantitative risk analysis to develop a formal risk management can leverage Artificial Neural Networks

What is similar between an engineer controlling an industrial facility and a bank operations manager controlling payment processing? Both deal with operational risks that require immediate action at the earliest sign of trouble.  Process for both is same as described in below diagram


Financial Institutions are primarily risk managers and manage a variety of financial risks — market, credit, operational, currency, liquidity, and others. For having a robust risk management the bank need to ensure that it should embrace data-informed technologies that are being applied to the following



Why need the Black Box?

Data-Informed Operations

Data-informed operations are the basis for day to day operations. No matter how sophisticated the data collection and processing systems are, a trained human is ultimately responsible for making critical decisions.  The bank operations managers can perform exception processing and error recovery based upon system-generated communications.

Any analytics applications that are guiding their decisions will submit their findings to a human,  to effect any changes to operations. The reason why operations department run by large sized teams who are challenged to keep improving their effectiveness through improved process and use of the latest cutting-edge technology.

Chasing False Alarms

Risk management teams use expert systems, primarily based on rules, to monitor operations and generate alerts. Expert risk managers set alerts based on rules, historical thresholds, specific KPIs, and the tuning of each of these over time takes up a lot of the time of experts to maintain the balance between risk exposure and team efficiency, and that has its consequences.

One of the biggest challenges of improving effectiveness is false alarms raised by analytics technologies currently used. Not being sensitive enough means that the risk exposure is higher, being too sensitive also means the operations team under much pressure is chasing false alarms.

No algorithm is useful in isolation, but instead from the perspective of how it interacts with its environment (data sampling, filtering, and reduction) and also how it manipulates or alters its environment. Therefore, the algorithm depends on an understanding of the environment and also a way to manage the environment


Any rules-based systems pose a major dilemma to an operations person because; the cost of missing an actual exception, models may be tuned extremely conservatively. As a result of this, it significantly increases operational cost, but they also create “alarm fatigue”, in which operators expect false alarms to such an extent that they miss a genuinely positive and allow an improper transaction.

Harry Henderson proposed an AI model which has both the old rules memory and a working memory where the model intelligently learns from the current environment and the rule matching system quickly re-tweaks the rules so as to avoid false alarms without losing the original exceptions




Trends and Human Processing (What is in the BLACK BOX?)

A robust risk management is about dealing with real-time transactional data and historical trends/learnings; there is an important aspect of time that affects how decisions are made. In general, humans are good at interpreting simple trends by looking at slopes and levels, but human have limitations to describe complex patterns. A solution to the problem is if expert AI systems can encode these trends


The problem with the AI system is when different pieces of information don’t arrive at the same time or rate, incorporating the trends in such data into any AI system tends to be difficult.

For example – In the case of the Monte-Carlo simulation the inputs may be fixed, but the frequency of the inputs A, B, C, and D may vary leading to which the random number generation of the Monte-Carlo model(MCM).  May not follow the probability distribution curve which is a function of (PDF), to ensure that various risk has been factored the outputs of the MCM  would be evaluated and tested for the hypothesis using multiple regressive inputs to validate the confidence level of the model.

A critical aspect for the Risk Modeler is to select the appropriate distribution function according to the data available; it can follow any Log function, Normal distribution, Chi –squared distribution function, etc. The modeler also needs to understand the behavior of the data in the practice; typically it is based on an available historical database.


Monte Carlo Simulation Model for risk assessment

This problem is exacerbated in financial services applications where trends are formed (and change) over periods of days, weeks or even years. Operations users cannot be expected to recognize long-term trends in customer behavior without expert system assistance. The result of such difficulties is it increases the number of effort people has to put into confirming alarms by interpreting patterns.


Expert instead of Learning Systems (Mind in the Box)

The next issue is that expert systems do not change by themselves as they have to be programmed by experts. The main advantage of AI system is that the whole process (training and testing) mimics the human brain reasoning like learning occurs in the minds of experts who then apply the lessons of their learning into the next versions of the rule engine base.


With the rapidly changing financial business and data landscape, the operational systems have not evolved quickly enough. This leads to more risk exposure and less optimized use of the margin money.

For exampleGame of Chess


But then the questions comes Can machine ever have a mechanism to reach a conclusion based on just common sense which is beyond logical reasoning? Can machines present facts which are beyond the mathematical formulas? Can machines make a logical deduction of the cases which has the rarest or the rare possibility of occurrence to ensure optimal use of time and resource?

Would AI system get thumbs up or thumbs down in future, is something that only time will tell, as the commercial application of AI has to withstand the challenge of diversity as different Banks, Insurance companies, funds and financial firms don’t speak the same risk – language. Never the less each one of them performs the cost-benefit analysis, sensitivity analysis, scenario analysis which permits to perform both the quantitative and qualitative analysis


Happy Reading!!

Abhinav Gupta

Future of Wealth management Platforms

The Customers don’t care about the platforms; all they care about is the services, experience and the reliability of the platform. 

Wealth management firms can no more compete on price, they are moving towards the value of outcome

Based on the key drivers of the wealth management business, below is the proposed business architecture for the wealth management firms in order to maintain the competitive advantage.

Forward thinking firms are combining both the client facing and back office technology to support multiple client and advisor segments


Making the customer experience and applying technology to improve it across various platforms would drive the long-term loyalty of the customer. The consistent customer experience across the various line of business is the key driver.

Wealth management firms struggle with silos of each product offering, as there is no integration with the front office to the back office application, making it difficult to maintain consistency – IBOR & PBOR solution would help them cover come the current problem

Apart from the customer wealth management advisory firms also needs real-time information and new ways of advisory life-cycle management, which would play an important role in getting more business and as the business grows, need to more sophisticated platform grows.

As the retail wealth management advisory models evolve to reflect increasing needs of the clients they bring more value added services to the clients, the firms at the same time need to keep a tap on the price of the wealth management platforms.

Today the clients are better informed and more technically capable than ever before, with the advances in consumer technology and the 24/7 news cycle enable seamless and almost limitless access to markets information to the clients makes it more and more difficult for the traditional wealth management advisory firms

Deepdive – Wealth Management Ecosystem

There are various business models that operate in retail wealth management, some firms offer a specific business model whereas other firms operate on multiple business models giving the customers the flexibility to choose the business model that suits best to their needs.

No model is likely to dominate the global wealth management because they are designed around different target customers

Primarily there are three main types of business models – Universal banks, Platform players and Private Banks and Brokerages. Various Broker affiliated advisors, RIA (Fee-based business), Hybrid and Dual registration, Robo Advisors etc. use full-service firms, Banks, Independent broker-dealers, Self-directing firms, self-clearing firms, discount brokerages, global financial firms etc. some of these business models are explained in details below.

The Business models differ from country to country and the challenges of different business models are similar. It is a difference in the regulatory regimes of the country. The customization of their services for the national markets and selection of products based on the maturity of the maturity of markets


Key Players

Traditionally, the industry was dominated by private banks and stockbrokers. But there were important regional differences in the dominant types of player. To some extent, this reflected differences in the structure and regulation of the financial services industry as a whole. Broadly, there are two main models:

  • North American model, where the industry is dominated by full-service and discount brokerages and money managers, whose strengths lie in the investment area, rather than in traditional deposit gathering, as noted above, the traditional emphasis here is on a (transaction driven) commission-based business model.
  • The European model, where universal and traditional private banks dominate, due to their ability to offer a comprehensive range of wealth management products and services. The emphasis here is on a fee-based business model.




Registered Investment Advisors (RIA)

These investment advisors (IA) are registered with SEC under the Investment adviser’s act of 1940 in the USA. An IA must adhere to a fiduciary standard of care laid out in the US Investment Advisers Act of 1940. This standard requires IAs to act and serve a client’s best interests with the intent to eliminate, or at least to expose, all potential conflicts of interest which might incline an investment adviser—consciously or unconsciously—to render advice which was not in the best interest of the IA’s clients . It is one of the fastest-growing sectors, benefiting from advisor and asset migration away from wirehouses. Strong client relationships, supported by product and operational support from large-scale platform providers.

The vast majority of broker-dealer firms serving retail clients also operate a separate RIA firm, which we refer to as a corporate RIA. The assets managed by corporate RIAs are not included in the independent RIA subsegment; rather, assets managed under a corporate RIA structure are rolled up under each broker-dealer firm’s subsegment. Representative firms in the independent RIA subsegment include Oxford Financial Group, Shepherd Kaplan, and Appleton Partners.

 Digital advisors/Robo-Advisors

Over recent years, a number of web-based advisors have emerged. They offer above-average advisory quality and act as a gateway to third-party product providers. They are Innovative providers leveraging technology, social media, and communities to attract younger and self-directed investors Data aggregation is a key value proposition of this new breed of Financial Advisors.

Family offices

They serve the very wealthiest clients, acting as an integrated hub for the family’s financial administration. They perform, essentially, three main functions:

  • Specialist advise and planning (including financial, tax, strategic and philanthropic);
  • Investment management (including asset allocation, risk management, investment due diligence and analysis, discretionary asset management and trading); and
  • Administration (including coordination of relationships with financial services providers and consolidated financial reporting).

A family office may be dedicated either to a single family or serve a small number of families. Some of the major private banks, such as Pictet and JP Morgan, have developed their own multifamily offices, but the vast majority are independent specialists (and have, in some instances, evolved from single family offices). Family offices are particularly well-developed in the US and are starting to evolve in Europe

Private Banks

 Is a broad category of players that includes the classic Swiss private banking partnership, mainly targeting HNWIs, these institutions offer clients end-to-end capabilities via a relationship with a senior banker (the relationship manager) that is confidential and founded on trust.

Retail and universal banks

They target affluent clients who need comprehensive advice and who value a close banking relationship and the emphasis is on ‘farming’ their existing customer base, including business banking clients. Examples include Citigroup, HSBC, Bank of America.

Trust banks

Are essentially the US equivalent of the traditional European private bank. Most have their roots in providing trust and custody services but have broadened their product range over the years. They now also provide asset management, insurance and financial, tax and estate planning. Their core target client segment is UHNWIs, but many have also developed tailored propositions for HNWIs

Stockbrokers and Wirehouses

Target self-directed investors and traders for their day-to-day transaction execution and investment needs, they offer low-cost access to a range of investment products as well as to extensive investment research. But they are not exclusively dedicated to affluent clients, do not typically offer much in the way of customized advice and often lack transaction banking products. It is a diverse group, including firms that have their roots in online discount brokering such as E*TRADE, as well as full-service brokers such as Morgan Stanley.

Product specialists

Include hedge funds, private equity funds, mutual funds and structured product providers. Lacking their own captive distribution channels, they manufacture products for distribution across a range of HNW channels, including private banks and financial advisors

Self-clearing retail brokerage (non-Wirehouses)

It refers to a group of large to mid-size national and regional broker-dealer firms (excluding the aforementioned Wirehouses) those clear securities transactions for themselves. Representative firms in this sub-segment include Edward Jones, Ameriprise, RBC Wealth Management, and Raymond James.

Fully disclosed retail brokerage

This refers to all broker-dealer firms that utilize another broker-dealer to clear securities transactions on their behalf (with the exception of some fully disclosed discount and online brokers, which are included in the discount and online brokerage subsegment). Fully disclosed broker-dealers are also referred to as introducing broker-dealer firms. Sample firms in this subsegment include Commonwealth Financial Network, First Allied Securities, and NFP Securities.

Discount and online brokerage

This subsegment refers to broker-dealer firms that cater to self-directed investors by offering low-cost securities execution. The majority of firms in this subsegment engage with clients via a Website. Representative firms in this segment include divisions of Fidelity, Charles Schwab, and TD Ameritrade.

Investor Categorization

Wealth management clients are changing and they are growing in number and in complexity and based on the global financial assets they have been categorized as HNW/UHNW, Mass affluent and Retail customer.  In order to categorize the customer net worth, the assets like cash and deposit, equity and bonds, mutual funds, alternative investments and IRA are considered. Some of the assets like the residential real estate, occupational pension assets and household debt are not considered.




Robo-Advisory vs. Human Advisors

A simple question

If you had a problem or a goal in mind, would you want a series of checkboxes, questions, telephone prompts or a real person to assist you?

The automated response system may include perhaps a dozen issues and solutions, based on algorithms, which are supposed to meet the needs of most people. A real person can give you real-time responses, specific to your questions when asked with accurate answers.

The financial advising industry has been buzzing about the emergence of Robo-advisors for the past few years as web-based advising companies, with the new technology stack most of these companies like the Vanguard’s, Wealth-front,, etc. have either acquired or developed Robo advisory platforms, spending millions of dollars. These online tools attempt to create and manage a client’s portfolio in a fast and inexpensive way.

Why and How Robo-advisors came into existence?

Historically the problem with trying to get an FIA was many have minimum asset requirements of $500,000 or larger and it is not uncommon for FIAs to charge 1-2% annually (or greater via the loaded investment tools like Mutual funds entry and exit loads). That is 1-2% you have to do better than the market just to keep up.

On the other hand, Robo-advisors manage portfolios automatically, with their decisions driven by the algorithm. Most Robo-advisors include portfolio rebalancing. One, Fidelity’s, would do the tax harvesting and reallocation for you for a 0.5 percent annual fee.

How Robo-advisors do it differently.

Let me try an explain using a simple example

When you do to buy a pair of denim, you essentially speak to the sales guy and just mention the waist size and the fit. They give you an option of 5 types of denim from different companies, but the ultimate decision is yours that which one you want to buy; this is similar to human FIA.

On the other hand you entered a showroom and based on your physical appearance and your choice the computer takes out the best denim suitable for you, now that is Robo advisory. The Robo-advisory work on a concept of “One Size that fits most of the people”

The computer is learning every time based on the historical data, assets performance, your choices and choices made by other individuals having similar risk appetite. The Robo-advisor changes the asset allocation automatically, where the investor does not have to make the asset allocation decision every time all that at very low fees compared to the human FIA.

Typically, these types of accounts can be compared with the Managed accounts that FIA manages wherein the FIA gets an amount from the investor, and the FIA takes the decision based on the market dynamics, the investment decision or asset allocation are completely based on the FIA experience. FIA may charge the end customers a standard fee 1-2% and a percentage of net profit of the portfolio.

Does Robo-advisor suit you and your dad both?

Firms like focus on the end-goal and is ideal for individuals who do not care or want to learn about the details of investing. For these people, the 35 basis points (or less) they pay is well worth the fee, and in many cases much better than hiring an FIA — both regarding cost and sound financial advice.

In my case, most of my assets are “Do it yourself” (DIY), but I’ve also been studying this for years and feel comfortable buying insurance, mutual funds and stocks myself. On the other hand, my dad may not log-in to his online account and would call up his FIA and get his insurance done.

Most of the Robo advisory platforms work on the Modern Portfolio Theory and the tools robot-advisors offer aren’t new, however. Traditional financial advisors had the same tools available to them for years and could roll up a personalized asset allocation plan unique to you.

How to choose a Robo-advisor for yourself or family, what things you need to consider?

Below are some of the parameters that you should consider while choosing a Robo-advisor and compare it with the Human FIA

  • Minimum Deposit – Some firms you can start out with nothing and others require sizable amounts to start with.
  • Annual Fees – Be aware of hidden costs, portfolio management fees, mutual fund entry/exit loads, ULIP management fees etc.
  • Asset Allocation – Does it offer all different products like Insurance, Mutual funds, Stocks etc.
  • Account Type Support – 100% automated vs human assisted advice
  • Tax Optimization – Does it give services like Tax-Loss Harvesting, Tax planning, Tax reclaims etc.
  • Retirement planning only or supports other short term goal based planning
  • Managed by you in which they give trading advice, or directly by the firm
  • Manage all your assets or just a portion

Although there are a few obvious perks/benefits of the Robo-advisor model, there are undeniable advantages that human, financial advisors bring to their clients. The Gen X may feel comfortable using an online tool to manage their money; however, when it comes to taking major decisions involving larger some of money and longer commitments they like to ripe for a face-to-face financial advisor.

I think, looking at the current capital markets and availability of Robo/RIA tools, and the individual investors the hybrid approach is would be appropriate where the Robo-advisors take over the day to day portfolio’s asset allocations, and the humans take up when building a new wealth management portfolio for an investor.


Abhinav Gupta

Certified Financial Planner (CFP)

Original Article (PDF)


Driving competitive advantage through Margin System

Future trends of Margin System 

  • By adopting this Integrated Margin Management framework, companies can drive competitive advantage by unlocking significant margin opportunities that were previously hidden, the use of sensitivity analytics and “What if” analysis features for getting the holistic picture of each counterparty positions
  • An understanding of the true drivers of margin and access to richer, more granular information about performance is essential to enable margin-focused decisions. Which includes effective liquidity and collateral position management.
  • Harmonize a diverse set of global operating entities and unlock global capability, scale and synergies to converge and standardize global processes, data and systems.
  • Developing a standard infrastructure to support these segregation requirements globally will be critical given the growth and complexity of segregated accounts.
  • Companies need to embed margin management with effective IM analysis and drill down for speedy dispute resolution.
  • Investment and central/external capacity fully leveraged to execute on margin opportunities and management of margin buffers more effectively
  • Margin initiatives balance cost reduction and value creation to provide a holistic response.

Based on the E&Y, KPMG and PWC research report

Showcasing all the core functional attributes of a Global Agnostic Margin System…



What steps can Business Ops and Technology department take to achieve synergy and agility?

Primarily the Operations and the Technology departments are working in silos in any organization, both operations, and the technology has multiple global stakeholders requesting for product or process enhancement. It’s hard to get a holistic picture of the product issues for a product and the process manager due to the agility involved in the highly complex process and products.

The stakeholders share their strategic product “needs” directly with the technology for example in the case of any regulatory enhancement this would directly come as an enhancement request to the technology dept. Whereas what the business “wants” for example a report, or some data element on a screen comes from the operations user to the technology dept.

Creating efficient process is now a key concern for business managers and technology architects, and there is a need to respond to disruptive innovation in the Financial Services sector. The technology dept. needs to support growth and return to the emerging “customer centric” world versus the traditional “product-centric” view.

What is needed? And how?

  • Cost efficiency via standardization
  • Faster time-to-market through deployment of integrated and tested components
  • Better operational control and Transparency through operations tracking and built-in performance measure
  • Standardization and use of best practices for product development and process optimization. Various departments need to share their best practices and success stories with each other and share the utilities that could help in improving the overall process.

Ops n technology


To have a fully integrated product and process solution there has to be a constant feedback between the ops and the technology department. Both the ops and the product development should use the same tool for incident management to get the holistic picture of the current state of the business, ultimately both the business goals and the technology goals need to tie to each other.

Ops n technology3

Abhinav Gupta

Public Profile –

Twitter @abhi13aug

Collateral Scarcity a growing concern!

With the new regulations, Basel 3, EMIR, Dodd-Frank, etc. all require more and more collateral of high-quality assets, higher haircut margins for some instruments. Increased collateral requirement in the market means greater safety against default to maintain stability. However,   the growing demand for high-quality collateral is creating a destabilizing factor as the intermediaries have limited supply of collateral like the T-bills and Treasury MMF’s creating “Collateral scarcity”, which gets worse in the stressed market conditions leading to margin calls, customer unwinding the positions and a large number of margin disputes.

Collateral scarcity for $700 trillion in OTC derivatives market – TABB Group report

Just like in the yellow monster in the Pac-Man game is how I look at the financial regulator, eating the high-quality collateral in the market, smaller banks, broker/dealers, buy-side firms, etc. are getting lesser in number day by day due to large collateral requirements in the global financial markets.


The regulators around the world have been working very hard to strengthen policies to stabilize the global financial system and prevent future crises like 2008. To do so, there’s an increasing pressure from the regulators to increase the liquidity ratios and the minimum capital requirement for the banks. All the banks have lobbied hard to keep that number down, Basel committee also trying to find the aftermath of increased capital requirement from Basel 2 to Basel 3. A large number of small banks, broker-dealers have either shut down their shops or have been acquired by larger banks making the financial ecosystem a lot more unstable as it gives rise to the concentration risk.

The question of how to exercise regulatory control over the banks and intermediaries which favors larger population.

The overall demand is going to grow faster than the supply of collateral is is expected by increase, few of the reasons are

  • As the number of securities that qualify for collateral is decreasing.
  • Some collateral holders will keep a collateral buffer to mitigate settlement delays, buffers more than specific requirements, reduce collateral supply.
  • With new OTC derivate regulations, higher margin requirement for Non-CCP cleared transactions.

For example, currently Eurozone is facing a stressed market condition, with Greece going down, Spain and Italy following the same lines, German banks beeing leveraged more than 28 times their net capital holdings. The reason is the demand & supply imbalance of Euro-bonds or similar securities, which is leading to increased funding cost, the rise in the price of the collaterals. At the same time, say larger German banks have greater access to these high-quality collaterals creating a zone of inequality in the financial marketplace.

The regulators should closely follow and respond to market developments and need to come up new financial innovations, and accept similar credit rating instruments to be used as collateral.

#Regulations, #BaselIII, #EMIR,#DoddFrank, #CollateralManagement

ECB on Collateral

Financial Market Participants, their objectives and expectations for 2016

There are various financial market participants, and each one of them have different objective and expectations, the expectations would keep changing over a period, I have tried to capture their goals & expectation in a near to mid- term future and what are the major budget drivers for the financial services industry in 2016.


Access to more markets, asset classes and alternative investment products having more liquidity, lower complexity and greater probabilities of gains.


  • Increased speed of dispute resolution and frequency of the transactions, dispute resolution, has consistently remained the top frustration factor for consumers in the past five to ten years, which calls for the need of next generation dispute management and CRM systems.
  • Real-time information visualization systems with integrated alerts with greater transparency
  • Multiple channels of trading activities( a hybrid of the traditional and non-traditional ways) with enhanced customer services


The Broker/Dealers striving for more accurate simulations, prediction of markets for enhanced profits, meeting the latest financial standards and regulations


  • Developing new processes and methods of trading securities in the markets by process and product re-engineering by platform complexity reduction
  • Enhancing the enterprise risk management (ERM) system and creating the risk appetite frameworks (RAF)
  • Creating and using more accurate tools to enhanced risk management, as the whole concept of “Too BIG to FAIL” is not true as more, regulators can & have the liberty of bringing down the big banks

Budget Drivers for 2016

Stock Exchanges & Trading Venues

Stock exchanges and trading venues success depends on transparency and credibility in the dealings of the securities market; that is achieved by disseminating trading information to the largest possible number of dealers and interested parties


  • Lower data latency with higher data accuracy so that the accurate trading information transmitted to broker/dealers and various communication channels like (ECN’s, DMA network)
  • Exchange consolidation is driving the new regulatory architecture

 Data Vendors

With the new regulations like MiFID, BASEL III, FATCA, EMIR, etc. Markets participants are dependent on the data vendors to provide more accurate data.


  • Moving towards collective models (Joint ventures) of data processing, as current market has many big data vendors like Bloomberg, IDC and some niche vendors EXTEL, with no clear market leaders

  • Aggregation of structured and non-structured dynamic reference data like price, corporate actions, counterparties transactions, rating, etc.
  • Data remodelling around various legal entities
  • Significant market drift towards the “as a service” model (InaaS, PaaS) for reference data


They are the watchdogs of the financial markets, who insure’s that markets are safe with low volatility


  •  Creating more standard processes for accounting and reporting across various market segments Intra-day margining for early fraud detection
  • Better surveillance & controls process resulting in automatic alarms in case of any fraud and suspension of trading activities.
  • Longer data retention cycles for auditing and enhanced reporting and accounting standards