Global Retail Banking Needs a Digital Makeover

If you don’t like change, you will like irrelevance even less.” -General Eric Shinseki, Former  US Secretary of Veterans Affairs

This blog has spent time documenting the ongoing digital disruption across the industry especially financial services. Is there proof that creative destruction is taking a hold in Banking? The answer is a clear & unequivocal “Yes”. Clearly, Retail Banking is undergoing a massive makeover. This is being driven by many factors – changing consumer preferences, the advent of technology, automation of business processes & finally competition from not just the traditional players but also the Fintechs. The first casualty of this change is the good old Bank Branch. This post looks at the business background of Retail Banking across the world & will try to explain my view on what is causing this shift in how Banks and consumers perceive financial services.

This blog post will be one of a series of five standalone posts on Retail Bank transformation. The intention for the first post is to discuss industry dynamics, the current state of competition and will briefly introduce the forces causing a change in the status quo. The second post will categorize FinTechs across the banking landscape with key examples of how they disinter-mediate established players. The remaining posts will examine each of the other forces (Customer  in more detail along with specific and granular advice to retail banks on how to incorporate innovation into their existing technology, processes and organizational culture.

Introduction – 

Retail Banking is perhaps one of the most familiar and regular services that everyday citizens use in the course of their lives. Money is a commodity we touch every day in our lives when we bank, shop, pay bills, borrow etc. Retail lines of banking typically include personal accounts, credit cards, mortgages and auto loans. 

For large financial conglomerates that have operations spanning Commercial Banking, Capital Markets, Wealth & Asset Management etc, retail operations have always represented an invaluable source of both stability as well as balance sheet strength. The sheer size & economic exposure of retail operations ensures that it is not only staid yet stable but also somewhat insulated from economic shocks. This is borne out by the policies of respective national central banks & treasury departments. Indeed one of main the reasons regulators have bailed out banks in the past is due to the perception that Main Street & the common citizen’s banking assets becoming a casualty of increased risk taking  by traders in the capital markets divisions. This scenario famously played out during the Great Depression in the late 1920s and was a major factor in causing widespread economic contagion. A stock market crash quickly cascaded into a nation-wide economic depression. 

Thus, retail banking is crucial to not just to the owning corporation but also to diverse stakeholders in the world economy – deposit holders, the regulators led by the US Federal Reserve (in the US) & a host of other actors.  

The State of Global Retail Banking – 

In the financial crisis of 2008, retail banks not only held their own but also assumed a bigger share of revenues as the recovery got underway in the following years. According to a survey by Boston Consulting Group (BCG), retail banking activities accounted for 55 percent of the revenues generated across a global cohort of 140 banks, up from 45 percent in 2006.[1] 

However, the report also contends that retail revenues since 2008 have been slowly falling as investors have begin shifting their savings to deposits as a reaction to high profile financial scandals thus putting pressure on margins. Higher savings rates have helped offset this somewhat & retail banks ended up maintaining better cost to income (CIR) ratios than did other areas of banking.Retail banks also performed better on a key metric return on assets (ROA). The below graphic from the BCG captures this metric. In the Americas region, the average ROA was 162 percent higher than the average group ROA in 2008. From 2001 through 2006, it was 51 percent higher. Global banking revenues stood at $ 1.59 trillion in 2015 – a figure that is expected to hold relatively steady across the globe [2]

It is also important to note that global performance of retail banks across the five major regions: the Americas, Europe, the Middle East, Asia, and Australia has generally varied based on a multitude of factors. And even within regions, banking performance has varied widely.[2]

Retail Banking - BCG

                                      Illustration 1 – Retail Banking is profitable and stable 

As stable as this sector seems, it is also be roiled by four main forces that are causing every major player to rethink their business strategy. Left unaddressed, these changes will cause huge and negative impacts on competitive viability, profitability & also impact all important growth over the next five years. 

What is the proof that retail banking is beginning to change? The below graphic from CNN [1] says it all –


Bank of America has 23% fewer branches and 37% fewer employees than in 2009.  That downward trend across both metrics is expected to continue as online transactions from (deposits to checks to online loans) grown by a staggering 94%. The bank is expected to cut more positions in reflection of a shrinking headcount and branch footprint[1].

Pressure from the FinTechs:

The Financial Services and the Insurance industry are facing an unprecedented amount of change driven by factors like changing client preferences and the emergence of new technology—the Internet, mobility, social media, etc. These changes are immensely profound, especially with the arrival of “FinTech”—technology-driven applications that are upending long-standing business models across all sectors from retail banking to wealth management & capital markets. Further, members of a major new segment, Millennials, increasingly use mobile devices, demand more contextual services and expect a seamless unified banking experience—something akin to what they  experience on web properties like Facebook, Amazon, Uber, Google or Yahoo, etc. They do so by expanding their wallet share of client revenues by offering contextual products tailored to individual client profiles. Their savvy use of segmentation data and predictive analytics enables the delivery of bundles of tailored products across multiple delivery channels (web, mobile, call center banking, point of sale, ATM/kiosk etc.).

Retail Banking must trend Digital to respond – 

The definition of Digital is somewhat nebulous, I would like to define the key areas where it’s impact and capabilities will need to be felt for this gradual transformation to occur.

A true Digital Bank needs to –

  • Offer a seamless customer experience much like the one provided by the likes of Facebook & Amazon i.e highly interactive & intelligent applications that can detect a single customer’s journey across multiple channels
  • offer data driven interactive services and products that can detect customer preferences on the fly, match them with existing history and provide value added services. Services that not only provide a better experience but also foster a longer term customer relationship
  • to be able to help the business prototype, test, refine and rapidly develop new business capabilities
  • Above all, treat Digital as a Constant Capability and not as an ‘off the shelf’ product or a one off way of doing things

The five areas that established banks need to change across are depicted below..


  1. Convert branches to be advisory & relationship focused instead of centers for transactions – As the number of millennials keeps growing, the actual traffic to branches will only continue to decline.  Branches still have an area of strength in being intimate customer touch points. The branch of the future can be redesigned to have more self service features along with relationship focused advisory personnel instead of purely being staffed by tellers and managers. They need to be reimagined as Digital Centers, not unlike an Apple store, with highly interactive touch screens and personnel focused on building business through high margin products.
  2. Adopt a FinTech like mindset – FinTechs (or new Age financial industry startups) offer enhanced customer experiences built on product innovation and agile business models. They do so by expanding their wallet share of client revenues by offering contextual products tailored to individual client profiles. Their savvy use of segmentation data and predictive analytics enables the delivery of bundles of tailored products across multiple delivery channels (web, mobile, Point Of Sale, Internet, etc.). Like banks, these technologies support multiple modes of payments at scale, but they aren’t bound by the same regulatory and compliance regulations as are banks, who operate under a mandate that they must demonstrate that they understand their risk profiles. The best retail banks will not only seek to learn from, but sometimes partner with, emerging fintech players to integrate new digital solutions and deliver exceptional customer experience. To cooperate and take advantage of fintechs, banks will require new partnering capabilities. To heighten their understanding of customers’ needs and to deliver products and services that customers truly value, banks will need new capabilities in data management and analytics.
  3. Understand your customer – Banks need to move to a predominantly online model, providing consumers with highly interactive, engaging and contextual experiences that span multiple channels—branch banking, eBanking, POS, ATM, etc. Further goals are increased profitability per customer for both micro and macro customer populations with the ultimate goal of increasing customer lifetime value (CLV).
  4. Business Process improvement – Drive Automation across lines of business  – Financial services are fertile ground for business process automation, since most banks across their various lines of business are simply a collection of core and differentiated processes. Examples of these processes are consumer banking (with processes including on boarding customers, collecting deposits, conducting business via multiple channels, and compliance with regulatory mandates such as KYC and AML); investment banking (including straight-through-processing, trading platforms, prime brokerage, and compliance with regulation); payment services; and wealth management (including modeling model portfolio positions and providing complete transparency across the end-to-end life cycle). The key takeaway is that driving automation can result not just in better business visibility and accountability on behalf of various actors. It can also drive revenue and contribute significantly to the bottom line. Automation enables enterprise business and IT users to document, simulate, manage, automate and monitor business processes and policies. It is designed to empower business and IT users to collaborate more effectively, so business applications can be changed more easily and quickly.
  5. Agile Culture – All of the above are only possible if the entire organization operates on an agile basis in order to collaborate across the value chain. Cross functional teams across new product development, customer acquisition & retention, IT Ops, legal & compliance must collaborate in short work cycles to close the traditional business & IT innovation gap.  One of DevOps’s chief goals is to close the long-standing gap between the engineers who develop and test IT capability and the organizations that are responsible for deploying and maintaining IT operations. Using traditional app dev methodologies, it can take months to design, test and deploy software. No business today has that much time—especially in the age of IT consumerization and end users accustomed to smart phone apps that are updated daily. The focus now is on rapidly developing business applications to stay ahead of competitors that can better harness Big Data’s amazing business capabilities.

How can all of this be quantified? –

The results of BCG’s sixth annual Global Retail-Banking Excellence benchmarking illustrate the value drivers. Forward looking banks are working on some of the above aspects are able to reduce cycle times for core processes thus improving productivity. The leaders in the survey are also reallocating resources from the mid and office to customer facing roles.[3]

Again, according to the BCG, digital reinvention comes with huge benefits to both the top and bottom-lines. Their annual survey across the global retail banking sector estimates an average reduction in operating expenses from 15% to 25%, increases in pretax profit by 20% to 30% and an average increase in margins before tax from 5% to 10%. [3] These numbers are highly impressive at the scale that large banks operate.

The question thus is, can the vast majority of Banks change before it’s too late? Can they find the right model of execution in the Digital Age before their roles are either diminished or dis-intermediated by competition?

We will dive deep into the FinTech’s in the next post in the series.


[1] CNN Money – Bank of America has 23% fewer branches than 2009

[2]BCG Research- Winning Strategies Revisited for Retail Banking

[3] BCG Research- Global Capital Markets 2016: The Value Migration

Capital Markets Pivots to Big Data in 2016

Previous posts in this blog have discussed how Capital markets firms must create new business models and offer superior client relationships based on their vast data assets. Firms that can infuse a data driven culture in both existing & new areas of operation will enjoy superior returns and raise the bar for the rest of the industry in 2016 & beyond. 

Capital Markets are the face of the financial industry to the general public and generate a large percent of the GDP for the world economy. Despite all the negative press they have garnered since the financial crisis of 2008, capital markets perform an important social function in that they contribute heavily to economic growth and are the primary vehicle for household savings. Firms in this space allow corporations to raise capital using the underwriting process. However, it is not just corporations that benefit from such money raising activity – municipal, local and national governments do the same as well. Just that the overall mechanism differs – while business enterprises issue both equity and bonds, governments typically issue bonds. According to the Boston Consulting Group (BCG), the industry will grow to annual revenues of $661 billion in 2016 from $593 billion in 2015 – a healthy 12% increase. On the buy side, the asset base (AuM – Assets under Management) is expected to reach around $100 trillion by 2020 up from $74 trillion in 2014.[1]

Within large banks, the Capital Markets group and the Investment Banking Group perform very different functions.  Capital Markets (CM) is the face of the bank to the street from a trading perspective.  The CM group engineers custom derivative trades that hedge exposure for their clients (typically Hedge Funds, Mutual Funds, Corporations, Governments and high net worth individuals and Trusts) as well as for their own treasury group.  They may also do proprietary trading on the banks behalf for a profit – although it is this type of trading that Volcker Rule is seeking to eliminate.

If a Bank uses dark liquidity pools (DLP) they funnel their Brokerage trades through the CM group to avoid the fees associated with executing an exchange trade on the street.  Such activities can also be used to hide exchange based trading activity from the Street.  In the past, Banks used to make their substantial revenues by profiting from their proprietary trading or by collecting fees for executing trades on behalf of their treasury group or other clients.

Banking and within it, capital markets continues to generate insane amounts of data. These producers range from news providers to electronic trading participants to stock exchanges which are increasingly looking to monetize data. And it is not just the banks, regulatory authorities like the FINRA in the US are processing peak volumes of 40-75 billion market events a day [2]. In addition to data volumes, Capital Markets has always  possessed a variety challenge as well. They have tons of structured data around traditional banking data, market data, reference data & other economic data. You can then factor in semi-structured data around corporate filings,news,retailer data & other gauges of economic activity. An additional challenge now is the creation of data from social media, multimedia etc – firms are presented with significant technology challenges and business opportunities.

Within larger financial supermarkets, the capital markets group typically leads the way in  being forward looking in terms of adopting cutting edge technology and high tech spends.  Most of the compute intensive problems are generated out of either this group or the enterprise risk group. These groups own the exchange facing order management systems, the trade booking systems, the pricing libraries for the products the bank trades as well as the tactical systems that are used to manage their market and credit risks, customer profitability, compliance and collateral systems.  They typically hold about one quarter of a Banks total IT budget. Capital Markets thus has the largest number of use cases for risk and compliance.

Players across value chain on the buy side, the sell side, the intermediaries (stock exchanges & the custodians) & technology firms such as market data providers are all increasingly looking at leveraging these new data sets that can help unlock the value of data for business purposes beyond operational efficiency.

So what are the  different categories of applications that are clearly leveraging Big Data in production deployments.


                      Illustration – How are Capital Markets leveraging Big Data In 2016

I have catalogued the major ones below based on my work with the majors in the spectrum over the last year.

  1. Client Profitability Analysis or Customer 360 view:  With the passing of the Volcker Rule, the large firms are now moving over to a model based on flow based trading rather than relying on prop trading. Thus it is critical for capital market firms to better understand their clients (be they institutional or otherwise) from a 360-degree perspective so they can be marketed to as a single entity across different channels—a key to optimizing profits with cross selling in an increasingly competitive landscape. The 360 view encompasses defensive areas like Risk & Compliance but also the ability to get a single view of profitability by customer across all of their trading desks, the Investment Bank and Commercial Lending.
  2. Regulatory Reporting –  Dodd Frank/Volcker Rule Reporting: Banks have begun to leverage data lakes to capture every trade intraday and end of day across it’s lifecycle. They are then validating that no proprietary trading is occurring on on the banks behalf.  
  3. CCAR & DFast Reporting: Big Data can substantially improve the quality of  raw data collected across multiple silos. This improves the understanding of a Bank’s stress test numbers.
  4. Timely and accurate risk management: Running Historical, stat VaR (Value at Risk) or both to run the business and to compare with the enterprise risk VaR numbers.
  5. Timely and accurate liquidity management:  Look at the tiered collateral and their liquidity profiles on an intraday basis to manage the unit’s liquidity.  They also need to look at credit and market stress scenarios and be able to look at the liquidity impact of those scenarios.
  6. Timely and accurate intraday Credit Risk Management:  Understanding when  & if  deal breaches a tenor bucketed limit before they book it.  For FX trading this means that you have about 9 milliseconds  to determine if you can do the trade.  This is a great place to use in memory technology like Spark/Storm and a Hadoop based platform. These usecases are key in increasing the capital that can be invested in the business.  To do this they need to convince upper management that they are managing their risks very tightly.
  7. Timely and accurate intraday Market Risk Management:  Leveraging Big Data to market risk computations ensures that Banks have a real time idea of any market limit breaches for any of the tenor bucketed market limits.
  8. Reducing Market Data costs: Market Data providers like Bloomberg, Thomson Reuters and other smaller agencies typically charge a fee each time data is accessed.  With a large firm, both the front office and Risk access this data on an ad-hoc fairly uncontrolled basis. A popular way to save on cost is to  negotiate the rights to access the data once and read it many times.  The key is that you need a place to put it & that is the Data Lake.
  9. Trade Strategy Development & Backtesting: Big Data is being leveraged to constantly backtest trading strategies and algorithms on large volumes of historical and real time data. The ability to scale up computations as well as to incorporate real time streams is key to
  10. Sentiment Based Trading: Today, large scale trading groups and desks within them have begun monitoring economic, political news and social media data to identify arbitrage opportunities. For instance, looking for correlations between news in the middle east and using that to gauge the price of crude oil in the futures space.  Another example is using weather patterns to gauge demand for electricity in specific regional & local markets with a view to commodities trading. The realtime nature of these sources is information gold. Big Data provides the ability to bring all these sources into one central location and use the gleaned intelligence to drive various downstream activities in trading & private banking.
  11. Market & Trade Surveillance:Surveillance is an umbrella term that usually refers to a wide array of trading practices that serve to distort securities prices thus enabling market manipulators to illicitly profit at the expense of other participants, by creating information asymmetry. Market surveillance is generally out by Exchanges and Self Regulating Organizations (SRO) in the US – all of which have dedicated surveillance departments set up for this purpose. However, capital markets players on the buy and sell side also need to conduct extensive trade surveillance to report up internally. Pursuant to this goal, the exchanges & the SRO’s monitor transaction data including orders and executed trades & perform deep analysis to look for any kind of abuse and fraud.
  12. Buy Side (e.g. Wealth Management) – A huge list of usecases I have catalogued here – 
  13. AML Compliance –  Covered in various blogs and webinars. – 

The Final Word

A few tactical recommendations to industry CIOs:

  • Firstly, capital markets players should look to create centralized trade repositories for Operations, Traders and Risk Management.  This would allow consolidation of systems and a reduction in costs by providing a single platform to replace operations systems, compliance systems and desk centric risk systems.  This would eliminate numerous redundant data & application silos, simplify operations, reduce redundant quant work, improve and understanding of risk.
  • Secondly, it is important to put in place a model to create sources of funding for discretionary projects that can leverage Big Data.
  • Third, Capital Markets groups typically have to fund their portion of AML, Dodd Frank, Volcker Rule, Trade Compliance, Enterprise Market Risk and Traded Credit Risk projects.  These are all mandatory spends.  After this they typically get to tackle discretionary business projects. Eg- fund their liquidity risk, trade booking and tactical risk initiatives.  These defensive efforts always get the short end of the stick and are not to be neglected while planning out new initiatives.
  • Finally, an area in which a lot of current players are lacking is the ability to associate clients using a Lightweight Entity Identifier (LEI). Using a Big Data platform to assign logical and physical entity ID’s to every human and business the bank interacts can have salubrious benefits. Big Data can ensure that firms can do this without having to redo all of their customer on-boarding systems. This is key to achieving customer 360 views, AML and FATCA compliance as well as accurate credit risk reporting.

It is no longer enough for CIOs in this space to think of tactical Big Data projects, they must be thinking around creating platforms and ecosystems around those platforms to be able to do a variety of pathbreaking activities that generate a much higher rate of return.


[1] “The State of Capital Markets in 2016” – BCG Perspectives

Big Data Driven Disruption – The Robo-Advisor..(1/3)

Wealth Management is the highest growth businesses for any medium to large financial institution. It also is the highest customer touch segment of banking and is fostered on long term (read extremely lucrative advisory) relationships. This three part series explores the automated “Robo-advisor” movement in the first post. We will cover the business background and some definitions . The second post will focus on the overall business model & main functions of a Robo-advisor. The final post will look at a technology & architectural approach to building out a Robo-advisor. We will also discuss best practices from a WM & industry standpoint in the context of Robo-advisors.


(Image Credit – Forbes)

The term ‘Wealth Management‘ broadly refers to an aggregation of financial services that are typically bespoke and offered to highly affluent clients.  These include financial advisory,  personal investment management, financial advisory, and planning disciplines directly for the benefit of high-net-worth (HNWI) clients.  This term can refer to a wide range of possible functions and business models.

A wealth manager is a specialized financial advisor who helps a client construct an entire investment portfolio and advises on how to prepare for present and future financial needs. The investment portion of wealth management normally entails both asset allocation of a whole portfolio as well as the selection of individual investments. The planning function of wealth management often incorporates tax planning around the investment portfolio as well as estate planning for individuals as well as family estates.

The ability to sign up wealthy individuals & families; then retaining them over the years by offer those engaging, bespoke & contextual services will largely provide growth in the Wealth Management industry in 2016 and beyond.

However,  WM as an industry sector has lagged other areas within banking from a technology & digitization standpoint. Multiple business forces ranging from increased regulatory & compliance demands, digital demands & expectations from younger, technology savvy customers and new Age FinTechs have led to firms slowly begin a makeover process. Let us examine these trends in more detail. 

Business Trends Driving the need for Robo/Automated Investment Advisors –

These trends  are a combination of industry reality as well as changing preferences on behalf of the HNWI clientele –

  1. Growth in the Wealth Management business largely depends on the ability to sign up new clients. Previously WM shops would not be interested in signinup up clients with less than a certain value of investable assets (typical threshold being $ 1 million). However the need to on-ramp these folks onto a long term relationship means being able to offer lower cost automated business models that better fit their mindsets
  2. The mentality of younger clientele has also evolved over the years. These clients are technologically savvy, they largely have a DIY (Do It Yourself) mindset and their digital needs are largely being missed by the wealth management community. This rising segment demands digital services that are highly automated & 24/7 in nature without needing to pay the premium charged by a human advisor
  3. Regulatory, cost pressures are rising which are leading to commodification of services
  4. Innovative automation and usage techniques of data assets among new entrants aka the FinTechs are leading to the rise of automated advisory services thus challenging incumbent firms. At traditional brokerage firms like  Morgan Stanley, Bank of America Corp. and Wells Fargo & Co. about 46,000 human advisers were employed as of 2016. The challenge for these incumbent firms will be to develop such automated investing tools as well as offer more self-service channels for customers [2]
  5. A need to offer aggregated & holistic financial services tailored to the behavioral needs of the HNWI investors on an individual basis

So where is the biggest trend in this disruption? It is undoubtedly, the Robo-advisor.

Introducing the Automated Advisor (affectionately called the Robo-advisor) –

FinTechs led by Wealthfront and Betterment have pioneered the somewhat revolutionary concept of Robo-advisors. To define the term – a Robo-advisor is an algorithm based automated investment advisor that can provide a range of Wealth Management services described below. The Robo-advisor can be optionally augmented & supervised by a human adviser. At the moment, owing to the popularity of Robo-advisors among the younger high networth investors (HNWI), a range of established players like Vanguard, Charles Schwab as well as a number of FinTech start-ups have developed these automated online investment tools or have acquired FinTech’s in this space.e.g Blackrock. The Robo-advisor is built using digital techniques – such as data science & Big Data – as we will explore in the next post.

What service models can Robo-advisors satisfy –

Full service Wealth Management firms broadly provide services in the following core areas which Robo-advisors can slowly begin supplementing –

  1. Investment Advisory – Helping a client construct an investment portfolio that helps her/him prepare for life changes based on their respective risk appetites & time horizons. The financial instruments invested in range from the mundane – equities, bonds etc to the arcane – hedging derivatives etc
  2. Retirement Planning – Retirement planning is a obvious function of a client’s personal financial journey & one that lends itself to automation. From a HNWI standpoint, there is a need to provide complex retirement services while balancing taxes, income needs & estate prevention etc. Robo-advisors are able to bring in market trends and movements of securities to ensure that client’s retirement holdings are not  skewed toward particular sectors of the marke.
  3. Estate Planning Services – A key function of wealth management is to help clients pass on their assets via inheritance. The Robo-advisor can assist a human wealth managers helps construct wills that leverage trusts and suggest suitable forms of insurance etc to help facilitate a smooth process of estate planning
  4. Tax Planning – Robo-advisors can help clients manage their wealth in such a manner that tax impacts are reduced from a taxation (e.g IRS in the US) perspective. As the pools of wealth increase, even small rates of taxation can have a magnified impact either way. The ability to achieve the right mix of investments from a tax perspective is a key capability and one that can be automated to a high degree
  5. Insurance Management – A Robo-advisor can help suggest and manage  the kinds of insurance purchased by their HNWI clients so that the appropriate hedging services could be put in place based on the client’s specific investment mix & exposures
  6. Institutional Investments– Institutional Robo-advisors can provide investment services to investors like pension funds, hedge funds etc while automating them a variety of backoffice functions

Currently most Robo-advisors limit themselves to providing the first function only i.e portfolio management (i.e. allocating investments among asset classes) without addressing issues such as estate and retirement planning and cash-flow management, which are also the domain of financial planning.[1]

Expect this to change as the technology rapidly matures in the years to come with advances in cognitive computing that will enable . At one of the earliest Robo-advisors, Betterment,  as of early 2016 – more than half of their $3.3 billion of assets under management comes from people with more than $100,000 at the firm. Another early starter, Wealthfront estimated more than a third of its almost $3 billion in assets in accounts requiring at least $100,000. Schwab, one of the first established investment firms to produce an automated product, attracted $5.3 billion to its offering in its first nine months.[2]


Robo-advisory business models

Currently there are a few different business models that are being adopted by firms.

  1. Full service online Robo-advisor that is a 100% automated without any human element
  2. Hybrid Robo-advisor model being pioneered by firms like Vanguard & Charles Schwab
  3. Pure online advisor that is primarily human in nature

Conclusion –

As one can see clearly, automated investing methods are still in early stages of maturity. However, they are unmistakably the next big trend in the WM industry and one that players should begin developing capabilities around. According to AT.Kearney, by 2020, Roboadvisors will manage around $2.2 trillion in global HNWI assets.[2]

The next post in this three part series will focus on the pivotal role of Big Data in creating a Robo-advisor. We will discuss system requirements & propose a reference architecture. 


  1. Wikipedia –
  2. Bloomberg – “The Rich are already using Roboadvisors and that scares the banks..”

Across Industries, Big Data Is Now the Engine of Digital Innovation..

The data fabric is the next middleware.” –Todd Papaioannou, CTO at Splunk

Enterprises across the globe are confronting the need to create a Digital Strategy. While the term itself may seen intimidating to some, it essentially represents  an agile culture built on customer centricity & responsiveness. The only way to attain Digital success is to understand your customers at a micro level while making strategic decisions on your offerings to the market. Big Data has become the catalyst in this massive disruption as it can help business in any vertical solve their need to understand their customers better. It aids this by providing foundational  platform for amazing products.

We have seen how how exploding data generation across the global has become a clear & present business & IT phenomenon. Data volumes are rapidly expanding across industries. However, while the production of data itself that has increased but it is also driving the need for organizations to derive business value from it. This calls for the collection & curation of data from dynamic,  and highly distributed sources such as consumer transactions, B2B interactions, machines such as ATM’s & geo location devices, click streams, social media feeds, server & application log files and multimedia content such as videos etc. It needs to be noted that data volumes here consist of multi-varied formats, differing schemas, transport protocols and velocities.

Internet of Things (IoT) has become an entire phenomenon to itself. It is truly a horizontal vertical (no pun intended) as the proliferation of applications of sensors is causing rapid change in system & application architectures. The system of IoT is burgeoning from the initial sensors, digital devices, mechanical automatons to cars, process monitoring systems, browsers, television, traffic cameras etc etc.

Big Data is thus crossing the innovation chasm. A vast majority of early adopter projects are finding business success with a strong gain in ROI (Return On Investment). The skills gap is beginning to slowly decrease with Hadoop ecosystem becoming a skill that every modern application developer needs to have. Increasingly customers are leading the way by deploying Big Data in new and previously uncharted areas like cybersecurity leading to massive cross vertical interest.


The five elements in Digital Transformation, irrespective of the business vertical you operate in, are –

  1. Customer Centricity
  2. Realtime multichannel analytics
  3. Operational improvements – Risk, Fraud & Compliance
  4. Ability of the business to visualize data
  5. Marketing & Campaign optimization

The first element in Digital is the Customer centricity.

Big Data drives this in myriad ways  –  

  1. Obtaining a realtime Single View of an entity (typically a customer across multiple channels, product silos & geographies)
  2. Customer Segmentation by helping businesses understand their customers down to the individual level as well as at a segment level
  3. Customer sentiment analysis by combining internal organizational data, clickstream data, sentiment analysis with structured sales history to provide a clear view into consumer behavior.
  4. Product Recommendation engines which provide compelling personal product recommendations by mining realtime consumer sentiment, product affinity information with historical data.
  5. Market Basket Analysis, observing consumer purchase history and enriching this data with social media, web activity, and community sentiment regarding past purchase and future buying trends.

Realtime Multichannel Analytics is the second piece of a Digital Strategy.

Mobile applications first begun forcing the need for enterprise to begin supporting multiple channels of interaction with their consumers. For example Banking now requires an ability to engage consumers in a seamless experience across an average of four to five channels – Mobile, eBanking, Call Center, Kiosk etc. The healthcare industry stores patient data across multiple silos – ADT (Admit Discharge Transfer) systems, medication systems, CRM systems etc but all of this must be exposed across different mediums of access. Data Lakes provide an ability to visualize all of the patients data in one place thus improving outcomes. Every customer facing application needs to be both multi-channel as well as one that supports  a unified 360 degree customer view across all these engagement points. Applications developed in 2016 and beyond must take a 360 degree based approach to ensuring a continuous client experience across the spectrum of endpoints and the platforms that span them from a Data Visualization standpoint. Every serious business needs to provide a unified view of a customer across tens of product lines and geographies. Big Data not only provides the core foundational elements for a realtime view of the moving parts of the business but also enables businesses to listen to their customers.

A strategic approach to improving Risk, Fraud & Compliance analytics  can add massive value and competitive differentiation in three distinct categories as shown below.

  1. Exponentially improve existing business processes. e.. Risk data aggregation and measurement, HIPAA/SOX/Manufacturing compliance, fraud detection
  2. Help create new business models and go to market strategies – by monetizing multiple data sources – both internal and external
  3. Vastly improve regulatory compliance by generating fresher and more accurate insights across silos of proprietary data

The onset of Digital Architectures in enterprise businesses implies the ability to drive continuous online interactions with global consumers/customers/clients or patients. The goal is not just provide engaging Visualization but also to personalize services clients care about across multiple modes of interaction. Mobile applications first begun forcing the need for enterprise to begin supporting multiple channels of interaction with their consumers. For example Banking now requires an ability to engage consumers in a seamless experience across an average of four to five channels – Mobile, eBanking, Call Center, Kiosk etc. Healthcare is a close second where caregivers expect patient, medication & disease data at their fingertips with a few finger swipes on an iPad app.

The ability of outbound Marketing campaigns to reach engaged customers in a proactive manner using the right channel has been a big gap in their effectiveness. The old school strategy of blasting out direct mailers and emails does not work anymore both from a cost as well as a customer engagement standpoint. Nowadays, campaigns for exciting new products & promotions need to be built on the rich customer intelligence assets that Big Data enables you to build. Examples of these capabilities are replete in sectors like Retail where offering a positive purchase experience in terms of personalized offers, price comparisons, social network based sharing of experiences et al drive higher customer engagement & loyalty.

The Final Word

My goal for this post was to communicate a business revelation that I have had in past year. While the semantics of business processes, the usecases & the data sources, elements, formats may vary from industry to industry ( e.g. Banking to Healthcare to Manufacturing to Telecom) – the approaches as well as the benefits from leveraging a data & analytics driven business model essentially remain the same. These capabilities are beginning to separate the winners from the rest of the pack.