Enhancing Compliance & Oprisk Management through Analytics

Post the financial crisis, banks in the US have faced increased regulatory scrutiny that has resulted in broader and tougher regulations. Bankers are fully aware of the investments and efforts they have to put in to comply with these regulations. Consequently, compliance function in banks is evolving towards a broader risk canvas that is now seeking tighter coordination between the first and second lines of defense. This poses new challenges to banks – from being compliant to getting the optimal returns from their investments.  The million dollar question on everybody’s minds is  - How are banks rising up to this challenge?

Recent studies have highlighted the enormity of the challenge this has created for banks. For example one study by Accenture shows that 92% of banks will be compelled to increase their compliance spend in 2014. In another report by Continuity Control, the new regulations have imposed an additional financial burden for just the last quarter (Q4) of 2014 is $241 million.

Enhanced regulatory scrutiny may be a necessary evil to watch over the much-maligned banking sector, but has spawned its own unintended consequences.  The huge anxiety of banks to be compliant and avoid penalties and the resulting hike in compliance spend has and will continue to impact ROE and profitability of US banks for years to come.

How are banks responding? A whole ecosystem of changes is taking place in this area.  Banks are deploying analytics to help them meet the challenge and enable them to make the right data driven decisions. Three important changes are on their way.

 First, bulk of the new spend has gone towards upgrading technology platforms. Banks are integrating extant analytical and compliance platforms so they can deploy data mining and analytics to get the right insights.  For example, analytical models are being deployed to proactively identify and monitor UDAAP compliance in customer engagements / acquisition.

Second, Banks are bringing new structural alignment between first and second lines of defense.  Compliance is now a broad based enterprise activity that will report to the Board or CEO and will include operational and business risk professionals. This is a significant change because in my view, it facilitates wider & deeper use of analytics to help banks stay compliant and out of regulatory trouble.

Third, data silos – the usual suspects - are posing roadblocks for banks in their new quest to be compliant. Incorporating structured and unstructured data for analytics is also an urgent initiative at banks. Banks are aware of these challenges - these are known devils anyway for some time now; but a renewed urgency backed by fat budget approvals is evident.

Banks need to keep a watchful eye on the expanding compliance management function. Technology upgrade and structural changes, while necessary, are only part of the solution and not a panacea by themselves. Banks need to look at compliance as an enterprise wide culture that every associate lives by 24/7. In an era where changes are swift, where disruptive innovations are continuous and almost a way of life, the best insurance for the banks is an open mind to change and adapt to win the customers’ heart. In a way, it is the same old wine, but in a new fancy carboy.

2020 – US Banks are betting big on Analytics

A recent study by Accenture talks about the future state of banking in US by 2020. Thankfully, the study reports, US banks have emerged from the travails of a battered economy. Two important findings from the study stand out.

1.       Banks face increased competition in coming years

2.       Emergence of a core group of full service banks that will be the backbone of US Banking system.

While we can debate the findings, the current activity stream at banks does indicate that there may be truth to this and that we may be already seeing the contours of US Banks by 2020.

Interactions with bank executives have definitely made one thing clear. There is immense buzz around this future landscape and almost every major bank has already undertaken or is seeking an internal assessment to review their preparedness for change. Branch banking is one area that is likely to see intense competition; many of the big players are already investing in redesigning the branch of the future;

The other 800 pound gorilla in the room is of course Analytics. Banks are very keen to step up their capabilities - technical as well as talent pool and are building structures similar to Center of Excellence for Analytics. COE for Analytics appears to be the widely accepted route to instill an analytics driven decision culture.  Backed by a war chest and executive / board mandates, massive efforts are on to upgrade their capabilities. Truth be told, many bank have discovered that they are woefully under-prepared.

Many banks are even toying with rebuilding their existing data-warehouse to incorporate a fuller and deeper digital understanding of their customers – euphemistically referred to as the 360^o view.

New regulatory standards like Basel III, Comprehensive Capital Analysis and Review (CCAR) and Dodd-Frank Act Stress Test (DFAST), Fraud detection, Anti-Money Laundering (AML), Know Your Customer (KYC) etc. have spawned their own set of internal reviews and investments. However, Analytical capability improvement is at the heart of all these initiatives.

Upgrading analytical tools and platforms is also top on the shopping list. Focus appears to be on investing in emerging technology – e.g automation of predictive analytics modeling, real time offer engines for customer acquisition, transaction (big) data analytics, real time personalization of customer experience etc. Many banks are building Center of Excellence (COE) for analytics.

Internal competitive pressure on executives is intense at banks; many executives are building their own analytics back office groups to have an edge over their peers. This could be counter-productive by building redundancy and generate dueling analytical capabilities and decreased sharing and openness. This is not a healthy development in the long run.

Some Banks are adopting a short term perspective in preparing for the 2020 scenario. For example some banks are recruiting Data Scientists who they think will solve all their quests for insights. However, they do not have a plan to resolve bottlenecks in data flow - all the way from the data-store to the analytical layer. In other words absent the required analytical data infrastructure, their plans are a non-starter and investments wasted. 

This brings us to another dimension to the catch up scenario.  The analytics maturity or preparedness for using analytics varies vastly in banks. Size and deep pockets have not necessarily translated into competitive advantage.  Banks that have sound data infrastructure and a clear 360^o view of their customers – in other words one vision of truth across the enterprise - have a head start and will maintain their tremendous advantage and will end up being the winners. These banks will benefit by deploying latest technologies and analytical platforms and guide business decisions as never before. They will emerge leaders of the pack. As for the rest, they have to do a lot of clean up and then catch up. 

While banks are in a hurry to catch up and not miss the bus, they need external help for a successful transformation. They would need expert advice so that they do not have the re-invent the wheel. They need external help to carve a broader picture and pick the best practices or solution set that will be most appropriate for their bank.

Most US Banks – small and big are on board this transformational journey. These initiatives involve great investments and outcomes are keenly tracked. Many careers are at stake. But those that succeed will form the backbone of US Banks 2020. This obviously will result in intense competition and change the banking landscape in the US forever.

As scores of banks embark on this exciting journey, the IT majors are closely watching the opportunities that this is creating.  Unfortunately, the fact is that it does not automatically translate into revenues for them. Many are still clueless on how to cash in. They have to do their homework and come up with crystal clear vision to help banks in this challenge.

Analytics Revolution - Why the struggle for growth?

IT majors have been excited about the convergence of Social, Cloud, Analytics and Mobility (SCAM).  It is widely believed that these will be the engines of growth in the future. Rightly so. N Chandrasekaran, CEO of TCS, India’s largest technology services provider, recently referred to the SCAM as "digital forces" and estimates that these digital forces would be a $3-5 billion opportunity in the next few years.  A Gartner study has reported that the SCAM market will be worth $107 billion by 2017.

It is true that Analytics - has generated excitement all around. Everyone can see and experience the impact that this convergence – that engenders Disruptive Innovations  - has on everyone’s life. I personally think that the hype is real and the huge revenue opportunity projected for this market space is based on solid grounds.

What the TCS Chief has not mentioned is that there are significant white spaces – industry-speak for critical gaps and blind spots in the effort to get this revenue. And the fumble, too, is very real. For example, if these projections and forecasts can be translated into revenue, why are we not seeing a Google or a Facebook or even their dwarfs in pure play Analytics?  There appear to be several reasons why IT majors have not been able to take advantage of the opportunities. The revenue is for them to lose unless they learn and take corrective action quickly.

Analytics business is a domain specific, hands-on and a devilish details game where domain expertise is all supreme. However, most global players have not been able to get the right folks to lead the practice. This has proved to be a disastrous non-starter. The problem is also compounded by lack of right skills in the marketplace. The analytics practices at the majors continue to be led by professionals who either have consulting or technology background but weak in hands-on analytics. This has blissfully insulated the practice from the analytic humdrum that businesses are experiencing. This is also reflected in the inability to identify or devise the right vehicle to exploit the surging analytic opportunities. In my view, the lack of appropriate leadership is a major roadblock to growth.

The IT majors also urgently need to revisit the internal business structure. The bunching of analytics catering to different industry segments or verticals under a single business unit may be convenient for administrative and bureaucratic reasons, but has not produced optimal results. This agglutination has come in the way of insight dominance since successful thought leadership in one vertical often has not passed muster at another. I think the analytics practice catering to each industry vertical must be a separate business unit by itself.

The outsourcing industry has mastered the art of building the business via the IT organizations of client companies. However this tested path has not helped build the Analytics business because the key players are not on the IT organization of clients. Outsources need to have a game plan for directly engaging the business side of the house.

Further the majors they are selling software products and tools that are often peripheral and non-core to generating analytical insights. Aided by an expanded definition of analytics, this may help generate revenue in the short run, but this has taken the focus off the insights business.  For example, a hypothetical solution that can build and deliver fraud detection models using large attribute set – including social media attributes – and look-up more than 10,000 datasets and yet instantly deliver accurate detections will be immensely popular.

Big data or new modeling techniques by themselves would not produce a disruptive innovation. The marriage of cutting edge technology and the resulting new innovative analytical techniques that can scale is the winning recipe. This is a keystone for success in analytics practice, yet conspicuous by its absence.

This success recipe has to be combined with a smart go to market strategy. I call it winning-with-a-thousand-cuts strategy. Instead of waiting for the dream multi-million, multi-year project, the focus must shift to building volumes through a huge portfolio of mid-sized projects. Execute several small to medium sized projects that will provide insights to the businesses in short to medium term - 6 to 12 month time frame. This paradigm has the potential for depth - to open up opportunities in every line of business, business unit or team level at clients and hence build scale in the analytics business.

Risk Intelligence from Social Networking – New Dimensions in understanding Consumer behavior

 

I came across a recent news item in Orlando Sentinel about how a Florida judge stopped a debt collections agency from pursuing an auto loan customer who was late on payments. Apparently the collections agency tracked the customer’s Facebook account and contacted her as well as sent messages to all her friends on Facebook. The judge’s ruling in the case may have highlighted privacy concerns, but what is of interest is that social network intelligence are opening up new and creative ways of understanding and managing consumer behavior.

Now, if debt collections agencies can use social networks in recovering payments - well at least until the Florida judge’s ruling – it would be of interest to know how these networks can be used in larger credit risk management initiatives by banks and financial institutions. While the regulatory / compliance / privacy angle is evolving, there is no doubt that social network intelligence provides powerful yet undiscovered dimensions to our understanding of consumer behavior.

Banks and financial services companies have always sought to better understand their customers. Their primary resource has been the credit bureaus (Experian, TransUnion, Equifax) who have a comprehensive record of credit activity of consumers.  From marketing to risk management, the credit bureaus continue to be the single go-to place for insights on customer’s past and also predicting future behavior.  Using their vast store of data, the credit bureaus created credit scores that were designed to predict the probability of a credit outcome such as default on a loan. 

Conventional risk analytical paradigms overlay credit bureau data on set of existing customers whose performance (example payment behavior) is fully tracked internally.  This would provide insights on external behavior - such as payments as agreed to other lenders or shopping for more credit - that may be useful. Matching this insight with internal data would provide risk intelligence that would be used in granting or denying additional loans / lines to the existing customers.

But what type of risk intelligence can be generated using social network intelligence?  While this is unchartered territory, definitely we can identify social network behavior of customers whose payment behavior we already know. We can use the same analytical paradigm of taking a set of existing customers and match it with social networking data – travels (location), payment transactions to glean insights. For example we can track delinquent customers’ travels and see if their delayed payments arise from travel and travel related splurges.  Frequent holiday travel alerts emanating from social network intelligence can lead lenders to proactively minimize / reduce / freeze credit lines. While the legal and regulatory ramifications of such actions are evolving and will be tested in courts or when regulators take a stand, it is well known that Banks have used and will not hesitate to use customer intelligence in creative ways to manage their profitability.

I see a new dimension to credit risk management opening up by using social network intelligence. There is no stopping this genie.