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.

Succeeding in Banking Analytics – Choosing the right business model

Also available here

What is an appropriate business engagement model for succeeding in the analytics business?  This is a great question, but has no simple answer. This question seems to be on the minds of leadership in analytics service providers. What engagement model do you choose to build long term trusted relationships with your clients? What is unique in banking analytics space?  In fact, this question also came up recently, rather unexpectedly, when I was having dinner with two old friends in beautiful Los Angeles.

Professional services provided by the vendors range from staff augmentation on one end of the spectrum to high-end solutions consulting that seeks to solve complex business problems.  All this emanates from the thought construct insiders refer to as the engagement maturity model.  This model tracks the morphing of provider-consumer relationship as the two embark on their journey over time. High-end consulting offers higher and obviously desirable ROI. Hence vendors strive to attain this utopia in each of their relationships. In an ideal world, if you can design solutions / offerings that will move clients up on the value chain - from staff augmentation to high end consulting, you have a winning recipe that will make you the top vendor with an enviable revenue stream.  All this is an ideal world. But, how do we work this magic in banking and financial services space in the real world?

Businesses that serve banking and financial services clients face hidden challenges.  It is well known that analytics is what differentiates successful banks. Large, well-run institutions have star-studded analytical teams that have the depth and skills to crunch through well-organized data and come up with insights to make the right decisions.  That is the upside. The down side is that, these teams may sometimes engage in dueling analytics in an effort to be one up on the other, an avoidable waste of resources and talent horsepower. Smaller institutions on the other hand, have smaller talent pool, less organized data and limited capability to undertake complex analytical projects on their own.  

Another key dynamic is that the outcomes of analytical projects impact the banks’ core decisions.  Hence banks often prefer to work with the crème-de-la crème in the business that they can trust. This provides the vendors a great window of opportunity to showcase their excellence in domain expertise, execution and delivery to win the trust of banks. Winning the trust of banks is a prerequisite for deeper, long-term engagements.  

In other words, the analytical ecosystems in these institutions are very different – ranging from the highly competitive and sometimes counterproductive to those with less sophisticated analytical infrastructure. Understanding the extant analytical ecosystem is critical in choosing the right business model for banking analytics providers.

But in the real world what engagement mix should we choose?  Seriously it depends on the prevailing analytical ecosystem of the banking customer. My personal view is that emerging and growing businesses tend to generate a significant proportion of their revenue via an on/off site staff augmentation model. Smaller boutique vendors have successfully demonstrated this as a key entry strategy in a very competitive business.  On the other hand, there seem to be fewer examples of providers choosing high end solutions as the dominant component of their mix. But I think an understanding of the nuances of this industry and the interplay of analytical ecosystem is fundamental to succeeding in banking analytics. This understanding helps discover the right mix.

Social Media and Banking – Sibos Video

This is video presentation on Social Media and Banking I found on Youtube;  Would highly recommend it;

http://www.youtube.com/watch?v=akdKEW_VYQU

Thanks  JJ Hornblass

Enhancing Social Network Intelligence

I was browsing the internet to see how companies have come up with several strategies to make their social media presence felt. I had already written about how social networks are beings used by debt collection companies to track delinquent customers.

My insurance company (auto & home) is pestering me to add it as a friend on Face book – which is really annoying.  There are definitely better, more subtle less annoying ways to befriend the right customer or prospect.  I want to discuss one such methodology.

We can extract useful insights from social media data by combining it with what we already know about the customer from other databases such as credit bureau, rewards and loyalty programs, public records, credit card authorization data, etc.  A smart coupling of any of these databases with social media data enhances quality of social network intelligence (SNI).

Let me walk you through a hypothetical example to elaborate my viewpoint.  Mr John is a Marriott rewards member with a Marriott rewards credit card. He has accumulated significant reward points over the years.  He is active on Facebook where his posts include several dinner outings he has enjoyed with his significant other. Let us examine how we can convert this social media post into actionable intelligence or Social Network Intelligence (SNI).

Data on John’s stay at the Marriott as well as rewards card usage is captured by Marriott and Chase Cards and mutually shared.  When we combine transaction level data i.e. card authorization data with John’s Facebook posts, we have an intelligence multiplier. We can authenticate or validate his favorite cuisines by cross matching social media posts with credit card authorization data with the following information:

a)    how many times in a given period did John eat out

b)    how much did he spend 

c)    the restaurant  and (hence the cuisine) he frequents

Here two important points have to be noted

1)    Social Media posts is tracked for actual willingness and the ability to pay for choice of product  / service – cuisine in our example

2)    By this validation , we have converted a social media posts into social network intelligence -  euphemism for  actionable intelligence

Social networks provide a wealth of information, but often in its raw data. For example John’s posting about his favorite dinner outings viewed in isolation may not be as powerful as when we know if he has actually spent money and how much on his dinners.  When combined with other databases, social media posts enhance its commercial usability.