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Leveraging Analytics in the Banking Industry

Deloitte Insights video podcast

Powerful industry trends are putting analytics at the top of the banking agenda. While advances in technology are increasing the amount of data organizations can collect, forces such as regulatory reform, systemic risk and customer analytics are increasing the importance of turning that data into insight — and ultimately better business decisions.

Tune into this episode of Deloitte Insights to learn how organizations in the banking industry can find growth through the application of business analytics.

Guests:

Brian Johnston, Principal, Deloitte Consulting LLP

Omer Sohail, Principal, Deloitte Consulting LLP

Transcript: 

Leveraging AnalyticsSean O’Grady, Host, Deloitte Insights: Hello and welcome to Deloitte Insights. Today, we are going to talk about how organizations in the Banking Industry find growth through the application of business analytics. On the desk for this discussion is Brian Johnston, a Principal and the leader of the consulting banking practice in Deloitte Consulting LLP. We also have Omer Sohail, a Director in Deloitte Consulting LLP and a Banking Business Analytics and Information Management leader. 

So gentleman my first questions let’s talk about the analytics landscape in the banking industry, what trends are we seeing right now Brian?

Brian Johnston: There are some very powerful trends right now that are occurring in the banking industry around Business Analytics. Banking is a data-driven business. It’s really about turning data into information and right now data is growing exponentially in terms of all the technology that has recently coming into the Banking Industry. But there are really three forces that are playing right now. The first is regulatory reform. Everybody has heard about Dodd-Frank, the act that was passed last year. It really had major ramifications on the whole business model in terms of the data and information that banks need to now track and report on. It is really changing the whole interaction model with the customers as well, that’s the first thing. The second is about systemic risk. The regulators are really pounding on the banks right now in terms of better understanding the data that they have, turning that data information so that they can make better business decisions, so that they can manage the institution in a much more effective and efficient way, very very key from a regulator perspective. We are not sure that’s going to go away anytime soon. And the third is around customer analytics. It’s really about the banks are focusing on customer segmentation, pricing, profitability; really the front-end piece of how do I grow and enhance my customer relationship over time.

Sean: Omer how about you. What’s your take on the analytics landscape in Banking?

Omer Sohail: Well, I couldn’t agree more with Brian. Everybody talks about the Dodd-Frank Act. There are actually 15 titles in the Dodd-Frank Act with a lot of focus on title 10 about the bureau of consumer financial protection. But there is requirement of some direct impact data from living wills and there are additional requirements, a lot of it is unknown about the office of financial research and the requirements coming down from the office of complex financial institutions and there is stuff like derivatives and central clearing and trade reporting. A lot of investment not only from the regulatory side of the house, but the banks are also proactively looking in to become a little bit more timely in their way of presenting meaningful information than data. One more thing I want to touch point that Brian mentioned was about systemic risk. There is this notion and also a fear in the industry and a lot of study has been done on this topic about the technology infrastructure is going to be a key hindrance on trying to be able to provide meaningful information and in timely manner to this additional requirements that are coming down the way. There is I think a feeling at least among us in the industry that it is going to take years for large bank holding companies to be able to get past this time. So there is the idea of how do you get past from a road map perspective and do some of the tactical things to get thing moving but you still have to modernize the whole landscape. And finally Brian hit it spot on was on the competitive side of the house, but a new world out here, simplification of the banking models, simplification of the products, enhanced share of the wallet. There are already so many deposits in the world and so many banks and everybody is being very competitive about it. And historically, I think the banks have not done a pretty good job in trying to relate with their customers. So a lot of focus on customer segmentation and pricing and those sorts of things as well as what we see.

Sean: Let’s move ahead to value. Brian how can a bank get real value out of the collection and implementation of analytics?

Brian: Few banks at this point have really developed capabilities to exploit data as a corporate asset if you will. But banks right now are focused on really three things on the data front. One is creating data strategies. The next is really developing data road maps and governance modes. So how do I really govern my data across the enterprise, how do I reduce the amount of data, how do I eliminate redundant data. And then the third is really around first identifying and then appointing and putting in place your data czar. Banks have struggled with this because it is very difficult for one single owner of data, but it’s very very important from an accountability perspective to have somebody really be the single point of truth from a data perspective that really goes across the enterprise.

Sean: Omer how about you? How can banks get real value out of analytics?

Omer: I will talk a bit about solutions and demand we are seeing in the market. There is this notion what you call the integrated risk analytics. So stress testing is a common examination exercise. Then there is a notion of reverse stress testing to understand how much liquidity exposures do you have. The banks have operated on credit market risk, but there is the idea of doing risk monitoring at a more proactive level. There is AML, a lot of focus, big focus around an AML program at a very large institution and continued to be focused on the home lending and insurance side around mortgage-backed securities but then also the notion of fraud analytics and mortgage fraud. So there is one piece of what I would call seeing in the market. The other thing is around back office data, reconciliation of data across general risk and finance that we have seen a huge uptake on. The idea is to have consistent information in the risk and finance side of the house and the regulators come calling, you can provide consistent and confident information in a timely manner. So these are the big large data programs that we have seen very large bank holding companies getting into. The last notion is something we touched about as well before was the idea of knowing your customer analytics. So putting your value on what your customer satisfaction is, putting your value on customer life time value, different customer segmentation schemes using tools like self-organizing graphs and what not that a lot of the banks are actually implementing, and then around even servicing the origination piece of the puzzle. So those are the investments that we are seeing the banks are actually doing.

Brian: Just one thing I want to highlight around the customer analytics. So banking is vey cyclical, very tied to the economy. Over the last few years, banks have really been focused on the cost side and creating efficiencies, which really is sort of a cost play. Over the last year or so, we have really seen banks focus on the customer, the revenue side. So how can you generate incremental revenue with the customers that I have through better cross-selling customer segmentation techniques. So we are really seeing a push sort of at the frond-end now around the customer.

Sean: Thank you very much for that gentleman. My last question is for both of you, but Omer, we will start with you and it is about the sea sweep. What are chief information officers doing right now to leverage analytics?

Omer: Well as usually, I have three things to say about everything so I will say three more things about this one. I think the fundamental thing if I were to say anybody’s simple name in terms was, there should be a business-led technology-enabled strategy on handling some of these big data programs. Historically, there has been inertia between the business and technology sides of the banks to be able to work together. Some of the banks were actually becoming leading players in the leveraging analytics and data and the business processes are actually trying to figure out what is the data that is being used by the business for and what problems we are actually trying to solve, what are these key risk indicators rather than doing technology for sake of doing just technology. The second thing that I would say as a success factor is the proper model of governance and control. So the people, the data stewards and with those data steward’s counsel said, where do these data stewards come from, the standards, the policies, the oversight in control and depending on the size and scale of a bank holding company and how distributed they are, there are different models that play well. So there is a notion of a centralized governance model and there is a notion of a distributed governance model and then there is what we call formal federation. So some things are centralized, well aligned of the business, and operate as different units. So it all depends on the way the bank is structured and we have seen some of the large banks actually moving a lot more toward a formal federation model. The last thing that I think is important is to continue to reduce cost and leveraging commonalities across different pieces of the banks, which is enabling shared services. So common sourcing strategies, building data and analytics center of excellence to actually harmonize and cleanse some of the data and provide to reduce provisioning points to the different lines of business to the extent possible. I think some of the successful banks are doing those sorts of best practices.

Brian: One thing I just wanted to build upon regarding what Omer said was how these programs really need to be business led. Omer and I have led a client last week and we are talking to some folks in the IT Organization who are part of an IT data program. And we asked simple questions like what type of decisions will the business to make that will be better or more efficiently made because of your data program underway. Who is the business owner of this data program? Simple questions like that they just could not answer, which really raised some flags around how is this data program positioned and will it be successful over time. So it really gets back to ownership, having been business sled, having that data czar, that both Omer and I have talked about, making sure that it is enterprise wide that you are really breaking down to silos. Difficult challenges but challenges that really do need to be addressed to have successful data program.

Sean: So make sure you always have accountability. You had been listening to Omer Sohail, a Director in Deloitte Consulting LLP and banking and securities’ business analytics and information management leader. You had also been listening to Brian Johnston, a principal in Deloitte Consulting LLP. If you would like to learn more about Omer, Brian, or any of the topics that we have discussed in this broadcast, you could find them and many more on our website, it is www.deloitte.com/us/podcasts.

For all the good folks at Deloitte Insights, I’m Sean O’Grady – we’ll see you next time.

 

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