The role of intelligent analytics in retail banking
Written by Banking Tech – Thomas Mathews is vice president of banking at Genpact
When looking at the retail banking industry today, I’m struck by how different the landscape is to a decade ago, writes Thomas Mathews
Since the financial crash retail banks are faced with more regulatory and financial restrictions than could have been envisioned. This is coupled with increased levels of competition and much reduced consumer trust. As they look to respond to this, a further significant change can offer part of the solution.
Today, Chief Information Officers (CIO) have more information and data than their predecessors could have imagined a decade ago, which if used effectively has the potential to address a number of these challenges. However, in my experience, many banks are failing to effectively harness the vast amount of data available to them and in turn are missing out on the opportunities that it brings.
I believe that the CIOs which develop ‘intelligent analytics’ functions to maximize this data will be able to grow revenue streams, reduce costs and improve compliance with regulation.
In the below, I touch on the challenges and steps that CIOs need to take to further develop their intelligent analytics functions.
The importance of accurate data to drive the data-to-insight-to-action process
In developing an intelligent analytics function, the first critical point to note is that banks must have one single source of accurate information. For example, in some banks, the data that the finance teams report to the market is different from the data that sits with the risk team. Data source integrity is crucial as without this any analytical conclusions may produce inaccurate insights and, as a result, poor decisions.
Once an accurate, integrated and comprehensive reporting system is in place, banks can use the data for a variety of purposes, such as ensuring compliance with regulations and identifying areas to cut costs. It can also be used to build a whole new approach to understanding customer needs, which in turn can be leveraged to drive revenues.
As an example, by developing effective and accurate information management systems, banks can measure operational performance and as this performance is measured, customer insights become apparent. What is measured, becomes relevant and tangible.
It is worth remembering though that turning data to insight and then action requires effective business processes, which are often overlooked. To generate material business impact requires implementation at scale, as well as appropriate design, incentives, and individual accountability.
Growing revenues through customer insights
With significant pressure to grow revenues, many banks are failing to make effective use of their customer data which can identify opportunities to cross and up sell.
One of the reasons for this is that traditional large retail banks have developed silos across their business. The consequence is that each business unit becomes self-sustaining and communications and interactions are reduced and weakened. One of the implications of this is that customers are required to have similar conversations with different parts of the bank.
For the bank, this creates additional costs and reduces its ability to understand what customers really want, resulting in lost opportunities to cross and up sell.
By improving communications and the sharing of data across business units, banks are able to adopt and make best use of intelligent analytic tools such as Know Your Customer technology. This type of technology is much more sophisticated than traditional CRM systems, and as a result, more tailored offerings, products and services including retail banking, corporate banking, credit cards, lending, asset management, and wealth management, can be delivered to attract new customers and better nurture existing ones.
Analytics can also be used to identify areas of the business where costs can be reduced and resources directed to where they are needed most.
There are a number of areas across the business where this is applicable. For example, the use of intelligent analytics in banks’ marketing functions can help with campaign management by ensuring that the right channels and pricing models are used for specific target audiences. In risk and compliance, building fraud models for acquisition and transactions allows risk managers to better understand the customer and identify early warning signals to avoid losses. For banks’ core customer operations, the development of analytic frameworks around customer satisfaction and call center activity can identify areas to tighten upstream and downstream processes linked to specific customers, thereby reducing costs and lowering the risk of customer dissatisfaction.
The key priority for banks is to ensure that intelligent analytics are rolled out across the business and that individual’s feel empowered to use the insights from the data to make effective changes. The customer will continue to demand more each day and banks need to adapt and be flexible to meet this evolving change in customer behavior.
Dealing with regulatory pressure and risk
As has been well reported, since 2008, regulatory oversight has expanded significantly, which in turn has dramatically increased the cost of compliance and the increased risk of failure. For example, regulations such as Basel III, Dodd Frank and Know Your Customer (KYC), have significantly expanded the rules governing banks’ activities.
Intelligent analytics can be implemented to enhance compliance and ensure that the complex decision-making process can be navigated smoothly. For instance, with accurate analysis banks will be able to compare activity to regulations, which can then be used to prioritize high-risk areas of non-compliance.
Intelligent analytics can also be used in future risk modelling, in particular to understand short and long-term profitability and capital adequacy. For example, banks can predict prepayments, delinquencies, defaults, cash-flows, and as a result adjust their loan-to-value ratios in real time.
Analytics is critical to ensuring the banking industry meets the challenges it faces. However, while basic reporting and descriptive analytics continues to be a must-have for banks, ‘intelligent analytics’ is still an under-utilized area. Once banks understand the untapped revenue generation and cost saving potential that this type of analytics provides, more will adopt it as an essential part of their business.
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