Top 4 Analytics Myths Busted

By Jasjeet Singh, Partner – Financial Services Analytics, Advisory – EY India

India’s banking industry is facing increased pressure on profitability with (i) Entry of new players (Payment Banks, Small Finance Banks) in an already competitive environment, (ii) Rise in NPAs across corporate and retail sector lending and (iii) Increasing threat of disruption of traditional banking models by Fintechs.

In such an environment, smart use of Analytics offers Bank’s an opportunity to better understand the past, control the present and confidently embrace the future. It can also help the Banks open up ‘new models of Banking’ where the focus will not be just on the financial product.

It is this promise of Analytics that led to almost all the banks (larger public sector banks and the majority of private sector ones) investing in analytics to optimise their decision making. They use analytics for fraud detection, simple credit risk calculations and marketing. However, only a few of these Banks have managed to hit the jackpot with the Analytics investments, and the rest have started discussing where they’re going wrong in this journey and what do they need to fix – people, process or technology?

The answer typically would start with reviewing the Analytics Solution Development roadmap to assess if it is (i) in sync with the Banks business strategy, (ii) in-line with the data availability constraints and (iii) focused on the right areas that hold the potential to drive Business Growth, Reduce Costs and improve operational efficiency.

We have listed some common myths that hold back Banks from realising the full top-line and bottom-line impact potential of Analytics and potential actions that the Banks can take to address the same:

• Myth 1- Having a Data warehouse is a pre-requisite to start the Analytics Journey

Banks believe that not having a single repository of data, such as EDW (enterprise data warehouse) is indicative of data not being adequately organized for analytics and decision making. Thus, they miss out on the benefits of Analytics that can be realized even in the absence of enterprise-wide data consolidation.

Action: Do not wait to fix data — leverage existing data within departments in the form of data marts to build analytics solutions while you decide about EDW

• Myth 2-Analytics is an IT function

Many banks have a technology-driven approach to analytics and run their analytics programs as standalone IT function; this usually ends up limiting the analytics outputs to “informative” instead of “transformational”; and make the function an enhanced Business Intelligence Unit instead of a Business Transformation Enabling Unit.

Action: Centralized analytics setup with joint business and IT ownership — set up analytics CoE (centre of excellence) led jointly by senior business and IT executives to provide direction and measure RoI (return on investment) for these programs; depute subject experts from departments to analytics CoE to drive prioritisation basis on-ground requirements and the ability to drive adoption.

• Myth 3 –Focus on Analytics solution development (tools, technology, technical skills) to get the ROI

Banks have been laying primary emphasis on production aspects of analytics (technology, tools, data and advanced analytic skills) over the last couple of years. However, it is ultimately people in the business who need to act upon those insights in order to generate positive business outcomes. Leaders need to make sure that their teams, across business functions, are fully equipped and required to use data analytics insights in order to present ideas, make the evidence-based case for change and to take action to generate value.

Action: Lay equal emphasis on Analytics solution development and embedding analytics solutions into business processes. IT Implementation SPOC and Business process SPOC should be engaged very early in solution development process to ensure seamless implementation

• Myth 4- Experience based judgement trumps data

Most bank leaders prioritize experience-based judgments over data-driven insights, or if they use Analytics are comfortable as long as data-driven insights help them validate the decision they have already taken. Analytics ROI in both the above cases is nil. With the advancement in technology, we today have the option of optimising decision-making at customer level (i.e., n=1) instead of taking decisions at aggregate portfolio level or at 8-10 customer segment level. This needs a fundamental change in how Banking Leaders operate

Action: Designate a Chief Analytics Officer who is not afraid to question status-quo; and provide Analytics function the Executive Sponsorship from the CEO’s office to enable this to become a Transformation Function.