RBI’s Plunge Into Data Science Can Help The Economy In Big Way

Central banks around the world are adopting data science to help shape policy, get a deeper insight into the economies, enhance regulatory risk, sharpen surveillance and ultimately transform the work that they do. Recently, Reserve Bank of India (RBI) too has decided to dive into the world of Big Data to improve forecasting, nowcasting, surveillance and early-warning detection abilities which will aid policy formulation.

How Big Data Can Help RBI

With the country reeling under a huge burden of non-performing assets (NPA) worth over ₹7.33 lakh crore as of June 2017, it has become important for the Central Bank of India to secure critical financial data, store and analyse transaction records for a robust monetary system. RBI has decided to utilise the data in policy making because there is an ongoing escalation in information gathering, computing capability and analytical toolkits, data collected through regulatory returns and surveys. Also, large volumes of structured and unstructured real-time information are being sourced from consumer interactions in the digital world.

Monetary Policy

The monetary, financial and transaction data will help the RBI to gauge the pulse of all the sectors of the Indian economy. With the real-time data, RBI will be able to mix, match and mashup all the information and will be able to roll out more realistic monetary policies.  Starting from interest rates, cash reserve ratio, the purchasing power of rupee, consumer and wholesale inflation, availability of credit for critical sectors like agriculture and manufacturing will be positively impacted by the data analytics. “We have to be forward-looking when conducting monetary policy rather than looking at inflation rates today or yesterday.” RBI Governor Urjit Patel said.

Inflation

It will also help in scanning retail inflation data on daily basis (published by the Ministry of Consumer Affairs) and also on weekly basis (published by Directorate of Economics and Statistics) and juxtapose it with data from even supermarkets to generate an inflationary expectation index well in advance.

GDP Growth

Analytics will help RBI to predict GDP growth and be really helpful in understanding and predicting real estate demand and prices. By plugging into databases of consumer credit rating agencies, RBI will also be able to spot the trends in retail lending.

Money Transfer

The data from money transfer like IMPS, NEFT and RTGS are huge in volume. With the help of data analytics, RBI will be able to do a timely analysis of this data to understand bank intraday liquidity management, retail and consumption data adjustments as well as a cluster of money movement formation in India. It will also help RBI understand the labour market using mobile phones. The call data records can help track population movement or migration patterns in response to labour market shocks and give an insight into the internal migration in low-income economies. “Analytics is helping deliver better-operating ratios through both top-line and bottom-line initiatives, drive customer engagement, mitigate risks, and optimize the utilization and deployment of banks’ resources.” SBI’s former head of analytics, Vijay Kumar told AIM. To harness this power of data analytics, RBI recently announced the setting up of an in-house Data Sciences Lab which will employ professionals with skills in data analytics, computer science, statistics, economics, econometrics and finance. The unit will be operational by the end of this year. Last year, RBI had also established ReBIT (Reserve Bank Information Technology) for its IT and cyber requirements to secure critical financial data, store and analyse transaction records and defend cyber and digital assets essential for a robust monetary system.

Indian Banks Who Are Using Analytics

While data science is a new kid to the central bank, it has already made inroads in the commercial banks. Almost every Indian bank is already using big data to drive profitable growth, manage debt and gain valuable insights about customers to serve them better. Here are some cases of how banks are using data analytics: HDFC Bank: HDFC Bank adopted analytics back in 2004 and have put in place a data warehouse and started investing in technology that would help it make sense of the massive troves of unstructured data captured by its IT systems. All central marketing campaigns are analytically driven and deliver on an average 3x-4x lift over BAU (business as usual conversion rates). It is the First bank to implement 10-second personal loan, 10 second Auto loan and Two wheeler loan. HDFC Bank Analytics and Business Intelligence team have around 265 employees – 27 from Marketing Analytics, 65 from Risk Analytics Unit and 173 from Business Intelligence. The ratio of analytics team personnel as compared to the to the employees of all the other departments is 0.004 (Analytics staff strength is 0.40% of the overall staff strength). The depth of analytics function in the bank can be gauged by the fact that 95% of all marketing efforts /activities being done by the bank are analytically driven. For key products like Personal Loan, Auto Loan and Credit cards, Analytics has enabled setting up of an end to end fulfilment process. Kotak Mahindra Bank: They have invested significantly in beefing up the CRM infrastructure.  Kotak’s analytical suite is powered by SAS, R, some open source tools and licensed tools that help drive basic and predictive analytics. The bank has also deployed IBM’s Unica — an omnichannel analytical CRM system that enables customer segmentation and creates direct marketing campaigns at those customer sets, thereby helping in tracking the campaign journey end-to-end. And through IBM Interact, the bank is able to engage the customer with consistent and uniform communication about the potential engagements that can be done. State Bank of India: SBI, one of the largest bank in the country has a fully functional data warehouse paired with both a robust BI platform and highly skilled statisticians. This has enabled the delivery of analytical insights for use at every level in the organisation for several years now. “SBI has a large and diverse portfolio of products servicing a wide spectrum of customer segments. We have grown significantly and rapidly over the past few years. The branch network has doubled over the last twenty years, and the total business has grown almost twenty-fold over the same period. The bank has maintained profitable operations throughout this period, while actively mitigating risks. Such explosive growth would not be possible to manage efficiently without the effective use of Data-driven management practices at SBI. Be it Treasury operations, or branch operations, Foreign-exchange dealing, or Credit Risk, development of Alternative channels or cross-sell, Rural Banking or Corporate Credit – Analytics plays a part in improving the bank’s performance on multiple metrics,”  Kumar told AIM. ICICI Bank: The use of data analytics has been a game changer for the Debt Service Management Group (DSMG) at ICICI Bank. Using analytics across the spectrum in DSMG has brought about a number of efficiencies in the debt management process. Axis Bank: They use analytics to research background information of customers and the likelihood of her/him taking a loan, before pitching to them. The implementation of SAS analytics has helped the bank in terms of returning big business value. “Our implementation of SAS Analytics has let us achieve substantial progress in terms of returning big business value to our organisation. For example, in our consumer lending business, we have automated the assignment of scores based on risk and other propensities. The risk scorecard has led to scorecard-driven underwriting, which we believe will result in better accuracy, consistency and, ultimately, lower credit costs for the business. We are also applying analytics in our marketing campaigns and are seeing great returns. All the new business practices based on our analytics work have increased the growth rate of that business unit and established the business value of analytics.” Balaji Narayanamurthy, Executive Vice President of the Axis Bank’s Business Intelligence Unit said.

Other Central Banks Which Are Using Big Data And Analytics

Not only Indian banks but Central Banks around the world are using big data to get a deeper insight of the economies that they manage. Federal Reserve Bank of the US uses consumer price indices to predict inflation, job sentiment and expectations in each quarter. Bank of Japan has been using analytics to analyse economic statistics by beating private forecasts on the accuracy of its GDP predictions and evolving its own experimental index that has pushed the government to assess if it’s understating growth. People Bank of China is using big data to boost its ability to recognize and prevent and reduce ross-sector and cross-market financial risks. Bank Indonesia is using data analytics to help make its policy more effective. It is using social media, news sites and other content to monitor public perception and rate expectations. European Central Bank is using big data to measure inflation in real time. Bank of England researchers has used big data to examine the pass-through of large exchange-rate changes.

Conclusion

Creating its own data science lab will help the central bank overhaul India’s struggling statistical system, help in better management of inflation, financial inclusion, banking supervision. With digital payments, frauds, bad loans and scams becoming more common, the establishment of such arm has been a long time coming. After $1.8 billion fraud in the Punjab National Bank, listed Indian banks have lost nearly ₹69,750 crore in market capitalisation. To avoid banks to continue bleeding, it is important for the bank regulator has itself more data-driven, and setting up a Data Science Lab is a step in the right direction.