Can Banks Leverage Big Data Analytics to Reduce NPAs?
By: Rajesh Shewani, Head - Technology and Solution Architecture, Teradata India.
Non-performing assets (NPA) are one of the biggest challenges facing the global banking system and, particularly, Indian banks. While Public sector banks have displayed excellent performance and have beaten the performance of private sector banks in financial operations, they are facing increasing levels of non-performing assets, year on year. On the contrary, the NPAs of private sector banks have shown a decline. The Reserve Bank of India’s ‘Financial Stability Report; the 15th biannual edition of which was released in June this year portends that Indian banks’ health will worsen. The report warns that the banking system’s gross bad loan ratio will rise to 10.2% of the total loan book in March 2018 from 9.6% in March 2017. For public sector banks, the gross bad loan ratio could rise to 14.2% by March 2018, from 11.4% now as per the report. Between December 2014 and May 2017, the non-performing assests of India’s listed banks increased from 4.3 lakh crores to 7.11 lakh crores.
Just to put that into context, this is double the total allocation for the defense forces made in the 2016 union budget (Rs. 3,40,922 crore) and almost 10 times the allocated for education (Rs 72,394 crore). For these large banking institutions, a significant portion of NPAs are the unpaid loans of large and listed blue-chip enterprises in the capital intensive sectors like steel, power, aviation, telecom and infrastructure who borrowed bullishly when times were good without anticipating or provisioning for subsequent downturns.
‘Too big to fail’
With the central bank, investors and policy makers baying for blood, bankers clearly have their backs to the wall. The causes of the current crisis resemble those of many previous ones: banks lent too much and too easily, skipping or over-looking adequate due-diligence and relying on questionable collateral (as was seen in the case of loans given to Kingfisher Airlines). As everyone will tell you, a bank’s collapse has a domino effect – affecting both the financial system, and the economy. The resulting outrage and chaos then has regulators stepping in to protect not just the bank’s small depositors but also its creditors (and consequently its shareholders) from losses they would otherwise face. More so in the case of banks that are seen as “too big to fail”. While this kind of protection may address the immediate problem, it rarely addresses the root cause issues.
Technology to the rescue!While central bank mandarins and regulators will debate this at the policy level and put in additional strictures for banks to follow, the question is if banks can better leverage technology such as advanced data analytical solutions and platforms to understand and manage risk better.It is important to approach tackling this issue in a phased manner by prioritizing on a select few capabilities initially such as external data acquisition for credit assessment models, early warning framework and collateral management for credit monitoring, soft landing and care programs for default management and centralized recovery and collection.
Implementing an early warning solution (EWS) can help substantially reduce banks’ NPAs. Teradata provides soltuions which have systematic approach that combines a bank’s existing and new data sources within a strong analytical framework, and helps banks develop a custom approach that is specific to their portfolio needs.
Data Analytics – Reducing the risk of NPAs
Advances in data storage and processing systems in banks makes it easier today to employ data analytics techniques to manage NPAs and drive revenue. Banks can benefit from solutions that unearth early warning signs of NPAs and also analyse patterns in historical data of customers during the credit appraisal process. Banks can also use data and analytics to ensure responsible lending. While credit bureau data can play a crucial role in customer management practices, decision analytics help banks and NBFCs undertake effective risk management measures and provide better customer service. With changing market conditions, a robust monitoring system would allow the bank to align its exposure in line with its risk strategy, enabling it to monitor slippages and indicate low asset quality on the loan book leading to credit and reputational risks for the banks. With an effective EWS, a Bank can have a definite process to govern credit monitoring, ensuring a standardised bank-wide approach to detect and escalate EWS. It can have an effective knowledge management system to retain the organisation’s learning of each type of customer. Free up relationship manager to make him or her capable to handle more responsibilities at the ground level and achieve better compliance to regulatory requirements and audits.