Using the past to predict the future and reduce NPLs in banking
When the Reserve Bank of India set March 2017 as the deadline for Indian banks to clean up their balance sheets, by classifying stressed assets as Non-Performing loans (NPLs) and setting aside funds (provisions) to cover them by the end of March 2016, many industry commentators were concerned. While the exercise was positive, in that it is designed to restore the health of the banks, boost lending and, in turn, drive economic growth, the implications on banks in the short term were worrying.
In the December 2015 quarter, banks in India added nearly $15 billion in bad loans, a 29% increase in NPLs from the previous quarter and a 50% jump over the previous year. Even more concerning, the aggregate net profit of the 39 listed banks fell 98% on a year on year basis. The total gross NPLs of banks, as a percentage of the total loans, has grown from 2.1% in 2008 to over 5% in 2015.
It is true that the bad loan crisis that has gripped India’s $1.5 trillion banking sector is a result of various factors, some of which are beyond the control of the banks. There are various reasons that could have possibly contributed towards this gradual build-up of NPLs and each of them needs to be catered to with a well worked out approach. Some of them would be business decisions such as deciding the criteria for ascertaining the credit quality, an organization’s risk appetite, striking the right balance between growth and credit risk etc. A point to be noted is that the larger and older State owned banks account for almost 90% of the total bad loans in the industry. However, the problem isn’t just confined to the public sector, some of the private banks also figure in the list.
However, as is normally the case, the effects are not being felt in the same way by all market participants. The question is – Why? Why is the NPL situation not equally grave for every organization? Perhaps, part of the answer lies in the technology they use, or to put it more correctly, in the effective use of technology to underpin a well-considered business strategy.
For example, let’s take the FinTech industry. Some FinTech’s are leveraging data and analytics to uniquely targeting the less credit worthy customer segment by using more effective and comprehensive ways of ascertaining a true picture of credit worthiness. Now, let’s take a look at some of the recent developments in technology which are relevant to this discussion.
The lending lifecycle begins with lead generation and this is also where technology can step in. Organizations that have been in the business for a few years can take advantage of the transaction data that they have accumulated over the period. Advanced analytics solutions can take big data and throw out predictive insights on the most appropriate target segments and the most relevant sales channels. Similarly, the data can be used to identify pattern based characteristics for ideal and delinquent customers. This knowledge can be used not only to automate the first level application filtering for cutting down cost and time but also to reduce the chance of customer turning delinquent later.
Further, while the customers are serving their loans, insights from past data can help in better servicing and identifying the most effective cross selling approach. In fact, the data analysis can help by predicting early which customers are most likely to turn delinquent, which in turn can be used to devise optimal collection approaches resulting in improved collection rates.
Analyst firm Ovum has predicted that data and analytics will be the key to the developments in digital channels for financial service industry. The report adds that in the next round of major platform developments, it will be the use of data analytics in real-time that will act as the key differentiator.
In a recent webinar on loan collections Craig Focardi from CEB TowerGroup talked about Mobile, Process Automation and Analytics as the highest ROI technology investments for Banks and Financial Institutions. The number of organizations leveraging the capabilities offered by the latest technology is still low probably indicating a lack of awareness. In a recent survey of retail bankers, 49% responded by saying that for loan collections they either do not use technology or are planning to soon refresh the legacy systems.
HDFC Bank in India uses analytics to make automated and pre-approved personal loan offerings. By taking this across the loan portfolio, the bank would have an edge in rolling out loans without taking undue risks. The use of analytics in ascertaining credit quality has enabled HDFC Bank to cut its NPLs in half from 2.05% in 2009 to 1.1% in 2014. Ratnakar Bank in India also adopted analytics to create a competitive edge in its business. Axis bank is also leveraging analytics in making informed decisions and enhancing customer experience.
It is evident that technology can empower Banks and Financial Institutions to tackle/reduce NPLs effectively while boosting business growth and productivity. It is essential for them to take the plunge and leverage the huge chunk of data that they have accumulated over the past years. This just reinforces the well known saying ‘history is the best teacher’!!