Data Analytics for Non-Performing Assets (NPA)

Ashish M. Shaji

01 Aug, 2020

Experts representing co-operative banks are of the opinion that data analytics for the management of Non-Performing Assets will be one of the topmost priorities while operating after lockdown. In this article, we shall look at the subject of Data analytics for Non-Performing Assets, but before that, let’s understand its meaning.

What are Non-Performing Assets?

Non-Performing Assets (NPA) is a loan or advance for which the principal or interest payment has remained overdue for a period of 90 days. A loan may be classified as a non-performing asset when it has not being repaid by the borrower.

Can data analytics be the answer for the management of NPA?

Digitization has been implemented in many industries, and banking is no different. However, investment and learnings have been limited to the retail landscape. Corporate banking and risk management applications have taken a back seat, or it started late. Lack of implementation or late implementation of digital technology has impacted the loans set up, and it has become one of the significant issues concerning the Indian Banking set up.

It is noteworthy to mention that data analytics can be used to improve the deficiencies of the banking industry and control the trickery of the borrowers. Established used cases from retail, as well as consumer banking, can be used without a massive investment and IT change.

What are the factors responsible for the surge in NPAs?

The major factors that are responsible for the surge of NPAs are as follows:

• Improper Due Diligence
• Lenient Credit terms
• Loose Credit Monitoring
• Collateral free loans
• Frauds
• Wilful default by customers

It may be noted that these factors can be addressed and controlled by AI and Data Analytics.

How can Data analytics be used to address the common problems of NPA management?

When we analyze the root cause of the issue with NPAs, we find that as far as borrowing entity is concerned, the issue relates to the mis-utilization of cash and loan, suboptimal project management and oversight, invisible and unclear corporate structures, excessive leverage on the balance sheet and creative accounting practices.

 As far as banks are concerned, highly divergent loan appraisal standards, absence of a common database, lack of timely intervention on red flags, excessive use of discretion, non-perfection of security interests, lack of loan covenants enforcement and not informing the statutory authorities are the common issues with NPA management.   

So now, coming back to the question, how can data analytics for non-performing assets address this issue? Well, there are a number of solutions to it. The first step could be an ideal big data platform that takes and processes data about a company from different sources. It can deploy standard open source and inexpensive analytical tools that can help in creating dashboards and distributing insights, thereby preventing the borrowers from having arbitrage from a distributed and diverse database.

Likewise, deployment of analytics can help in the elimination or reduction of subjectivities in appraisals and human errors in assessments. In the loan management stage, big data can enforce credit discipline across banks and reduce abnormal behavior by the commencement of non-discretionary action like statutory notification, etc.

What are the benefits of using data analytics for Non-performing assets?

 As stated earlier, data analytics provide a number of solutions for the management of non-performing assets. Therefore it is beneficial.

Some of the primary benefits of Data Analytics for non-performing assets are as follows:

• Data analytics can help in the forecasting of the NPAs as NPAs are going to pose a massive challenge after Covid-19 considering a lot of businesses are not doing well. It can help in predicting loans that are going to turn NPAs in the near future.
• Data analytics can help the business recovery teams to come up with the right strategy to deal with the account in terms of measures and strategies.  
• By using analytics, cartelization that is resorted by the borrowers to prevent banks from recovering the fair value of security can be thwarted.
• Data analytics will cause the enhancement of the process and reliable database automation, which will further help in improving the transparency in decision making.
• It will also help the bankers not to fear later accountability, which is one of the significant obstacles in the rebooting of the system.

How is data analytics helping in tackling the risks faced by the banking sector?

The banking sector today faces numerous risks at all times. Some of the grandest risks faced by them are non-performing assets. The use of data analytics for non-performing assets is helpful in such cases. Data helps in monitoring the health of the bank, and analytics allows it to chart a pattern of time and reasons as to why borrowers default on their payments. This is applicable to both corporate and individual borrowers.  

 For instance- a drop in rainfall expectancy or poor climatic conditions increases the chances of default from an agricultural company or from a person or a company that is dependent on rural markets for their business. Therefore, the cost of capital or the credit for such companies or individuals would be more than what it usually is for others (solvent individuals or companies). Hence, having such information about risks helps the banks to avoid them and also prevent unwanted distress.  

Conclusion

Also, read: Customer Onboarding in Digital Banks