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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.
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.
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.
The major factors that are responsible for the surge of NPAs are as follows:
It may be noted that these factors can be addressed and controlled by AI and Data Analytics.
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.
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:
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.
Also, read: Customer Onboarding in Digital Banks