New Solutions for an Evolved Outlook towards Special Mentioned Accounts

New solutions for credit monitoring enable Banks to automate identification of potential defaulters before getting tagged as SMAs

In today’s fast emerging world, banks and other financial institutions are making strides in innovation. The combination of RPA, AI/ML-powered systems, and statistical models is enabling a comprehensive outlook on data in digital lending.

Maintaining performance and transparency in processes enhances efficiency in lending. As the spotlight shifts to a new approach to Banking – 'Value Co-Creation’, lending processes based on this new outlook are seeing a rise in profits.

Collections from Special Mentioned Accounts are becoming easier with Value Co-creation’s clear goals in place – to ease the load of repayment with a customer-friendly approach! Aided by systems that support big data and run predictive analytics, Banks can look at same problems in collections, but can solve them differently.

Value Co-Creation in Lending: Shifting from Customer-centric to Customer-driven

Nowadays, people are inherently creative and want to shape their own experiences. Here value co-creation comes into play. Engaging and collaborating in activity with Banks harnesses their creativity in the process of product development and creates a well-developed feedback loop. In the case of repayments, this helps Banks enable a truly customer-driven outlook.

For collections, engaging borrowers in collections processes is a difficult but important task. A shift towards adopting the value co-creation approach requires lenders to maximize positive interactions. Interaction with aftersales and collection teams can be transformed into a process that considers customers’ hardships and offers customized repayment options.

Recent research shows, Banks using this approach deliver value two to four times greater than those which don’t leverage co-creation business models. This value gap has four main tenets – and Increased organizational flexibility, improved customer insight, greater revenue growth, and lower marginal costs.

Traditional Ways to Identify SMAs and What's New?

Special Mention Accounts are those assets/accounts that show symptoms of bad asset quality in the first 90 days of receiving the loan amount. The identification of these accounts are necessary for early discovery of stress in bank loans. Special Mention Accounts are usually categorized in terms of duration.

However, these accounts exhibit signs of irregularities indicating the possibilities of more stress in coming days. These can easily be tracked in in financial statements. Some discrepancies that is noticed in accounts are — delay in submission of stock or financial statements, frequent return of cheques issued by borrowers, non-compliance of terms and conditions for loan sanctioning, etc.

Gradually, regulators are expanding the list of trackable indicators that tag an account as stressed. New-age credit monitoring systems go beyond the traditional methods of identifying accounts as —

SMA 0 are accounts where the Principal or Interest payment is not overdue for more than 30 days, but show signs of incipient stress.

SMA 1 refers to those loan accounts in which the installment or interest is overdue for 1 month from 31st day to 60 days.

SMA 2 refers to accounts in which the installment or interest is overdue for 2 months from 61st days to 90 days

SMA-NF refer to Non-financial (NF) signals of incipient stress. The ‘Special Mention’ category of assets are considered not only on the basis of the non-repayment or overdue position of the loan accounts but also due to other factors that reflect potential sickness/irregularities in the account (SMA -NF).

These are called 'Early Warning Signals (EWS)' in banking parlance. These are valid indicators for stress in a borrowal account. These are intended to alert the management that if no corrective / appropriate action is initiated on the SMAs well in time, then such accounts may turn bad and become NPAs.

Before big data came into play, incipient stress was identified in a few borrower activities like frequent return of cheques issued by borrowers, non-payment of bills discounted or under the collection, incomplete documentation in terms of creation / registration of charge / mortgage etc., and more.

Early Warning Signals, today, are now collated from transactional (based in CBS), financial (financial submissions) , external media (social, news), and backward and forward industry linkages.

Early Warning Systems: Big Data and Predictive Analytics for Credit Monitoring

Built to identify early warning signals, an Early Warning System (EWS) powered by Artificial Intelligence (AI) and Machine Learning (ML) predicts and prepares Banks for risks from borrowal accounts 9 - 12 months in advance. Apart from the traditional signs of stress, it enables lenders to track external causes and its effects before it manifests in loan books.

For internal as well as external causes, Big Data Analytics (BDA) collects, organizes, processes and analyzes large, diverse (structured and unstructured) and complex sets of data. Further on, predictive analytics helps predict future events mainly referring to specific types of analysis on current activities, market standing, and backward-forward industry linkages.

Proactive Engagement with Borrowers: Foreseeing Opportunities in Challenges

Newer monitoring processes bring transparency and help minimize risks well before time. More importantly these new-age credit monitoring systems help improve customer experience with foresight into portfolio performance. Value Co-creation can be prioritized and potential defaulters can be engaged to be more interactive in their repayment tenure.

However, the deteriorated loan portfolios which are on the verge of falling into delinquency can be administered by a One Time Settlement (OTS) solution, that offers intelligent decisioning to zero in on an amount that can be demanded from defaulting borrowers.