New solutions for credit monitoring enable a wider outlook for identification of potential defaulters
In today’s fast-emerging world, Banks and Financial Institutions (FIs) are making strides in innovation. The combination of Robotic Process Automation (RPA), AI/ML-powered systems, and statistical models enable a comprehensive outlook on data in Digital Lending.
These technologies maintain process performance and transparency to enhance efficiency in lending. Where the spotlight shifts to a new approach in Banking – 'Value Co-Creation’, these are beginning to play a bigger role.
The approach helps Banks base consumer engagement on products and services co-created with active inputs from borrowers. Most of the offerings in the approach personalize customer journeys, where technologies simplify functions for the intended engagement.
In line, collections from Special Mentioned Accounts (SMAs) are becoming easier with Value Co-creation’s clear goals in place – to guide borrowers in times of distress. Aided by systems that run predictive analytics, Banks can face problems with this new age outlook.
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 collaborative activity with Banks helps create a well-developed feedback loop. In the case of repayments, this helps Banks enable a truly customer-driven outlook.
For collections processes, engaging borrowers is a difficult but important task. Adopting the value co-creation approach requires lenders to maximize positive interactions. Engaging interaction improves after-sales teams in an attempt to be considerate towards a borrower's hardships.
Here, data solutions using new-age technologies help lenders proactively solve customers' problems in situations where an account shows incipient stress. For this, systems monitor many factors of a borrower's account and enable a holistic view of any changes in repayment terms if needed.
Recent research shows, Banks using this approach deliver value two to four times greater than those which don’t leverage the co-creation business model. This value gap has four main tenets: 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 (SMAs) are those assets/accounts that show symptoms of bad asset quality in the first 90 days or before it is identified as NPA. Identifying these accounts is 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 — delays in submission of stock or financial statements, payment defaults, delays in repayment, etc.
Gradually, regulators are expanding the list of tracking indicators that tag an account as stressed. New-age credit monitoring systems go beyond the traditional methods of identifying accounts. The traditional method of tagging stressed accounts are —
SMA 0 are accounts where the Principal or Interest payment is overdue for less than 30 days, but show signs of incipient stress.
Here the case can also be recognized as SMA-NF which refers to Non-financial (NF) signals of incipient stress. This can be because of other factors reflecting potential sickness /irregularities in accounts.
SMA 1 refers to those loan accounts in which the installment or interest is overdue for 1 month from 31 day to 60 days.
SMA 2 refers to accounts in which the installment or interest is overdue for 2 months from 61 days to 90 days
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), Financials (Financial Submissions), external media (social, news), backward and forward industry linkages, and other statistical data from analytics solutions.
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 their 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 in more interactions during a 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, which offers intelligent decisioning to zero in on an amount that can be demanded from defaulting borrowers.