In recent years, the financial sector has been suffering from a ballooning number of bad loans and non-performing assets (NPAs). Despite decent  performance showing 8% NPAs rate on June 30, 2020, the figures are expected to shoot up to 10-11% on March 31, 2022, according to S&P Global’s research. 

While traditional banks and large financial institutions have resources and reserves to protect themselves from the disastrous consequences, microfinance institutions (MFIs) have to maneuver in shallower waters trying to stay afloat. The only visible option for MFIs now is leveraging smart loan monitoring and early warning systems (EWS).

Digital loan monitoring and a data-driven approach

Getting a loan on the books is one part of the deal. It’s vital to retain the borrowers until the loan is due, which might take quite a time. During this period, the borrowers may transit through different credit profiles, despite their sound and trustworthy initial due diligence status. And things may go wrong with a loan before it is repaid.

While monitoring borrowers’ financial conditions and health, banks appeared to be slow adopters of technological tools for efficiency maximization and improvement of capabilities of risk management. MFIs are far more flexible and quick in arming themselves with new state-of-the-art technologies. 

For traditional banks, assessment of borrowers and loan monitoring technology can sometimes be placed out of focus and at lower priority. Microfinance institutions can and should take that as their advantage to avoid over-indebtedness and loan portfolio deterioration.

Benefits of digital loan monitoring

Digital, data-driven loan monitoring systems benefit MFIs in:

1.) Making loan monitoring processes less resource-intensive by automating remote data capturing from fragmented data sources, reducing manual processes of collecting and merging information, and assessing borrowers on the principle of protective predictive analytics instead of reactive credit scoring.

2.) Minimizing the time-to-money period through simplification and standardization of data collection, full automation of monitoring of all covenants, and end-to-end lending process orchestration.

3.) Increasing operational efficiency by carrying out more in-depth and frequent reviews while monitoring early warning signs of credit quality deterioration.

Early warning system is autopilot of digital loan monitoring

With the expected rise of NPA at hand, microfinance institutions need to rethink their existing credit risk monitoring approaches and practices. They need to move from a compliance-driven reactive approach and go towards a proactive framework of loan monitoring, focusing on predicting defaults before they occur. 

For this purpose, MFIs need to have an automated solution for tracking their borrowers’ credit health—An Early Warning System (EWS). 

Due to EWS implementation, microfinance organizations can kill two birds with one stone: 

1.) predict and prevent defaults,

2.) improve upsell and cross-sell.

Default Management

MFIs are in an unfavorable position as they don’t have much detailed information about their potential customers in comparison with traditional banks. The data about the customer’s financial state and payment discipline are only obtained in the process of collaboration and from external sources. 

In these conditions, microfinance institutions are recommended to thoroughly monitor customer data to proactively and timely react to changes in financial health. Manual or monthly checks are not sufficient. 

An EWS is a set of certain guided processes for the automated identification of risks at the initial stage and constant customer financial health monitoring. MFIs need to secure themselves from upcoming defaults by leveraging Early Warning Systems that raise ‘red flags’, when negative early warning signs occur. 

Cross-Sell And Upsell

Apart from predicting and tracking probability of default, MFIs can and should use EWS for cross-sell and upsell improvement. Unfortunately, this profitable side of Early Warning System capability doesn’t appear to be high on the list of priorities for many organizations.

Meanwhile, EWS can completely replace irregular and ineffective manual customer notifications about new products. An Early Warning System ensures real-time financial status tracking. And if customer financial indicators get better, the system automatically responds by offering new products via available communication channels with the client.

Due to automated analysis of customer financial behavior, EWS can ensure cross-sell improvement by offering related products and services. Additionally, if a microfinance institution does not have a separate anti-fraud solution, the Early Warning System can cover this security gap by detecting unusual behavior patterns. 

Conclusion

By leveraging new tools such as automated monitoring, artificial intelligence, and machine learning, lenders and MFIs particularly become capable of deriving meaningful insights into credit events, behavior of borrowers, and probability of default. 

To achieve the new vision, microfinance institutions must switch to a workflow that allows them to collect and store relevant predictive information and apply top-notch predictive analytic technologies for loan monitoring and financial distress forecasting.