The consumer lending business is centered on the notion of managing the risk of borrower default. The increasing concerns around Non-performing Assets have stressed the entire financial system. NPAs are one of the significant challenge lenders faces, and an immense need to strategize and implement a comprehensive approach to managing NPAs has risen.
Lenders must deploy automated tools to strengthen the loan origination system using early warning signals and extensive dashboards for decision-making. Rather than treating NPAs after they’ve already occurred, firms must adopt preventive measures to control them.
NPA management: identifying and managing early signals
In recent years, the lending sector has seen a rise in Non-performing Assets for various reasons. In this scenario, lenders must exercise greater caution while monitoring or sanctioning new loans.
The lack of due diligence before and after loan disbursal is the single most significant contributing factor to the fraud apart from defaults, economic slowdown, and tax lending practices.
Using disruptive technologies such as artificial intelligence (AI) and machine learning (ML), firms can detect suspicious transactions in loan accounts on a real-time/near real-time basis, which will positively contribute to the overall health of the credit portfolio.
A comprehensive approach to NPA management that includes preventive and curative actions across the credit life cycle is required. Automated decision-making, early warning systems, and sourcing external credit data can further strengthen lenders’ existing credit origination and appraisal process and drive lower NPA formation.
Need for an Early Warning Solution (EWS)
As the government and regulatory bodies have already been pressuring banks to bring down the NPA levels, an all-inclusive NPA management solution with Early Warning Signals can be their lifesaver. It not only helps in dealing with the existing NPAs but also prevents future loans from becoming delinquent by embracing numerous precautionary ways.
A comprehensive early warning framework that includes identifying the right customer segment, understanding the data landscape, formulating early warning triggers, and creating a risk mitigation plan can help substantially reduce a firm’s NPAs.
Any standard EWS will track financial, behavioral, and external indicators and raise red flags for any sudden value change in these indicators. It will help the firms to be better prepared to tackle the situation.
AI-based EWS
Today’s early warning systems are more effective in monitoring and detecting red flags and putting firms’ interests ahead of individual interests by measuring and monitoring risks, placing lenders back in control of their data and decisions.
So, how do they work?
The AI-powered EWS systems run on data. With hundreds of thousands of loans impending, it is difficult for banks to monitor individual accounts and automate the loan retrieval system. While loan retrieval could be automated with time, the data signals and the behavioral patterns involved could indicate many inferences. Hence, EWS systems solve the issue by analyzing the data points and monitoring each account to its precision. The real-time account monitoring system is crucial as it could raise red flags when it identifies issues.
EWS digs deep into data like customers’ spending patterns, personal savings, and the overall financial performance of an individual. It, in turn, provides more accurate results and differentiates a “good” borrower from a “bad” borrower.
Effective early warning systems rely on practical tools, mitigating actions, processes, and proper monitoring and reporting.
EWS consists of 3 phases
1. Data integration: Build an EWS platform by combining multiple data sources. A regulatory EWS recognizes borrowers who are at risk of hardship or default.
2. Early warning trigger model using predictive analytics: AI-powered algorithm processes the various data variables and generates triggers. A proprietary advanced analytics algorithm calculates the probability of default of the selected portfolio.
3. Risk Mitigation: An effectively managing workflow system that actions the cases triggered by the system.
The business case for an early warning solution
A comprehensive early warning framework that includes identifying the right customer segment, understanding the data landscape, formulating early warning triggers, and creating a risk mitigation plan, can result in 20-25% reductions in NPAs.
An EWS can:
1. Reduce the possibility of future NPAs
2. Help make effective decisions to limit exposure to default-prone segments
3. Utilize capital effectively
4. Minimize default through regular evaluation of customer portfolios
5. Limit piling up of NPA stocks
6. Maximize loan recovery through timely action
Conclusion
Today’s early warning systems are more effective in monitoring and detecting red flags and putting a firm’s interests ahead of individual interests by measuring and monitoring risks, placing lending firms back in control of their data and decisions.
As the government and regulatory bodies have already been pressuring financial firms to bring down their NPA levels, an all-inclusive NPA management solution with Early Warning Signals can be their lifesaver. It not only helps in dealing with the existing NPAs but also prevents future loans from becoming delinquent by embracing numerous precautionary ways.
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