The consumer lending business is centered on the notion of managing the risk of borrower default. Non-performing assets are one of the major challenge lenders face. In this current unprecedented situation with the pandemic, real-time monitoring of transactions in the loan portfolio will provide yet potent ammunition for lenders to keep the risk profile of the credit portfolio, reeling under the impact of the situation, under check. Inconsistent or unreliable approaches to credit analysis expose firms to unnecessary risks. Lenders must be able to assess the risk of default for each customer so that they can decide to whom the offer should be granted, or actions should be taken.
Unfortunately, accurately estimating the credit risk of a borrower is the most challenging task many lenders face. Lenders need a comprehensive approach to non-performing asset management.
Technology intervention in the form of Early Warning Systems help financial institutions to intelligently map and monitor flow of funds and prevent frauds
Need for an early warning solution
A comprehensive early warning framework that included identifying the right customer segment, understanding the data landscape, formulating early warning triggers and creating a risk mitigation plan can help substantially reduce firm’s NPAs.
The lack of due diligence before and after loan disbursal is the single largest contributing factor to the frauds apart from defaults, economic slowdown, and lax lending practices. With the help of disruptive technologies such as artificial intelligence (AI), machine learning (ML) and streaming, data- lenders will be able to “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.
An early warning solution can help firms:
- Reduce the new NPA flows( and the resulting reduction of NPA in stocks)
- Maximise the recovered value and reduce the exposure at defaults with timely alerts
- Better utilize capital
Best foot forward
Today’s early warning systems are more effective in not just monitoring and detecting red flags, but also putting firm’s interests ahead of individual interests by measuring and monitoring risks, placing banks back in control of their data and decisions.
So, how do they work? Early Warning Systems rely on multiple data sources to measure and monitor risks. There are four distinct transformative components to the whole system of extracting actionable insights from disparate data:
- Collating data from multiple touch points
- Cleansing, validating, and restructuring data into valuable information
- Algorithmic processing using next-generation technologies and data modelling to generate insightful early warning signals/alerts
- Case management by channeling
Early Warning Systems that mine and understand credit-related data – both transactional and external touch points – could have detected and flagged anomalies ahead of time. This gives firms an inkling of inadequate governance in their hierarchies, transactions and customer accounts, before it becomes too late or too difficult to launch a recovery.
Now not only are lenders and financial institutions taking an active interest in governance and monitoring, they are going the extra mile by ensuring that the system is functional and advising additional measures from a regulatory perspective.
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