The past decade has seen a steady rise in Non-performing Assets (NPA). Rising NPAs pose a serious challenge to the financial sector causing adverse effect on profitability and credit growth. NPAs could be a result of negative business results, fraudulent behavior or adverse macro-economic condition, however, the impact of such NPAs pose a direct threat not only to financial institution’s top/bottom line but also its reputation.
Even though the gross NPAs of Indian banking sector has seen a decline from a peak of 11.2% in March 2018 to 9.2% in September 2019, the post-COVID situation demands for a closer scrutiny on the entire credit landscape(RBI Financial Stability Report 2019). In the coming years, the NPAs are still expected to rise to 13%-15% even with RBI’s prudent measures to allow and extend moratorium periods on loans(RBI Financial Stability Report 2020). In the future, the financial institutions will be forced to depend on external data in addition to the internal data to assess the borrower’s repayment ability.
With the external factors playing a causal role, it has been found that gaps in proactive governance and technological innovations as the major hindrance to pre-empt any potential delinquency. The pre-facto analysis done during the loan origination and the post facto monitoring to regularly assess the borrower’s financial situation will need a major overhaul in order to cope with the changing times.
In 2006, RBI came up with many measures for early detection, prevention and reporting of financial stress and frauds. But the availability of data and the eventual amalgamation of internal and external data remains as a big challenge. In some financial institutions, significant advancements are made in the data procurement front, but still lacks the operational efficiency to continuously manage borrower assessment, regularly monitor risk and take necessary actions internally.
Verisk Financials’ Early Warning System (EWS) helps financial institutions collect data, both internal and external on the same platform. Additionally, it is possible to incorporate a complete workflow driven solution for collaboration across the hierarchy and business units. Users can continuously monitor risk profiles of customers through deeper analysis of the underlying contributing factors derived from the holistic data using advanced machine learning models. A recommendation engine also helps to flag accounts for corrective actions in a transparent and auditable manner.
Combine bank's data with multiple sources of alternate external data data such as company master data, ratings, credit bureau, legal, newsfeed & social media info etc. and external financial data (accounts and transactions)
Holistic data provides a comprehensive view aiding in identifying & assessment of the company risk
Monitor loan portfolio and red flag EWS based on the risk profile of customers on an ongoing basis.
Continuous self-learning of new emerging patterns through Machine Learning models.
Flexible data sharing strategies and deployments options.
Lower operational overheads and significant cost benefits.
Monitor loan portfolio and assess company financial health through ML model driven risk score
Pre-packaged EWS published by RBI and DFS along with ability to add custom EWS
Configure key parameters such as weightage, threshold, etc. to suit your own needs
Configurable internal workflows to assign, track and audit actions
Continuous monitoring through alerts and reminders
Cloud deployment for cost optimization