Rising NPAs pose a serious challenge to the financial sector, leading to weakening of 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.
While gross NPAs of Indian banking sector had declined from a peak of 11.2% as of March 2018 to 9.2% in September 2019, the post-COVID situation might require a closer look to entire credit landscape. Even the RBI's prudent measures to allow extended moratorium periods on loan installments the NPAs are expected to rise to 13%-15% in the coming years. Going forward, Financial institutions will not only depend upon internal data, but also on external data to assess the borrower's repayment ability.
With the external factors playing the causal role, gaps in proactive governance and technological innovation have been identified as a major impediment to pre-empt potential delinquency. Both pre-facto analysis borrowers during loan origination; and post-facto monitoring to regularly assess financial condition of borrowers and changing economic situations, needs a complete overhaul.
From a regulatory standpoint, RBI came up with a framework for early detection, prevention and reporting of financial stress and frauds in 2016. However, the biggest challenge in implementing such measures, remains the availability of data and eventual amalgamation of internal and external data. Some financial institutions have made significant advances on the data front, however, bringing the operational efficiency to manage EWS and recommend actions internally, still remains elusive.
Verisk Financial BAM helps banks not only to solve the data puzzle, with availability/amalgamation of internal and external financial data from different FIs and other external data on the same platform, but also 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 a transparent and auditable manner.
Combine bank's data with multiple sources of external data such as MCA, Ratings, Credit Bureau, Legal, etc., and external financial data (Accounts and Transactions) through Account Aggregators
Holistic data provides a comprehensive view aiding in identifying & assessment of borrower risk.
Monitor loan portfolio and assess borrower financial health through ML model driven risk score.
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.
Create and analyze your custom portfolio on multiple dimensions like industry, product, customer size, etc.
Assess borrower's 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.