AI/ML-powered Credit Monitoring Systems rely on regulator prescribed EWS methodologies
Across both the public and private sectors in India, banks are yet to overcome the challenge of bad loans. In the financial year 2021, public sector banks in India reported a total of over INR 6 trillion in gross non-performing assets (NPA). This was a decrease from INR 7.3 trillion Indian rupees in 2019.
In contrast, private sector banks reported an increase from INR 1.8 trillion in FY 2019 to INR 2 trillion in FY 2021 in gross NPAs.
The slight improvement of the banking stability indicator (BSI) in September 2018 was due to improved asset quality, but it had also been noticed that profitability has eroded further.
Further, the banking sector also observed a delay in detecting and reporting such frauds to the regulators. Due to such occurrences, the banking sector has been under pressure from regulators to adopt technology-driven proactive measures to tackle the menace of NPAs and loan frauds.
A Revolutionary Approach to NPAs: Early Warning Systems
Keeping frauds in mind, the RBI proposed Early Warning Systems (EWS) to depict beforehand, whether accounts are becoming NPAs or not. In support of this thought, D2K Technologies offers a befitting EWS product.
CRisMac Early Warning System: A set of automated processes for identifying risk at a nascent stage in the bank's loan portfolio. The rule-based solution identifies borrowers at risk of distress or default well before they actually default. A host of Early Warning Signals are identified from data of systems and processes already implemented in banks.
Early warning signals are categorized into – transactional, financial, non-financial, external, and statistical indicators – to identify emerging problems in credit exposure at an early stage. These consist of real-time business data from internal transaction activities, submitted financials, and big data from external factors such as forward-and-backward sector linkages, ESG data, news, social media, and more.
The automated rule-based EWS identifies the traces of potential stress/default long before the occurrence of actual default, enabling the bank to initiate timely remedial measures. Hence, EWS facilitates the bank in strengthening the health of the bank’s credit portfolio.