The retail loan industry in India is growing rapidly. Compared to March 2021, the retail loans increased by 50% in March 2022. NBFCs are mainly responsible for this huge surge in the growth of personal lending. Issuing credit cards also grew by 13%. Lenders are more willing to increase loans and credits mainly because of the change in the credit underwriting process. From March 2021 to March 2022, ₹24 crore loan was disbursed. Traditionally, credit limit evaluation is a manual process. People looked at income slips, loan repayment receipts, and other documents to evaluate the credit limit of a person. This type of evaluation was heavily biased and based on the individual’s perception of lending. For example, two businesses in Delhi and Jabalpur would not get the same credit limit even if their income and financial status is the same. Geographical locations too played a critical role in credit limit because someone from Delhi will get a higher credit limit than someone from Jabalpur. Modern and technology-based credit evaluation approach takes the guesswork out of the process. Standardized metrics are used and numerous different financial parameters are considered for approving credit limits. Now, people in second-tier cities will also have access to higher credit limits, irrespective of where they are located. Fintech companies use advanced automated APIs to evaluate creditworthiness solely based on financial status. This has allowed financial institutions to offer loans symmetrically throughout. Credit Risk Evaluation Benefits For Banks Credit risk evaluation models that rely on historical data are inaccurate and outdated. Improved and streamlined evaluation models are the need of the hour for banks and NBFCs that are competing to improve loan offerings. Automated evaluation models can predict customer behaviors and tap into new data sources. It opens up new market segments, increasing the reach of financial institutions. Revenue increase – When the new dynamic model of credit underwriting is employed, data from multiple sources are fetched to calculate creditworthiness. Based on new models, banks can increase their revenue by 5% to 15%. This is possible by lowering the cost of acquisition, increasing acceptance rates, and offering a good customer experience. Reduce credit-loss rates – If the evaluation model can predict and pick out customers more likely to default, banks can reduce their credit losses by 20% to 40%. This can greatly help banks to improve their capital and diversity service offerings. Improved efficiency – Automated data extraction, evaluation ML models, and case prioritisation can improve banking efficiency by 20% to 40%. Low-risk cases can be processed quickly. High-risk cases are analyzed more thoroughly based on improved evaluation models. How To Implement New-Age Credit Evaluation Models? Fintech in the banking sector involves automating banking processes to create an agile environment. The best way to develop a credit evaluation model is to expand data sources and mine existing data to find credit signals. Use A Modular Architecture Credit risk evaluation varies on a case-by-case basis. Hierarchical architecture is not suitable anymore. Modular architecture for credit risk evaluation models allows fintech companies to add or remove data sources based on the creditor risk. Using this model, banks can gather financial information from multiple data sources and assign scores based on the importance of data to evaluate creditworthiness. Gather Data From Multiple Data Sources The decision-making model should have a predictive analysis capacity to evaluate credit risk. The future risk of a creditor can be evaluated using historical data. Apart from traditional data, use non-traditional and external data for underwriting. Use Data Mining To Identify Credit Signals While the data sources are useful, the true potential of credit risk evaluation models can only be unlocked with proper data mining tools. Data from multiple sources must be pulled together, compared and analysed to identify credit signals. Modern-day ML models can take incomplete or partial banking data and predict the customers’ outlook for categorisation. This helps in segmentation based on geographical location, past financial history, etc. Leverage Fintech Expertise While credit risk evaluation is crucial, NBFCs need not spend their resources building a brand-new credit risk evaluation model. They can leverage the technical and cloud expertise of fintech partners to build upon the evaluation model they already have. As the new models are modular, banks can determine the type of evaluation modules necessary based on the target market.
Tag: risk
7 Types of Risk Management You Must Know About
The COVID-19 pandemic has caused major disruptions in banking operations. Consumers have become more demanding and so, risk management must be robust. According to leading banking professionals participating in the Deloitte Banking Risk & Regulatory Academy, financial institutions must focus more on credit risk management. The banking structure must restructure and be prepared for forbearance. Apart from commonly known risk management strategies, leading banks also focus on Environmental, Social, and Governance (ESG) by improving data management strategies and analytics. Moving to the cloud is the next inevitable step to navigating complex risks and ever-changing regulatory requirements. Types Of Risk Management Risk in the banking sector refers to unplanned incidents with major financial consequences, such as reduced or lost earnings. Risk management involves establishing a series of protocols and multi-step procedures that can precisely and accurately mitigate risks. Risk management planning should help financial institutions to recognise threats, assess the damage, and take control measures to prevent risk and minimise the damage. 1. Liquidity Risk Management Banks must safeguard long-term asset funding using short-term liabilities. Funding risk for banks increases when the net outflows increases. This can be due to the non-renewal of different types of retail and wholesale deposits or unexpected withdrawals. Funding institutions must also be prepared to deal with time risk when the expected fund inflows are delayed. Risk management is essential when the non-performing assets increase. Call risk happens when contingent liabilities crystallise, and no viable business opportunities are available. 2. Interest Rate Risk Management Determining the right interest rate that is beneficial for the banks and also for the customers is always challenging. NBFCs that offer lower interest rates to beat the competition must be careful because the adjustments must not result in reduced Market Value of Equity (MVE). The interest rate risks can affect the banks’ earnings and the economic value of the off-balance sheet. 3. Market Risk Management Market fluctuations can lead to market risk when the mark-to-market value of trading portfolios goes down. Also called price risk, the market risk can dramatically increase when the transactions have to be liquidated. Different factors, such as volatility in commodities, equities, currencies, and interest rates, can influence market risks. 4. Credit Risk Management As NBFCs try to capture market share by disbursing more loans to underserved markets, their credit risk increases dramatically. The NPA level of the Indian banking system is high. When the borrowers fail to fulfil their obligations, the counterparty risk and country risk for banks increase. Loan portfolio management and detailed evaluation of borrowers are crucial to managing credit risks. 5. Operational Risk Management Banks risk a huge financial loss when internal processes and systems fail. Global financial links have increased as the banking and financial sector adopt automation. As a result, the potential for operational risks also increases. Transaction risk can result in failure in business continuity. Compliance risk can affect the integrity and credibility of banks. 6. ESG Risk Management ESG risk is the new age risk for financial institutions as they have to comply with inclusion and diversity policies. The pressure from multiple governments to contribute to climate change policies also affects investment value for the banks. Proactive risk management using models that integrate ESG and climate data must be used. 7. Reputational Risk Management In the highly competitive banking sector, reputational risk can result in a loss of trust by customers and stakeholders. This risk can be caused by poor customer service, corruption, and fraud. Banks can prevent class-action lawsuits and other punitive damages with proper reputational risk management. Mitigate Risks With Risk Management Framework Banks and financial institutions must build a robust and scalable risk management model. The framework should include all risk parameters with adequate risk grading. The framework should be updated continuously based on updated risk tolerance levels. The model risk management framework must be built into banking operations for effective risk mitigation.