The importance of credit to a business cannot be overstated, especially in the case of micro, small, and medium enterprises (MSMEs), which often operate with limited means. Although much effort has been made over the last decade to boost the availability of loans to MSMEs which are an integral part of the Indian economy, there is still a massive credit gap that needs to be filled.
Only about 15 per cent of the credit demand of Indian MSMEs is met by formal credit, according to a recent study. That’s hardly encouraging news for a country that aspires to be a $5 trillion economy in the next couple of years. Considering that India’s 6.3 crore MSMEs generate 29 per cent of the country’s GDP, account for 40 per cent of exports, and employ over 12 crore people, their need for timely access to credit at reasonable interest rates cannot be ignored any longer. Here is where financial technology powered by artificial intelligence (AI) and machine learning (ML) comes into play.
The advent of AI and ML is revolutionizing the digital lending landscape in India, making it more flexible and accessible for MSMEs. Banks and NBFCs perceive MSMEs to be high-risk clientele for a variety of reasons, the most important among them being that many of these enterprises, particularly small and micro units, are not adept at bookkeeping and other documents as required by formal lenders for loan processing and underwriting. Another impediment is the lack of capacity of these units to provide adequate collateral in order to obtain financing.
Inability to demonstrate creditworthiness to potential lenders prevents small businesses from obtaining formal credit, and a lack of credit and repayment history makes them even less eligible for loans. Hence, they rely heavily on informal sources of credit such as local moneylenders, which can be expensive and exploitative. AL and ML are helping to break this vicious cycle in various ways.
Automating loan application process
AI and ML can be used to automatically assess loan applications and identify which ones are likely to be repaid on time. By using AI to analyze and process customer data, banks and other financial institutions are able to make more informed lending decisions. This automation also reduces the processing time for loan applications. Loan application processing for MSMEs has seen a transformation since the implementation of GST. There have been proactive reforms using various digital infrastructures/platforms that have transformed the way MSMEs are financed. These infrastructures/platforms are now progressing to the next stage of automation, with banks moving to wherever MSMEs are, rather than MSMEs coming to bank branches.
Automated underwriting: This allows lenders to offer loans to a broader range of borrowers, including those with limited credit histories. By analyzing customer data and using predictive modelling, lenders are able to assess the creditworthiness of small businesses and offer loan terms that are tailored to their specific needs. This can make it easier for MSMEs to access credit and manage their debt, helping them to grow and succeed. Underwriting had limitations in the past regarding data availability and therefore it was done at bank branches with manual intervention to check the authenticity of data and then take the credit call. Because the majority of the data required for underwriting is now available digitally, technologically advanced banks and financial institutions are transitioning to an automated underwriting process, which has resulted in improved asset quality.
Alternative scoring models: AI-powered lenders use advanced data analytics to assess the creditworthiness of borrowers, which has helped them approve more loans and offer better rates to MSMEs. To make loan decisions, these models examine a wide range of data, including financial and non-financial data. Financial institutions can use these models to consider aspects such as transaction history, supplier and customer relationships, and cash flow, which can provide a more comprehensive perspective of an MSME’s creditworthiness. While scoring models were built only o credit information in the past which would not show the real 360-degree information of MSMEs, the future scoring models will be based on multiple data points such as GST, income tax, bank accounts, and many other data formats.
Personalizing lending experience: By analyzing customer data and using machine learning algorithms, lenders are able to tailor their products and services to the specific needs and preferences of individual borrowers. This can help to create a more relevant and personalized lending experience for MSMEs, which can increase customer satisfaction and loyalty. AI and ML can also be used to develop customized repayment plans that consider a borrower’s specific circumstances. This would make it easier for borrowers to repay their loans on time, reducing the overall default rate. Through conversational commerce tools such as voice assistants, chatbots, and user-friendly interfaces, financial institutions can offer 24/7 customer service and make it easier for MSMEs to access information and apply for loans at any time.
Fraud detection can be done more effectively with the help of AI rather than traditional methods. This would protect lenders from losses due to fraudulent loan applications and help ensure that only genuine borrowers receive financing.
Thus, by leveraging the power of AI and ML, lenders, in collaboration with FinTechs and digital platforms, are able to improve the lending experience for MSMEs and help them succeed in the rapidly-evolving digital economy. The digital lending market in India was estimated to be worth Rs 2.7 trillion as of March 2019 and is expected to grow up to Rs 15 trillion (at a five-year CAGR of 41 per cent), accounting for nearly 16 per cent of retail lending in FY24. This heralds a huge promise for MSMEs which have been traditionally underserved by financial institutions. MSME lending is poised for a complete transformation, and soon we will see that these enterprises will almost stop going to bank branches for any financing requirements.