The opinions expressed by Entrepreneur contributors are their own.
You are reading Entrepreneur India, the international franchise of Entrepreneur Media.
The importance of credit to businesses cannot be overemphasized, especially for micro, small and medium enterprises (MSMEs), which often have limited means of doing business. Despite many efforts over the past decade to improve loan availability for micro, small and medium enterprises, an integral part of the Indian economy, there is still a huge credit gap that needs to be filled.
According to a recent study, only about 15% of the credit needs of Indian MSMEs are met through formal credit. That’s hardly encouraging news for a country that aspires to become a $5 trillion economy within the next few years. Considering that India’s 6.3 million MSMEs generate 29 percent of the country’s GDP, account for 40 percent of exports, and employ more than 120 million people, their need for timely access to credit at reasonable rates can no longer be ignored. This 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 to MSMEs. Banks and non-bank financial institutions view MSMEs as high-risk customers for a variety of reasons, not the least of which is that many such businesses, especially SMEs, are not adept at bookkeeping and what is required to provide loans from formal lenders. Additional document handling and underwriting. Another hurdle is the lack of ability of these units to provide sufficient collateral to obtain financing.
Failure to demonstrate creditworthiness to potential lenders prevents small businesses from obtaining formal credit, while a lack of credit and repayment history makes them even less eligible for loans. As a result, they rely heavily on informal sources of credit, such as local moneylenders, which can be costly and exploitative. AL and ML are helping to break this vicious circle in various ways.
Automate the loan application process
AI and ML can be used to automatically evaluate loan applications and determine which ones are likely to be repaid on time. By using artificial intelligence 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 of loan applications. Since the implementation of GST, there has been a transformation in the processing of loan applications for MSMEs. There are positive reforms using various digital infrastructures/platforms that change the way MSMEs are financed. These infrastructures/platforms are now moving to the next stage of automation where banks move to where MSMEs are, rather than MSMEs coming to bank branches.
Automatic underwriting: This allows lenders to extend loans to a wider range of borrowers, including those with limited credit histories. By analyzing customer data and using predictive models, lenders are able to assess the creditworthiness of small businesses and offer loan terms tailored to their specific needs. This can make it easier for MSMEs to access credit and manage debt, helping them grow and succeed. Underwriting used to have limitations in terms of data availability, so it was done with manual intervention at the bank branch to check the authenticity of the data and then answer credit calls. As much of the data required for underwriting is now available digitally, technologically advanced banks and financial institutions are transitioning to automating the underwriting process, thereby improving asset quality.
Alternative Scoring Model: AI lenders use advanced data analytics to assess the creditworthiness of borrowers, which helps them approve more loans and offer better interest rates to MSMEs. To make lending decisions, these models examine a wide range of data, both financial and non-financial. Financial institutions can use these models to take into account aspects such as transaction history, supplier and customer relationships, and cash flow, thereby gaining a more complete picture of an MSME’s creditworthiness. Past scoring models were only built on credit information and could not show the real 360-degree information of MSMEs, while future scoring models will be based on multiple data points such as GST, income tax, bank account and many other data formats.
Personalized 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 helps create a more relevant and personalized lending experience for MSMEs, thereby increasing customer satisfaction and loyalty. AI and ML can also be used to develop customized repayment plans based on the specific circumstances of the borrower. This will make it easier for borrowers to pay back their loans on time, reducing overall default rates. With conversational commerce tools such as voice assistants, chatbots, and user-friendly interfaces, financial institutions can provide 24/7 customer service, making it easier for MSMEs to access information and apply for loans anytime.
Fraud detection can be done more effectively with the help of artificial intelligence rather than traditional methods. This will protect lenders from losses caused by fraudulent loan applications and help ensure that only genuine borrowers receive financing.
Therefore, by harnessing the power of artificial intelligence and machine learning, lenders, in partnership with fintech companies and digital platforms, can improve the lending experience for MSMEs and help them succeed in the rapidly evolving digital economy. India’s digital lending market was estimated to be worth Rs 2.7 trillion as of March 2019 and is expected to grow to Rs 15 trillion (5-year CAGR of 41%), accounting for nearly 16% of retail loans in FY24. This bodes well for MSMEs, traditionally underserved by financial institutions. Micro, small and medium-sized loans are about to undergo a complete transformation, and soon we will see that these enterprises hardly go to bank outlets to seek any financing needs.
