Addressing Low-cost Credit Challenges of MSMEs with AI and ML

The advent of automation tools like AI and ML assist contemporary lenders in identifying borrowers' credit risks by meticulously analyzing data

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MSMEs are considered the backbone of the economy and play an instrumental role in the socio-economic development of a country. India has approximately 6.3 crore MSMEs as of mid-2021 according to IBEF. However, there are innumerable challenges that MSMEs have to face due to the lack of proper analysis of alternate data sources. One such challenge includes access to low-cost credit.

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Accessing credit from traditionally risk-averse lenders like banks and Non-Banking Financial Companies (NBFCs) is becoming increasingly tricky as lenders are keen on reducing the risk of Non-Performing Assets (NPAs). MSMEs, especially first-time entrepreneurs, often do not hold formal records and credit history, thereby failing to furnish sufficient collaterals required by traditional lending institutions. This hampers their low-cost credit disbursal, specifically to small businesses.

However, in the wake of COVID-19, digital transformation and innovation in futuristic financial technology have soared high. The advent of automation tools like Artificial Intelligence (AI) and Machine Learning (ML) assist contemporary lenders in identifying borrowers' credit risks by meticulously analyzing data from alternative sources like a digital footprint. This is particularly beneficial for thin-file borrowers with insufficient formal documentation as it helps lenders assist borrowers' creditworthiness without relying on traditional methods. On the back of AI, ML, and Big Data, lenders build comprehensive credit-risk profiles of potential borrowers to reduce NPA risks seamlessly.

Automation tools: A shining dawn for MSMEs

AI and ML greatly assist MSMEs in addressing low-cost credit challenges. Generally, the cost of credit for MSMEs is high due to a lack of collateral. The inability of banks to lend below a certain amount of loan results in formal credit penetration to Small and Medium Businesses (SMBs) that languishes at less than 15%. High friction costs turn into costlier borrowing costs for SMBs, and business drivers find it challenging to understand due to inadequate information and inconsistent financial data.

However, these challenges can be addressed smoothly with the help of automation tools like AI/ML. Here are a few ways:

  1. Businesses can use their private GST, bank data under consent as information collateral that can help draw better insights on their credit capacity along with public data like litigation data, compliance filings like Ministry of Corporate Affairs (MCA) services ,Employee Provident Fund Organisation trends (EPFO), and court cases.
  2. AI/ML algorithms support risk deaccessioning by distilling actionable insights from disparate sources of data.
  3. With the help of RPA (Robotic Process Automation), data sourcing infrastructure can be set up to get the coverage of all the business entities with a digital footprint.
  4. Trust Score, a proprietary scoring algorithm, can be designed to assess the health of MSME.
  5. Lenders can be equipped with ML-based Early Warning Systems (EWS) to monitor a business.
  6. Risk scoring models can help make better business decisions and consistent underwriting.
  7. Lenders can digitize and implement solutions at a low cost with the help of zero human touch solutions, and protocol-driven open Application Programming Interfaces (APIs) coupled with workflows. It assists in reducing the overheads costs.
  8. AI/ML technologies can equip lenders with novel capabilities to underwrite thin-file borrowers.

Moving Forward

Besides solving the aforementioned challenges, lenders can utilize plenty of alternative data sources for underwriting. With the transparent practice of sharing data, both businesses and banks benefit. Since many business owners often avoid sharing their private data with banks or lending institutions, small businesses have to suffer due to the uncertainty associated with lending to small businesses, resulting in high-interest rates. Banks are gradually becoming more comfortable revising the rates at which the credit is extended by using informational collaterals.

According to the Central Statistics Office, MSMEs contribute approximately 30% of India's Gross Domestic Product (GDP), making it pivotal for the country's economic development. A smooth financing or lending procedure for the sector will also ensure the nation's financial inclusion. Therefore, lending institutions must adopt these cutting-edge tools and expand their lending criteria.