What is alternative data, and can it help improve lending decisions?

MSMEs are the heartbeat of economic vitality, fueling 30% of our GDP and generating a wealth of job opportunities. Recognizing the growing importance and number of MSMEs in the sector, now numbering 64 million, the government has introduced initiatives like Udyam to boost credit growth and provide incentives to support the industry. Despite this, they still face major challenges, particularly when accessing finance. The sector has an alarming $530 billion credit gap, primarily due to traditional lending methods of data collection.

These methods typically rely on reviewing bank statements and financial documents to assess a borrower's risk and creditworthiness. However, this approach doesn't work well for MSMEs with little or no credit history, known as 'thin file' borrowers. Consequently, alternative data models that use innovative credit scoring mechanisms are emerging to fill this gap. Using alternative data in lending means utilizing a set of structured and unstructured data points to assess the business's creditworthiness.

This data ranges from digital footprints, online behavioral data, and digital payment information to business performance metrics and telecom data. Lenders can use this data to gain deeper insights into MSMEs' behaviors and needs. This, in turn, helps them design customized financial products and connect financing with the energy, commerce, and health sectors. Beyond this, lenders also spot trends that can expand their offerings, turning these insights into new opportunities.

Trends and trials in MSME Lending

Disruptive technologies, particularly AI and machine learning, are revolutionizing lending decisions. The global AI in lending market is set to grow at a 7% CAGR, and India is swiftly embracing this trend. Lenders are now using AI-powered models that analyze diverse data sources to better understand the MSMEs' financial health. This enables them to offer more flexible credit products tailored to each business's specific needs. For example, an MSME in the retail sector can benefit from loans with adjustable repayment terms based on seasonal revenue fluctuations, ensuring they have the necessary funds during peak periods.

Adding to this, the increasing digital footprint of MSMEs offers valuable, real-time, and verified data crucial for evaluating their ability and willingness to repay loans. Many new fintech lenders capitalize on this by using MSMEs' digital data, customer insights, and advanced analytics to refine their business models. This approach adds an extra layer of alternative data to traditional credit assessments. These lenders also address the gap left by traditional lending sources, which only meet $289 million of the total $1,544 billion debt demand. By integrating AI and digital data, they are making strides in bridging this significant credit deficit.

Given this, the use of alternative data comes with its own set of challenges that must be managed to fully realize its benefits. One key challenge is linking and aggregating data from various sources for a specific individual or MSME. Unique identifiers are required here to accurately and affordably match data to the right person or business to do this effectively. There are also concerns about potential issues like discriminatory practices, data privacy, and transparency. These concerns underscore the need to ensure that systems using alternative data are fair and don't produce biased or arbitrary results. Although challenges persist, the advantages of incorporating these data sources into credit evaluation models cannot be ignored.

Finally, the shift from raw data to meaningful insights isn't just about using technology; it's about reimagining how to meet the financial needs of MSMEs. With abundant alternative data sources, there are exciting opportunities to create a more inclusive financial space. As this new era of lending emerges, stakeholders must understand and address the obstacles of integrating various data sources, ensuring that lending decisions are quick and accurate to drive financial progress.