Financial independence and accessibility are unarguably the two key pillars of India’s growing and modern economy. The evolving FinTech ecosystem of the country is enabling individuals and businesses to have easy and faster credit access, tailored to their needs, to facilitate this growth. While credit enables individuals to purchase, it allows MSMEs to cater to this demand and thus create jobs in the economy. However, access to formal credit, especially to MSMEs, has been a challenge for loan providers. This can be attributed to the lengthy loan disbursal process and assessment of the credit risk worthiness.
Often, lack of proper documentation hampers this process. High costs of financial products and services and low penetration of banking facilities in the semi-urban and rural India is another problem plaguing the lending industry. According to PwC, in 2019, the overall demand for both debt and equity finance by the MSMEs stood at around ₹87.7 trillion. Out of this, the demand for debt alone accounted for ₹69.3 trillion and only 16 per cent of the total MSME debt was catered to by formal sources.
While Digital Lending has been able to bridge a few gaps, there are still some areas such as risk worthiness, product customisation for different market segments and availability of formal credit providers that need attention. Earlier this year, RBI constituted the Working Group to making lending more fair, safe and inclusive. FinTech innovations are now focusing on using cutting-edge technologies to override these hiccups and to make credit available for the unserved and underserved.
So far, traditional asset-based data was being used to determine a borrower’s risk appetite and future requirements; loan providers are now leveraging surrogate data resources such as social media, telecom activities, spending patterns along with psychometric analyses to serve the credit-invisible customers.
Expansion and customisation of services
The customisation and personalisation of financial product and services for the next billion users are still at a nascent stage. Formal credit facilities are yet to fully serve the needs of smaller businesses and untouched customer pockets across the Indian hinterland. With increased smartphone penetration and availability of low cost internet tariffs, there is an increase in customer touchpoints and hence, creation of large pool of structured and unstructured data. By leveraging alternative data sources and data analytics, credit providers can cast the web of their services wide and bring more people into the formal credit system. Further, alternative underwriting data can also help the lenders weigh the creditworthiness of the borrower without having to go through thick case files.
Lenders, if possible, can even refer to the existing and potential borrower’s personal milestones such as marriage, birth of child, change in cities, new employment or retirement, to customise their offers and pitch new products to them.
Assessing risk worthiness
Historically, the credit worthiness of a borrower has been assessed based on the mandatory documents he or she would submit as part of the application process. Often those in the rural areas are unable to furnish their records and hence denied access to formal, forcing them to resort to informal credit channels such as moneylenders, relatives, who charge an exorbitant interest. With the digital footprints of a borrower, especially in the post-pandemic world, utilising non-conventional sources of information to analyse the credit scoring and repayment capacity of the individual can make affordable credit accessible to the underserved. In the MSME category, alternate sources like PoS information, utility bill records and other fixed business expenses can offer a comprehensive credit risk assessment. This data can even help lenders curate loan schemes based on average ticket size of different customers, across sectors.
Prevention of frauds
Transitioning from the traditional offline lending mechanisms to digital lending comes with its own challenges. The document intensive lending application helped in verifying submitted proofs but with digital lending, acquiring information via online channels can attract fraudsters. With information gathered from social media and other sources can potentially flag fraudulent behaviour or inconsistencies in online behaviour with the submitted application. This can help loan providers to take preventive measures and deploy a deeper verification mechanism for a customer’s credit history to mitigate fraudulent activities.
Alternative data is slowly picking up pace in India’s lending industry but lenders are yet to explore fully. In today’s digitally native world, lenders can fasten the entire digital lending process by leveraging different data sources and insights thereof for identity verification, compliance, credit underwriting and for fraud detection. Adoption of alternative data sets can even help with differentiated product offerings for different retail and enterprise business loans.
The potential of using alternative data for financial inclusion is immense but it also brings with other risks such as inaccuracies in capturing data, AI-bias, exposure to cybersecurity threats to name a few. Therefore, lenders who are seeking to leverage AI and ML to churn the infinite customer data into meaningful insights must ensure that they are deploying all necessary precautions to successfully take digital lending to the last mile customer.
The author is Co-founder and CEO, Decimal Technologies