The credit scoring provided by AI/ML is poised to revolutionize the retail credit and MSME credit lending business, which is growing at a very high rate as borrowers still have no access to formal credit. Risk assessment and financial inclusion are being improved with the help of AI/ML-driven credit scoring models. These innovations are contributing to reducing the credit gap between MSMEs and first-time borrowers to enhance access to loans.
Blockchain and AI agents are also becoming important enablers. There is no Code of technology that is enabling lenders to continuously recalibrate the credit system based on daily learning and serve in the underserved segment. Credit assessment is improving in terms of transparency and security, thanks to blockchain-powered credit records.
Artificial intelligence agents continue to set the standards higher around credit assessment, as well as incentive automation, in terms of efficiency, effectiveness, and customer experience.
Data-Driven Lending Evolution
Another major transformation area is automated sales incentives. The use of incentive automation based on AI/ML is becoming a common practice by lending institutions with a need to optimize salesforce and collection performance. DSAs (Direct Selling Agents), loan officers, and bank sales teams are being motivated by using dynamic structures of commission, real-time tracking, and granularized performance-based rewards.
This is also contributing to an efficient sale of financial products as these institutions continue to be compliant with the changing regulatory changes. Conventional credit scores tend to disaggregate underserved people.
This gap is likely to be filled in the years to come by the use of alternative credit scoring models. The retail credit and MSME lending ecosystems are moving towards alternative credit models to evaluate borrowers with no formal credit history. FinTechs uses transactional data, location data, employment data, and digital spending behavior to offer credit to individuals that goes beyond traditional credit bureau-based evaluations.
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Behavioral Analytics in Lending
AI/ML and behavioral analytics are contributing to this transformation. The AI/ML models recognize the location of intelligence, spending patterns, payment patterns, and psychometric variables to determine the financial discipline of a person. Risk profiling is being used by some lenders to determine the creditworthiness of a borrower and is improving access to loans by underserved groups.
Digital ecosystem embedded credit is also becoming popular. Embedded credit solutions are being integrated by e-commerce platforms, digital wallets, and ride-hailing services to enable users to use pre-approved loans depending on their transaction history.
Meanwhile, the BFSI and FinTech industry is facing new challenges because of the rapid change of regulation related to data privacy, FinTechs and digital payment. The DPDP Act in India has imposed new rules for the strict usage of consent-based data, which compels FinTechs to rearchitect AI-based credit assessment models without violating data privacy rules.
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AI-Driven Finance of the Future
The Digital Lending Framework released by the RBI has further constrained the digital lending by ensuring that only regulated banks and NBFCs are permitted to pay out loans. This is now directly affecting AI-based lending models and incentive-based loans. Also, another area of regulation is the standardization of AI in credit decisioning.
To promote equity and transparency in AI-based credit assessment, the regulators can present a set of guidelines. The solution to the problem of biases in credit decisions and the fact that automated systems do not discriminate against some demographics will be a major concern on the part of FinTechs.
About The Author
Ram Ramdas, Founder and Chief Evangelist at Wonderlend Hubs, is an innovative fintech leader and has extensive experience with over 30 years of educating and working in technology, finance, and innovation. He advocates for the use of no-code lending platforms, AI-driven credit scoring, and the automation of incentive management to more inclusively create access to finance. Driven by simplicity, ethics, and scalability, Ramdas remains an ongoing custodian for the future of digital lending through the lens of transparency and impact.
