How Specified User Fintechs Are Bridging The Data Gap To Accelerate AI Adoption In Lending

The Indian digital lending industry is undergoing a major transformation due to its unprecedented pace of growth. According to recent statistics, more than 200 Mn people have taken out retail loans in a year, with the number growing at a 20% CAGR. The significant rise in the disbursement volume not only exhibits the uptick in the number of borrowers but also demonstrates the emergence of digital lending players in the market.

Many fintech companies are overshadowing brick-and-mortar lending institutions by digitising every aspect of the lending process. This can be attributed to the rapid adoption of Artificial Intelligence (AI) and Machine Learning (ML) models that expedite and enhance the lending process. Given the scenario, new-age lenders are moving from traditional risk models to a data-backed approach to be more relevant in the market. 

A Major Step Towards Addressing Gaps In The Lending Ecosystem

Data is the most critical element for any AI or ML model. In lending, credit bureau data and alternate data become the base for any propensity model for loan origination, preparing scorecards for underwriting, or even creating early warning signals on an existing portfolio.

Hence, data becomes the most powerful and significant force that drives the digital lending industry. In the present ambiguous scenario, the Indian lending industry has flagged several concerns about the dynamics of the data distribution of borrowers among lenders.

India has more than 1.2K active lenders, out of which only 1% have access to advanced data and analytics tools. This creates a significant gap on the supply side as small and mid-sized lenders lose out on the data-driven lending race. The new-age loan origination and underwriting tools, which are accessible only to large-sized lenders, create a huge disparity in data intelligence. Consequently, these lenders have to incur high acquisition and underwriting costs, ultimately leading to high-interest rates for borrowers.

Grappling with an unregulated lending scenario, the Reserve Bank of India (RBI) planned to put a guardrail on the ecosystem. The apex bank announced the appointment of a new set of fintech companies as ‘Specified Users’ of Credit Information Companies (CICs) under the Credit Information Companies (Amendment) Regulations Act, 2021, based on stringent eligibility criteria. These Specified User fintechs get access to credit data, run analytics and help digital lenders make data-driven decisions. 

The appointment of Specified User fintech players has not only regulated credit data distribution but also resulted in more streamlined and secure digital loan processing. 

AI Underwriting Models 

Every year, over 15 Mn ‘New to Credit’ borrowers enter the credit ecosystem. This makes loan underwriting a tricky process for lenders under the existing conventional models. Every customer or borrower has unique financial circumstances, which bring uncertainty many inches closer to making credit decisions.

If an underwriting practice is not backed by data and analytics, it can lead to economic meltdowns for lenders. And that’s where Specified User fintechs come to the rescue, providing lenders with the ability to interpret enormous amounts of data much faster and more accurately than conventional underwriting practices. It equips lenders with AI and ML-backed underwriting models, adding an extra layer of better oversight on how data sets can be used strategically to come up with personalised solutions for each borrower.

Fintech players are among the early adopters of technology. The advent of Specified User fintechs helped lenders to venture into segments that were deemed high-risk by conventional lenders. Simply put, they have been successful in bridging the accessibility gap for underserved lenders, making them ride the wave of AI. 

Predictive Algorithm To Streamline The Lending Process

In practical terms, AI works in an intuitive manner, like predicting defaulted or paid loans. Specified User fintech combines AI algorithms with ML classification mechanisms to create probability models for lenders to have better credit decision abilities. The technologies are applied to improve credit approval, risk analysis and measure the borrowers’ creditworthiness, which further helps small and mid-sized lenders scale with ease. 

Fintech companies that are recognised as Specified Users have competencies to store huge amounts of credit data and build AI and ML models on structured and unstructured data sets. This provides more streamlined and better insights for borrower segmentation, predicting loan repayment, and building better collection strategies. Besides this, Specified User fintechs are helping lenders to be on top of automation whether in loan underwriting or pricing for personalised offerings. 

Similarly, lenders’ ability to recognise early warning signs proves to be extremely beneficial for credit risk management. Recognised by the RBI, lenders can be certain of the credibility of Specified User Fintechs in terms of data and analytics. 

Specified User fintechs rely on intuitive but data-backed behaviour that detects any suspicious borrower and flags it as fraud. Unlike traditional tools of analysis, it can alleviate the possibility of human errors arising from biases, discrimination, or exhaustive processing practices. By utilising NLP (Natural Language Processing), lenders can accurately generate warning signals instantly. 

Final Thoughts

The landscape of digital lending in India is evolving. Lenders can benefit from data hygiene performed by AI and ML infrastructure established at the end of the Specified Userfintech. 

Lenders are empowered to improve customer experience, leverage predictive analysis, improve risk assessment, improve credit decisions, and break down sales bottlenecks by automating and centralising all significant practices.

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