A game-changer in the quickly changing world of financial technology is customised financing. Fintechs can fill the gap between conventional credit evaluation techniques and the varied financial needs of contemporary consumers by providing customised loan solutions. This strategy raises customer satisfaction and loyalty as well as acceptance rates and lender commission income. In the end, it increases fintech profitability by cross-selling more goods and services.
The Case for Personalised Lending
Because they can collaborate with different lenders, fintechs have a considerable edge over non-banking financial businesses (NBFCs) and traditional banks. Fintechs are able to provide a wider selection of credit solutions that are tailored to different consumer segments because of this flexibility. Fintech companies can create a portfolio of lending partners to get beyond the restrictions imposed by individual lenders, such as stringent risk tolerance or geographic boundaries, and thereby provide suitable credit products to a larger consumer base.
To match clients with the appropriate credit products, many fintechs do not, however, have a methodical methodology. They frequently send leads to several lenders or provide each consumer an extensive list of lenders, which lowers approval rates and irritates customers with constant follow-up calls.
Understanding the Risk Criteria of Lenders
Every loan partner has different risk thresholds and screening requirements that vary depending on the sector, region, income level, and credit bureau score. Although lenders might not reveal every detail of their lending scorecards, they usually divulge several important limitations. To match customers with appropriate credit products, fintechs need to be aware of these limitations.
Assessing the Customer’s Risk Profile
Fintechs need to precisely determine the risk profile of their customers in order to match them with the right product. Credit bureau scores are generally recognised and are available by gently pulling data from the bureaus. Statements from banks are essential for larger loans. In addition, when used in accordance with RBI regulations and with client authorisation, alternative data sources such GST records, financial statements, account aggregators, and device/SMS data might offer insightful information.
Matching Lender and Customer Risk Profiles
In order to match customers with appropriate credit products, modern fintech systems need to make choices in real time. This necessitates the use of algorithms that can instantly recommend the optimal product fit by matching a customer’s risk factors with the requirements of multiple lenders.
Leveraging AI for Personalised Lending
Fintech companies are depending more and more on AI-powered platforms to customise loans and maximise conversion rates. These platforms combine information from several APIs, such as account aggregators, credit bureaus, GST, KYC, payment histories, and SMS data from devices. The systems provide predictive ratios and triangulations that evaluate risk and fraud potential by combining and analysing this data.
Fintechs can design lending partners’ risk policies with AI platforms’ intuitive interfaces even if they lack IT or coding experience. The policies of each lender can be kept in different configurations, guaranteeing exact compliance with their requirements.
Real-Time Decisioning Engine
Using integrated API connectors, the platform retrieves further data when a loan application is submitted. Following that, it applies the set selection criteria to every lending partner and product. The technology makes real-time recommendations for the best lender and product for the customer based on loan terms and approval probabilities.
Continuous Improvement with AI
These systems enable fintechs to continuously improve their targeting and product recommendations by using AI modules that test various scenarios and quantify their effect on offer take-up rates. The risk policies are suggested to be adjusted in order to maximise approval rates by the AI/ML algorithms, which identify patterns between risk factors and approval outcomes.
Measurable Outcomes
Fintechs’ response and approval rates have significantly increased as a result of the adoption of AI-driven solutions. Leading credit card fintechs in India, for instance, have shown increases in response rates from 1.8% to 22% on pre-approved offers. A year later, several fintech companies have stated that, as a result of real-time decisioning and customised product recommendations, lender approval rates have increased from 22% to 85%.
Final Thoughts
Fintechs can increase loan acceptance rates and enhance financial results for borrowers and lenders by providing customised lending products. Customised loan proposals that fit the financial situation and objectives of potential clients are more likely to be accepted, which raises conversion rates and lowers default rates. Personalised lending also increases customer satisfaction and trust since it makes clients believe that their financial service providers appreciate and understand them. This strategy allows fintechs to cross-sell more goods and services, which increases client loyalty and boosts profitability.
