The Role Of Advanced AI In Digital Lending Business
Innovations in technology continue to disrupt the financial services sector and have changed the way consumers engage in financial transactions.
Consumers can now make online payments, transfer funds, make investments, and seek loans seamlessly through digital platforms, anywhere, anytime, through a device of their choice. According to Statista, in 2021, the total transaction value in digital payments globally was US$ 7.52 trillion, which is expected to grow to US$ 8.49 trillion in 2022.
Global digital lending market is on a fast growth track
Another fast-growing segment in the fintech space is digital lending, which refers to bank-independent loan allocation for MSMEs and SMEs, as well as personal loans to individuals. The loan amounts are usually smaller than the traditional loans allocated by lenders such as banks. Borrowers can apply for loans through the online platforms or apps of digital lending companies. The global alternative lending market is projected to reach US$ 361.30 billion in 2022 and likely to continue to grow at a CAGR of 2.45% to reach US$ 407.80 billion by 2027. The digital lending market in India is also growing exponentially, expanding from about US$ 9 billion in 2012 to nearly US$ 110 billion in 2019.
Artificial intelligence is transforming the digital lending business
Artificial intelligence (AI) and machine learning (ML) play a central role in digital lending platforms as they improve data analysis to make their credit risk assessment more efficient. Leading digital lending platforms use AI and ML to analyze large volumes of data to make informed and data-backed lending decisions, and to detect fraudulent applications, potential defaulters, as well as good customers who can be targeted for cross-selling and up-selling of other offerings.
Traditional lenders typically used a prospective borrower’s financial data to assess creditworthiness. However, with the advent of technology, specifically AI models, lenders can use a wide variety of data including customer’s digital behavior, social media profiles, digital payments data, and a host of other data points to evaluate creditworthiness with greater accuracy. Since AI models can ingest large volumes of structured and unstructured data from disparate sources, they can create credit scores in real-time, enabling credit managers to make better informed and explainable decisions.
Fuel business growth with quality leads and improved conversion rates
The AI model not only powers data-backed credit decisioning, it also helps create user personas, which play a useful role in identifying similar applications in the future. For instance, a positive user persona would refer to an ideal borrower who is most likely to be approved for a loan. These user personas help businesses fine-tune their marketing and outreach campaigns, and can be used to precisely target good customers, thereby improving the quality of leads. Further, the ongoing analysis of a greater number of customer applications and the data therein, the AI model is able to update the positive user persona. These continuous updates further enhance the quality of leads to pursue.
(The given article is attributed to Gautam Sinha, CEO, LTFLoW)
