The role of advanced AI in the digital lending industry


The AI ​​model not only enables data-driven credit decisions, but also helps create user personas, which play a useful role in identifying similar apps in the future.

Technological innovations continue to disrupt the financial services industry 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 the device of their choice. According to Statista, in 2021, the total transaction value in digital payments worldwide was US$7.52 trillion, which is expected to reach US$8.49 trillion in 2022.

The global digital lending market is on a rapid growth path

Another fast-growing segment in the fintech space is digital lending, which refers to independent lending to banks for MSMEs and SMEs, as well as personal lending to individuals. Loan amounts are generally lower than traditional loans from lenders such as banks. Borrowers can apply for loans through online platforms or apps from digital lending companies. The global alternative lending market is expected to reach US$361.30 billion in 2022 and is expected to continue growing at a CAGR of 2.45% to reach US$407.80 billion by 2027. India is also growing exponentially, growing from around US$9 billion in 2012 to nearly US$110 billion in 2019.

Artificial intelligence is transforming the digital lending industry

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, data-driven lending decisions, and to detect fraudulent apps, potential defaulters, and good customers that can be targeted for cross-selling and up-selling. other offerings.

Traditional lenders typically used a potential borrower’s financial data to assess creditworthiness. However, with the advent of technology, particularly AI models, lenders can use a wide variety of data, including digital customer behavior, social media profiles, digital payment data, and a host of other data. other data points to assess creditworthiness with greater accuracy. Since AI models can ingest large volumes of structured and unstructured data from disparate sources, they can create real-time credit scores, allowing credit managers to make better-informed and explainable decisions.

Boost your business growth with quality leads and improved conversion rates

The AI ​​model not only enables data-driven credit decisions, but also helps create user personas, which play a useful role in identifying similar apps in the future. For example, a positive user personality would refer to an ideal borrower who is most likely to be approved for a loan. These personas help businesses refine their marketing and outreach campaigns, and can be used to precisely target the right customers, improving the quality of leads. Moreover, through the continuous analysis of more client applications and the data they contain, the AI ​​model is able to update the positive personality of the user. These continuous updates further improve the quality of leads to pursue.

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