What AI Could Be in Lending in the Future

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tanjimajuha20
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What AI Could Be in Lending in the Future

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This was reported by Oleg Skvortsov, Chairman of the Board of the Association of Russian Banks (ARB), on February 29 at an open discussion "Artificial Intelligence in the Banking Sector". "We asked the NBKI to provide a vision of the situation. In their opinion, the use of neural networks has improved the scoring that the NBKI now uses. This model allows us to predict when a loan will be overdue by more than 90 days," he added.

In addition, according to benin whatsapp number database ARB, AI helped ARSfin LLC (financial portal Bankiros) increase the loan approval rate to 7.5%; thanks to machine learning (ML), the speed of data processing and the accuracy of profile assessment increased - the average number of calls to a client before the first approval decreased by half in a month according to the results of ML testing in Bankiros in October 2023.

"The banking sector is one of the most advanced in terms of AI capabilities. AI is capable of processing a large number of invoices and identifying patterns between them. AI technologies improve the accuracy of creditworthiness assessment, which improves the quality of loans, reduces the number and volume of delinquencies, which ultimately helps prevent defaults," explains ARB President Garegin Tosunyan.

"Machine learning models can quickly adapt to changes in the market or in borrower behavior, which allows for a more flexible response to changing conditions and minimize risks; in addition, more accurate and faster decisions in credit scoring allow banks to offer customers more personalized lending terms and improve the overall service experience," says Yulia Vdovina, Director of Digitalization of Client Service in Remote Channels at BSS LLC.

Banks should be mindful of potential biases or discrimination that could arise when using incorrect data or features to predict creditworthiness using AI, she said.



"In the future, banks will use AI to identify hidden patterns to more accurately determine the creditworthiness of borrowers. This includes not only financial data, but also data from various sources, such as social networks, online purchases and behavioral analysis," says Yulia Vdovina. "An important aspect will be the use of AI to detect and prevent fraudulent activities in lending: this includes analyzing customer behavior and identifying abnormal patterns.
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