Robot Lenders: How AI is Changing the Credit Risk and Lending Landscape
Ayan Mahajan
September 25, 2025
Key insights:
- AI is on track to become the engine behind modern fintech lending and credit underwriting.
- Fintech lenders are beginning to use AI to cut costs, make approvals faster, and be able to dynamically adjust credit standards as market conditions shift.
- Resilience and competitiveness will soon hinge on AI adoption, with data-driven lenders better able to scale, control losses, and maintain investor confidence during volatile cycles.
- AI won’t replace humans in the lending field, but will definitely redefine their role; we will most likely see a shift away from a focus on risk assessment and calculations to a focus on higher level decision making based on the analyses and predictions of AI, which means companies that best learn to work alongside AI and accurately interpret AI output to make decisions will have the greatest leverage in coming years.
What is Credit Risk and Fintech Lending?
The flow of money is integral to the economy, and one of the main ways that is sustained is through lending. Borrowing money gives businesses the ability to make important investments, allows people to buy homes and create wealth, enables people to go to college, and is key for a myriad of other purchases and investments that empower people and keep the economy growing. Loan underwriting is basically just the process used by lenders to assess credit risk of a particular borrower, deciding on whether or not to approve the loan.
As compared to traditional banks, fintech lending companies are mostly vertically integrated, which means that they focus on specific sectors as compared to the overall economy. For example, Cherry or CareCredit is focused on healthcare and provides financing options for those seeking medical care. Fintech companies get access to funding through flow partnerships, where they banks and private asset managers buy certain volumes of loans on a regular basis. In this case, credit risk is transferred from the fintech to the flow partner, whether that be a bank or private asset manager. Later, fintechs can access credit facilities where large banks provide them funding on the balance sheets to fund their originating loans, and over time, they build the ability to securitize loans after they have built a strong historical performance of those loans.
Credit risk refers to the potential financial loss that lenders can incur if the borrower fails to repay the loan. When institutions evaluate credit risk they look at two overarching questions: (1) can the borrower pay it back, and (2) will the borrower pay it back? The former determines the ability of borrowers to repay their loans, while the latter determines their willingness. Compared to banks and credit unions which are respectively strict/conservative and focus on community with personal approaches, fintechs focus on efficiency and are a lot more dependent on technology and alternative data for their decision making and lending. Some of the key factors fintechs use to decide who they should lend to or not include, but are not limited to, FICO scores, debt to income ratio, past borrowing info, existing assets, and cash flows/bank transactions.
How AI is changing the Fintech Lending Industry.
Many fintechs are beginning to incorporate AI to improve credit risk evaluation. With the large and increasing amounts of data fintechs have to parse through, AI has the ability to automate many lower level tasks and develop more robust and proficient models to evaluate credit risk. While in the status quo, many lenders are still primarily reliant on conventional methods and stray away from AI, it is slowly being integrated into the industry and forecasts predict its widespread use in the near future.
Source: The Business Research Company
According to Radhakrishnan and Mubashar (2025) from the International Banker, Gen AI can aid with the credit memo creation process by extracting key data and summarizing/analyzing financial performance given their speed and accuracy at such tasks. Furthermore, at higher level judgement roles, Gen AI can act as assistants able to guide executives on different decisions, and help lenders get a deeper understanding of the potential borrower. A major challenge associated with AI use in lending operations, however, is that the models can sometimes hallucinate and return false conclusions and responses, but this problem will be diminished over time as the models will continue to be trained and improved on more data.
Specifically, AI models are being embedded by lenders into underwriting platforms and technology to generate the probability of a borrower defaulting and hence a loss estimate, and is able to assign risk grades using a combination of traditional data, such as current loans, bank transaction details, past defaults, as well as alternative data, such as rent, utility payments, and even more complex things like online behavior which truly creates a more comprehensive profile for each borrower that tells more about them than just their bank account and transaction history.
Source: SoluLab
Apart from making underwriting more efficient and robust, AI integration into lending also bolsters strategic resilience and competitiveness. Due to its speed, underwriting platforms incorporating AI machine learning models can better adjust their approval rates in response to broader economic changes or even changes to relevant sectors alone, and they can tighten underwriting almost immediately whenever capital costs rise to reduce loss volatility and keep confidence high. Additionally, AI can help price loans more precisely at the individual level and identify the best interest rates and/or discounts applied onto borrowers to maximize profitability.
Indeed, AI is not here to take over fintech lending, but instead will become a key tool used by lenders to streamline and expand their operations, using it as a tool alongside executive human judgement to make the best decisions. This also means that for fintechs to remain competitive, they’ll have to start integrating AI models into their core infrastructure and design their lending operations and platforms around it. At the same time, however, due to the unknown risks posed by AI and the importance of higher level human expertise and judgement, it remains important for human employees and executives to remain in the loop, which must be considered by companies when building or modifying their infrastructure.
Nonetheless, the future of AI in fintech lending and credit risk looks promising, with the opportunity for companies to make credit underwriting more accurate and efficient, as well as use advanced tools to better link their operations to the economy and markets in real time, allowing them to better respond to changes. It is imperative for fintechs writ large, both existing and those rising to the stage, to stay on track with advancing AI and subsequently keep evolving.
Works Cited
Consumer Financial Protection Bureau. “CFPB Issues Guidance on Credit Denials by Lenders Using Artificial Intelligence.” Consumer Financial Protection Bureau, 19 Sept. 2023, www.consumerfinance.gov/about-us/newsroom/cfpb-issues-guidance-on-credit-denials-by-lenders-using-artificial-intelligence/.
“How Leading Lenders Are Using AI Now to Drive Growth and Volume.” The Financial Brand, 24 Nov. 2025, thefinancialbrand.com/news/loan-growth/the-lenders-who-wait-on-ai-will-get-left-behind-193888?categorySlug=loan-growth&postSlug=the-lenders-who-wait-on-ai-will-get-left-behind-193888. Accessed 6 Feb. 2026.
Johnson, Helen. “Transforming Risk Management in Financial Services with Generative AI – SPONSOR CONTENT from PROVECTUS and AWS.” Harvard Business Review, 7 Oct. 2025, hbr.org/sponsored/2025/10/transforming-risk-management-in-financial-services-with-generative-ai. Accessed 6 Feb. 2026.
Radhakrishnan, Anand, and Sadaat Mubashar. “The Future of Corporate Lending: How Generative AI Is Transforming Credit Assessments.” International Banker, 5 Mar. 2025, internationalbanker.com/technology/the-future-of-corporate-lending-how-generative-ai-is-transforming-credit-assessments/? Accessed 6 Feb. 2026.