Conduct an online search, and you’ll discover relatively few sources addressing the deepening relationship between credit processes and artificial intelligence (AI). We’ve written extensively about loan underwriting, but in this article, we’ll focus on how financial firms and other businesses are increasingly deploying AI for credit processes.
In 2023, there was a scramble to fit AI into (seemingly) every product. However, we’re still witnessing a lack of coherent direction for integrating AI into credit processes. AI has vast potential, yet the specifics of its application remain ambiguous.
Nonetheless, our discussions with clients reveal a strong interest in implementing instant credit decisioning.
Their motivation?
The loan underwriting process, when completed manually, can take hours.
However, this is only one of many applications to credit processes. Recently, we’ve been interested in how AI can enhance the accessibility and regulation of lines of credit and revolving credit facilities.
(Watch a recording of our webinar, where one of our clients, YouLend, discusses using AI to implement instant credit decisioning).
AI-powered credit scoring uses the general rubric of assessing creditworthiness – analysing bank statements, income statements, balance sheets and credit history – and then considers additional data (e.g., loan and asset performance data).In February, emerging fintech Payaga Technologies secured a $280 million credit facility. Payaga analyses data from credit bureaus to single out desirable borrowers who don’t meet FICO standards. Lending firms like Payaga operate with a data-heavy approach. By using AI to read volumes of financial data, companies anticipate that the methods and systems used for providing loans will improve over time.One lesser-known benefit of AI in credit processes is its potential to reduce financial exclusion. AI technologies can assist underrepresented groups, such as minority-owned businesses, with accessing credit lines and more. AI can also provide a more nuanced perspective of an individual’s creditworthiness by analysing a broader range of financial indicators (e.g., alternative credit data like rental payment history). Unless carefully monitored, AI could unintentionally reinforce existing prejudices. For instance, Amazon used an AI hiring system that preferred male over female candidates. The system’s bias likely stemmed from how the AI was trained (i.e., on data that already contained biases). Without appropriate governance, AI credit systems can produce distorted results.
In revolving credit facilities, the borrowing limit and interest rate can vary (as well as the terms and conditions of the loan) based on the borrower's creditworthiness. AI can quickly extract the borrowing rate of the loan, the recipient’s credit history and the expiry date from loan agreements and credit reports. Underwriters can use these insights to predict risk and adjust the terms of the loan, maintaining a mutually beneficial, long-term relationship with the borrower.
Developing personalised credit offers through behaviour analysis has also become a popular AI application. It seems counterintuitive that an automated system can provide a more personalised experience than a customer success team. Such a presumption depends on the general precept that the more data you can input into AI, the better the quality of the output. Using extensive data to extend personalised offers may result in mutual benefits for lenders and borrowers (if implemented correctly). For example, Atlas Credit used one of Experian’s intelligent data-driven marketing services to deliver personalised offers to high-value prospects, boosting loan originations by 185%.
The cybersecurity framework for preventing unauthorised credit misuse is well-established.However, we’re witnessing a shift from using rule-based fraud detection to AI-based systems to identify latent patterns in data. When deployed, AI technologies can identify unrecognised transactions instantly. Companies could even use behavioural biometrics – such as typing speed, swiping or mouse movements – to combat the $30 billion annual cost of credit card fraud.
One change that we can comfortably anticipate? Speed. Fast decision-making and effective customer communication empower credit decisioning and analysis. Jessica O’Hare, Head of Product at YouLend, noted that with AI:
“We can fund a lot faster than having to wait to extract the information from the bank statement. So our number one goal is improving customer experience, and we can do that through AI.”
However, AI will transform the skillsets of those teams behind credit processes. To embrace this transition, Gartner recommends that finance teams undertake further education on leveraging digital technology to drive better financial performance. Investing in AI expertise and technology is a strategy for succeeding in an unpredictable economy. Owning and operating a robust AI architecture will allow companies to adapt to market changes, shifts in consumer credit behaviour, etc.
We’d recommend an automation tool for those wanting to extend their platform’s current capabilities (rather than investing in a third-party lending platform). An automation data tool – such as Evolution AI’s extraction software – is specialised, meaning that building a company equivalent will take significant time and resources better spent elsewhere.
Evolution AI offers a multi-award-winning automated data extraction service. Try our new financial statement product or book a demo at hello@evolution.ai for more information.
