How Will AI Change Lending?

Banking Industry Trends

The popularity of artificial intelligence (AI) has skyrocketed since the launch of tools like OpenAI’s conversational chatbot, ChatGPT, which accumulated more than one million users a mere week after going live. ChatGPT effectively brought AI into the mainstream and financial institutions are paying close attention to the field. A recent Axios article reports that North American banks published 80% of all bank AI research and made 60% of all bank AI-related investments last year.

AI presents a wealth of opportunities to enhance performance across various functions – from fraud prevention to customer support. But, what about lending? Lending, especially commercial lending, is the bread and butter for most financial institutions’ bottom lines.

While there are still many open questions about using AI within existing regulatory requirements, it is worthwhile to consider how this new technology could be used to enhance the lending experience for both borrowers and banks.

Use Cases for AI in Lending Today

From automated loan decisioning tools that many banks use now, to the AI-driven risk models that are being explored for the future, today’s financial institutions have several opportunities to evolve their lending operations for a digital-first world. AI is already promising to revolutionize how financial institutions assess risk, underwrite loans and enhance the overall borrower experience.

Among the ways AI is being used in lending today include:


Lenders can sift through vast amounts of data to find consumers who match their credit criteria and send right-sized offers. This allows financial institutions to deliver timely, personalized offers to the most valuable or high-potential customers.

Beyond targeting, AI can enable institutions to develop bespoke credit models for those high-value customer segments that consider unique data points or variables that support more accurate decisioning and more tailored product offers.

Evaluating creditworthiness:

Machine learning and AI-based credit models can incorporate a range of internal and external data points to more precisely evaluate creditworthiness.

Over the last several years, new types of alternative credit data have gained traction by giving lenders a more complete picture of a borrower’s financial health. With more supplemental data points combined with the power of AI, banks can be equipped to serve a wider variety of borrowers without being exposed to added risk.

Portfolio management:

AI can also present a more complete picture of a bank’s current portfolio to make better decisions. For example, AI-driven models can help lenders set initial credit limits and suggest when a change could help them increase wallet share or reduce risk.

Bankers Steer the Ship, AI Assists

AI or any machine-learning automation that’s used in credit decisioning should always incorporate the bank’s credit policies and fair lending practices to determine how each credit request should be reviewed and decisioned.

For example, Baker Hill’s technology supports multiple decision strategies, including scored, non-scored, fully automated decision, automated decision with manual review or always manual review. Many banks often choose to automate decisions for loans under a certain dollar amount. For larger, more complex deals up to a certain dollar amount, some banks may opt to use auto-decisioning with manual review.

In addition to sound credit policies, AI and similar tools require good data to generate effective, accurate outcomes, which our team covered more in a recent podcast episode. The outcomes or decisions will only be as good as the data that’s fed into the model, which means the right technology integrations and relevant data sources will be crucial.

AI was never meant to entirely replace the human role in making financial decisions. In banking, there will always be some element of a personal touch. The most effective applications of AI blend the expertise and personal touch of a human with the logic and efficiency of AI. In these scenarios, AI is designed to assist the banker, who maintains control of the decisions, the relationships, and ultimately, the direction the bank heads in.

The banks that win will be those who safely explored AI’s potential early on. By doing so, forward-thinking institutions can be prepared to use this cutting-edge technology to elevate their customer experience and their bottom line.