AI-configured lending platforms could ease core system changes, analysts say

Any intervention in a bank’s core system makes an engineering team wince. Legacy solutions are rigid, so a single change can take a month to move through. Lending, which is one of a bank’s major sources of income, feels this most.

Anyone who has shipped a release on a bank core knows the pattern: a new product needs a new approval tier, a revised document checklist, an adjusted scoring rule, and an updated reporting feed, each touching code that has run for decades.

As a result, new products and updates get postponed even when everyone agrees they’re needed: the risk of a mistake has always outweighed the risk of waiting. Or rather, it used to: lagging behind technological change has become a far bigger risk today.

However, imagine a separate, AI-configured lending platform deployed alongside the core. New credit flows run independently and connect to the core through integrations. This way, changes to lending infrastructure become a series of small, tested releases, and mistakes surface in testing before they reach a live borrower. Could this soothe the pain for engineering teams?

Demand Is Growing Faster Than Legacy Cores Can Change

The loan origination software market is expected to grow at an 11.8% compound annual rate through 2030, according to The Business Research Company. For context, that’s almost the same pace as the broader enterprise software market, based on Technavio projections. That’s worth keeping in mind, considering that lending is usually the most change-averse corner of a bank’s technology stack.

One driver of this growth is the rigidity of legacy cores, which slows down changes to lending. Banks and fintechs are increasingly buying tooling that lets them ship credit products around the core without waiting months for a small update to be approved.

Engineering teams know that a single mistake can be very costly. In April 2018, TSB Bank migrated its customer data to a new platform, which failed as soon as it went live. A significant share of its customers lost access to banking services, and the bank incurred heavy losses. Many engineering teams still bring up that case whenever they plan a core release.

What’s more, in lending, errors reach the balance sheet directly. A misconfigured approval threshold approves loans that should have been declined or turns away borrowers who qualified. Either mistake shows up in losses or lost revenue within weeks.

Where AI Adds Leverage in Lending

Banks usually start adding AI with the most familiar and seemingly safe integrations: chatbots and service assistants at the customer interface. A lender that adds a chatbot takes on a less obvious risk. Generative models sometimes produce wrong answers with full confidence. In Grant Thornton’s 2026 AI Impact Survey, only 18% of banking executives said they were fully confident in their AI controls.

AI’s leverage in lending comes from how fast a lender can change what surrounds the credit decision. The decision itself stays with approved scoring models and credit committees.

Speed has always had commercial value. Changing lending infrastructure on a legacy core takes 12 to 18 months of engineering work, and new launches stall for that entire period. A full core replacement takes three to five years. Meanwhile, borrowers apply elsewhere.

The Federal Reserve’s 2026 Report on Employer Firms found that the share of US small business credit applicants seeking financing from online fintech lenders rose from 17% in 2020 to 29% in 2025, with many firms citing faster decisions as a reason.

AI Starts From the Business Requirement

Instead of running analytics or taking part in credit decisions, AI can bring huge value in the configuration work that helps launch and update lending products. The input is a business requirement written by the product owners. A credit team describes what it wants: who can apply, which documents are required, the approval tiers, what data goes into the score, what gets reported, and when.

AI turns each point into platform configuration: eligibility criteria, a document checklist, an approval workflow with its tiers, connections to the data sources behind the score, and a reporting schedule. On a legacy core, engineers build each of these manually, and every change goes through a full release cycle. With AI, the product is built feature by feature on a shared foundation. Engineering and compliance teams review and test each change before it goes live, and the sign-off stays with them.

The requirement can be entirely independent of the existing core. AI doesn’t need to read or interpret the core’s logic, so a lender can specify a product the current system was never designed to support. Two decades of undocumented code stay untouched.

The new credit flow runs on a separate platform deployed alongside the core. The two connect through integrations, and the core keeps its existing records and functions. None of its code has to be rewritten before a new product can launch.

Building on the Data That Exists Today

Many banking AI projects stall on data, though. But there’s no real need for a huge database – it’s good to have, but not a must. AI drafts the underwriting rules and data requirements around the sources that exist; the risk team approves them before anything runs. When a source is missing, it defines how to collect it: borrower intake, guarantor intake, required and uploadable documents, and external data sources.

This “data building” process helps banks and fintechs expand on their real data inventory, gaps included, and vendor assumptions about what should already be collected stop driving the project.

Data readiness stops being a precondition for launching a credit product and becomes part of the same configuration step.

Product Change VS Core Change

For most lenders, the practical question is whether new credit products can ship and existing ones can adapt while a multi-year core migration continues. A separate lending platform, configured with AI based on business requirements, answers that question. A lender can launch and adjust credit products each quarter while the core program keeps its own timeline. That decoupling is where AI is proving its value in lending today.