For a retail lender, speed and caution pull in opposite directions. Applicants want an answer today. Credit teams need to check identity, income, property and fraud risk before they commit. Every manual check adds days, and every shortcut adds risk.
Octane has built a loan underwriting solution on IBM watsonx Orchestrate that does both. Thirteen agents check retail loans and write the credit memo. Then a person approves.
This article walks through how a loan moves from application to an open account, how the controls work, and what changes for a credit team.
A typical loan file is a stack of documents. There are identity papers, payslips, bank statements, tax returns and property records. Credit analysts read each one, cross-check the figures and write up a credit memo. It’s skilled work, and much of it is repetitive.
Meanwhile, fraud is getting smarter. An edited payslip can look perfect on its own. It’s only when you compare it with bank credits and employment records that the problem shows up. That comparison takes time most teams don’t have.
Applicants feel the delay too. Every extra day between application and decision is a chance for them to go to another lender. And when the answer is no, they deserve a clear reason and a path forward.
The solution is built for chief credit officers and heads of retail lending who want faster decisions without loosening policy. It supports underwriters and credit managers who want to spend their time on judgement, not data gathering. And it serves risk and fraud teams who need suspicious files caught early and explained clearly.
Our banking solution runs on a fictional lender, Octane Financial Services. It shows how a loan moves from application to an open account.
Apply. The applicant fills in a short, four-step form and shares or uploads their documents. They can do it in English, Hindi or Marathi.
Check. Five agents work at the same time, checking credit history, identity, income, collateral and fraud. Each one records its evidence.
Summarise. A summary agent writes the credit memo from what the checks found.
Recommend. Fixed rules apply the credit policy. The result is a rejection, a counter-offer, a fraud review or a recommendation to approve.
Approve. An underwriter or credit manager approves it, within their own limit. The key fact statement then goes out.
Open and notify. Approved loans open straight into core banking, with the repayment schedule in place. Letters and emails go to the applicant in their language, with clear reasons.
A typical application is checked and summarised in 18 to 36 seconds.
An application overview. The agents recommend approval, every check is listed with its evidence, and the underwriter makes the call.
The demonstration follows four applicants, each with a different result.
Approved. John applies for a personal loan. All five checks pass, the credit memo recommends approval and the underwriter approves it. Seconds later, the loan account appears in core banking with its repayment schedule.
Rejected, with reasons. Janet applies for a home loan. Her credit score is low, and her date of birth, address and income don’t match across her documents. The application is rejected, and she receives a letter in Hindi explaining why and what she can do next.
Counter-offer. Andrew is self-employed and asks for a loan against property. The loan would be 86% of the property’s value, above the policy limit of 60%. Rather than a flat no, the rules make a smaller counter-offer that fits the policy.
A counter-offer. The loan requested is above the policy’s loan-to-value limit, so a smaller amount is offered instead.
Fraud review. James submits a payslip that has been edited. On its own, it looks fine. But the agents notice the file was saved by a PDF editor, the fonts are mixed, and the salary doesn’t match his bank credits or his employment records. The file goes straight to the fraud team, with every signal explained.
A fraud review. The agents explain each warning sign, from the edited file to the salary that doesn’t match the bank credits.
Our agents can reject, counter-offer or recommend. They can never approve. Human in the loop is built in, and only a person with the right authority approves. The sums behind each decision, such as what the applicant can afford and how much the property is worth, are worked out by fixed rules, not AI. Every finding carries the document, the page and the exact quote, so an underwriter can check it in seconds. Every rejection comes with reasons and what the applicant can do next.
Our demonstration follows Indian retail lending rules and languages, but the pattern applies to any lender. The credit policy, the documents and the approval limits are yours to set.
Before, analysts spent most of their time gathering and checking data. With the agents, they start with the checks done, the evidence linked and the memo drafted.
Before, fraud was often caught late, if at all. With the agents, every file is checked the same way, and suspicious ones are flagged before anyone approves them.
Before, applicants waited days and sometimes got a vague no. With the agents, they get a fast answer, in their own language, with a clear reason.
These results come from industry research, not from a single Octane client. McKinsey and the IACPM report a 20 to 60% productivity gain for credit analysts from AI-drafted credit memos, and about 30% faster credit decisions. IBM’s Asteria case study, a pilot, showed 33% less time spent on the loan advisory process.
A general AI assistant can summarise a document, but it can’t run a credit process, apply your policy consistently or show an auditor where each figure came from. Traditional automation can move data between systems, but it can’t read a payslip or spot an edited document.
Octane’s agents do the reading and checking inside a process governed by your credit policy. People approve, and every step is recorded.
Will the AI approve loans on its own? Never. It can recommend, reject or counter-offer under your policy. Approval always needs a person with the right authority.
Can a regulator follow the decision? Yes. Every finding links to the document, the page and the exact words, and every step is recorded.
Do we have to replace our core banking system? No. In a real project, the agents connect to the systems you already run.
Is customer data safe? Our demonstration uses made-up applicants and documents. For a real project, how your data is handled is agreed with your team at the start.
The first step is a live demonstration, where you can watch an application go from submission to an open account. For lenders ready to go further, our Early Access Pilot proves the value on your own lending process in six to eight weeks. We agree the success criteria with your leaders, configure the agents for your policy, measure the results against today’s baseline and finish with an executive readout and a fixed-cost plan for production.
Faster decisions win customers. Consistent rules protect the loan book. Clear evidence makes every decision easy to review, whether by a credit committee, an auditor or a regulator. Your credit team spends less time gathering facts and more time on the judgement calls that need them.
Explore the banking agents at agents.octanesolutions.com.au, or book a demo with our team.
We go the extra mile so you can go the distance.
Screenshots are from Octane’s demonstration environment. The companies, people and data shown are fictional.