Bridging Risk & Innovation: Why AI Matters in Credit Analysis

Why Does AI Matter in Credit Analysis?

AI matters in credit analysis because it helps financial institutions make faster, better-informed lending decisions while handling far more data than manual review allows. According to Mr. William Francis, Director of Mandiri Tunas Finance (MTF), the value of AI comes down to four things: speed, intelligence, coverage, and productivity.

He shared this view at the Executive Luncheon: Bridging Risk & Innovation - AI in Credit Analysis, hosted by Fineksi together with Oradian in Jakarta on 7 October 2026.

About the Event: Executive Luncheon by Fineksi

The Executive Luncheon is an exclusive, invitation-only forum that Fineksi holds regularly. It brings together Chiefs, Directors, and Heads from leading banks, multifinance companies, and fintech institutions in Indonesia to exchange views on how AI is being applied across the financial industry.

This edition focused on one of lending's most persistent challenges: balancing risk control with the need to innovate and move faster.

The Four Reasons AI Is Essential for Financial Institutions

One of the highlights of the event was the presentation by Mr. William Francis, who shared how MTF uses AI across different stages of its business processes. He outlined four core reasons why AI has become important for financial institutions:

Pillar	What It Means	Impact on Credit Analysis
Speed	Accelerates analysis and decision-making	Shorter loan turnaround times
Intelligence	Turns raw data into actionable insight	Better-informed credit decisions
Coverage	Expands capacity to analyze large volumes of data and documents	More thorough review of every application
Productivity	Automates work that used to be done manually	Analysts focus on judgment, not data entry

1. Speed: Faster Analysis and Decisions

Credit analysis has traditionally been slowed by manual document checks. AI shortens this cycle, helping lenders move from application to decision more quickly without lowering their standards.

2. Intelligence: From Data to Better Decisions

Data alone does not make a good credit decision. AI helps convert scattered information into clear insights that support sounder business judgment.

3. Coverage: Analyzing More Data, More Thoroughly

When teams review documents by hand, they often have to sample or skim. AI expands analytical capacity so institutions can examine large volumes of data and documents in full.

4. Productivity: Automating Manual Work

By automating repetitive tasks, AI frees credit teams to spend their time on what humans do best: evaluating context, assessing risk, and making final calls.

Real-World Use Case: AI-Powered Bank Statement Analysis

One of the most compelling applications discussed was the use of AI for bank statement analysis.

Borrowers often submit bank statements that run to hundreds or even thousands of pages. Reviewing them manually, page by page, takes significant time and effort and leaves room for human error.

With AI, thousands of pages of physical bank statements can be converted into summaries and analytical insights in a short time. Instead of a stack of documents waiting to be read, credit analysts receive structured information they can use immediately to assess a borrower's cash flow and financial behavior.

The Strategic Value of AI: More Than Just Speed

The key message from the session was clear: AI's strategic value is not only about getting work done faster. It is about helping financial institutions extract insight from data more efficiently, so every lending decision rests on a more complete picture of the borrower.

For lenders, this means:

• Faster loan processing without sacrificing credit quality
• More consistent analysis across applications
• Earlier visibility into potential risks
• Better use of analysts' time and expertise

How Fineksi Supports AI-Driven Credit Analysis

Fineksi is an AI-powered Document Intelligence for Credit Analysis company. It helps financial institutions streamline their credit process and detect fraud by ingesting all borrower documents and delivering AI-based credit analysis.

The result: lenders can process loans faster without sacrificing quality, while working to reduce non-performing loans (NPLs). Use cases like the bank statement analysis discussed at the luncheon reflect exactly the kind of document-heavy work Fineksi is built to transform.