AI Credit Scoring Software

For decades, credit scoring has shaped the way banks decide who gets a loan. At One Bank (title changed due to NDA restrictions), credit managers process hundreds of thousands of lending applications each year, relying on credit scores built from customers’ past financial behavior to estimate their ability to repay. However, these standard rules are applied uniformly to all future borrowers, meaning many capable applicants are denied.

Over time, this cautious approach led to overinsurance among loan managers, a steady drop in approved loans, and a noticeable decline in the bank’s revenue. To address this, One Bank set out to assess how artificial intelligence could compare to — or even surpass — traditional credit scoring when it comes to predicting which customers might default.

Plavno’s specialists began by examining which parts of the lending process could be automated to boost the accuracy of creditworthiness assessments and shorten the time required for credit decisions. Following a thorough process analysis, Plavno identified four key elements essential to automating the bank’s credit scoring system:

• Credit process automation
• Borrower risk assessment
• Credit history analysis
• Credit risk forecasting

As a result, the final product was built around four distinct segments focused on assessing borrower capacity. AI-powered application scoring serves as the first line of defense in the credit approval process. Advanced algorithms analyze key data from loan applications — including income, employment status, assets, and liabilities — to detect patterns and estimate the likelihood of repayment. By automating this process, the system not only accelerates application reviews but also minimizes the risk of human error. Compared to manual assessments, AI delivers faster and more precise evaluations of a borrower’s creditworthiness.

AI-driven behavioral scoring provides dynamic, real-time monitoring of a borrower’s financial activity. Algorithms analyze transactions, payment patterns, and other financial behaviors, using machine learning to detect shifts that may signal potential risks. This enables banks to respond swiftly to emerging issues and offer tailored solutions to borrowers. By enhancing risk detection and management, AI ultimately improves the efficiency and effectiveness of lending operations.



AI-powered fraud scoring acts as a crucial shield against financial threats. By analyzing vast amounts of data in real time, algorithms can spot suspicious transactions and unusual behaviors that signal potential fraud. Machine learning uncovers patterns often invisible to human analysts, enabling banks to swiftly block suspicious activities and prevent financial losses. In doing so, AI not only safeguards the bank’s assets but also enhances customer protection against fraud.

Debt recovery scoring is the optimization of the debt recovery process. Algorithms assess the likelihood of repayment and determine the most effective strategies for working with debtors. Machine learning analyzes a debtor's credit history, financial situation and behavioral factors. This allows banks to reduce recovery costs and increase repayment rates.



The AI credit scoring software developed by Plavno is uniquely grounded in game theory principles. It applies two evolutionary game models — one between the bank and individual borrowers, and another between the bank and SMEs or large enterprises. By analyzing the strategic behaviors of both players across numerous interaction points, the system determines whether to approve or deny credit. Since implementing the AI-driven scoring system at the end of 2021, the bank has seen a dramatic impact: the number of expert loan denials has been cut in half.