Machine learning applications in enhancing loan-level transparency and decision-making

Machine learning algorithms have become a standard in modern industries, solving numerous complex problems such as image classification, personal recommendation, and smart trading (Gadi, 2023; Burugulla, 2025; Challa, 2022). This work makes a step further in the application of ML in boosting the loan market by developing a framework that efficiently analyzes the massive loan subset of the securitization market, traditionally characterized by a lack of transparency and insufficient subordinated bonds pricing. Our main contribution specifically supports potential loan buyers in conducting thorough analyses and buyers in modeling the complex loss-given default in real estate properties. To properly approach this objective, we access, clean, and preprocess a set of tens of millions of residential mortgage loans and develop a severe loss function dependent on numerous economic factors for each loan and for the entire securitization trust in order to appropriately link the managerial and contractual levels.

While traditional credit risk models deployed in the financial industry have successfully predicted potential defaults and portfolio losses on large-scale data mainly composed of publicly available companies, more challenges have to be overcome in the similar but less developed loan-related markets (Pamisetty, 2024; Gadi, 2023; Burugulla, 2025). Banks or large institutional lenders usually provide alignment of interests or monitor for the completion of other covenant-stipulated terms. In addition, over time these significant pledged assets securing the debt can cause the value extracted from the collateral to deviate from the contractual obligation over the loan life. Deterioration is negatively linked to the loan probability of paying all expected installments and to the eventual collateral repossession where additional high costs might evolve.