Fraud in Consumer and Mortgage Lending: Algorithms for Detection and Prevention

Citation: Dikopoltseva Svetlana Aleksandrovna, "Fraud in Consumer and Mortgage Lending: Algorithms for Detection and Prevention", Universal Library of Business and Economics, Volume 02, Issue 03.

Copyright: This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

In the paradigm of accelerated digital transformation of the lending sector and the exponential growth of loan volumes the relevance of countering fraudulent schemes increases manifold resulting in economic losses for both banking institutions and end borrowers. The study is focused on substantiating comprehensive algorithmic solutions for detecting and suppressing fraud activities in the consumer and mortgage lending segments. The objective of the work is to systematize modern anti-fraud methods and to formulate a scientific and methodological foundation for designing highly scalable and adaptive security systems. The methodological basis of the research relies on an extensive analysis of specialized publications in recent years an overview of advanced practices in the application of machine learning big data processing and their integration with external information ecosystems. As empirical justification the results of the implementation of the author automated system for comprehensive borrower verification are presented. Experimental data demonstrate the superiority of hybrid architectures combining multi-level verification against governmental and commercial registers with intelligent encoding of risk profiles and interregional matching of applications. The expediency of evolving from fully manual processing to 90 % automation of decision-making is argued which ensures the processing of multimillion flows of credit applications with high speed and reliability. It is concluded that further development of fraud prevention systems will take place at the intersection of artificial intelligence technologies and predictive analytics creating a synergistic effect to enhance the quality of risk management. The materials presented in this article will be of interest to specialists in financial risk management fintech industry researchers and software developers for credit institutions.

Keywords: Credit Fraud Mortgage, Fraud Fraud Detection, Fraud Prevention, Machine Learning, Scoring, Automation, Big Data, Borrower Verification, Fraud Monitoring.

https://doi.org/10.70315/uloap.ulbec.2025.0203024