Credit repair is the process of fixing a credit history that has one or more problems, such as errors, identity theft, or actual delinquencies and similar issues. Credit report inaccuracies can be disputed easily with the credit bureaus and at the same time whenever a consumer is affected by identity theft would require an extensive amount of investigation and steps to fix the same. As per Federal Trade Commission (FTC) guidelines, consumers are protected and have rules in place to dispute any fraudulent activity in their credit report. This loophole is being exploited by bad actors and credit repair companies to falsely raise a dispute on the recent activities of new tradlines, new mortgage, or fraudulent activities with the only aim to remove such activities from their credit file and boost their credit score which in turn they will use it to get more loans or open new tradelines. This process of intentionally raising false disputes to mislead the lenders and financial institutions is called Credit Washing.In other words, Credit Washing is the act of working with the credit bureaus to dispute legitimates charges with the intention of improving a previously reported low credit score, either by falsely disputing incorrect items (yourself or with the help of a company) or by falsely correcting certain financial behaviors. This journal discusses the basic understanding of Credit washing,its impacts on financial markets,risks associated,current measures to monitor and control credit washing, proposed enhanced methods of advanced predictive machine learning and AI capabilities to improve detection of credit washing in order to protect the financial interests of millions of people who are genuinely impacted by false reporting and also to safeguard consumer rights.

Credit Washing, Machine Learning, AI, Automated Fraud Detection, Identify Theft, Predictive Modelling, Credit Score Manipulation, Behavioral Analytics

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