“Loans Like Me” is an AI-driven web app that democratizes HMDA (Home Mortgage Disclosure Act) data, enabling consumers to compare quoted interest rates and mortgage details against similar loans and detect the probability (low, medium, or high) of bias. Directly from the app, the user can report possible bias to the CFPB and request assistance to find a more fair rate. The app’s dashboard also informs the consumer of factors that could influence interest rates, including key details from comparable loans and historical bias in the data (e.g., an institution’s past lending patterns, or geographic trends, affecting individuals that objectively "look like them"). With easy access to this information, consumers have a firm foundation to advocate on their own behalf and find lenders with less propensity toward possible bias.
For regulators, Loans Like Me overlays incoming real-time data from consumers over a map of historic credit deserts and automatically flags financial institutions within and across geographic regions as automated calculations and comparisons reveal biased/discriminatory practices. With this tool, regulators are able to readily identify emerging or enduring bias and target remedial actions in short order, rather than awaiting annual submissions to identify issues retroactively.
Our solution is predicated on the use of four pillars of information and incorporating artificial intelligence to create a more robust and transparent solution for lending discrimination analysis that has the granularity to facilitate consumers and regulators across the agency.
Our approach deconstructs a complex problem into its smallest building blocks and then addresses each through means of analysis of the following types of data sources:
We built an algorithm that clusters similar consumers based first on financial factors with the greatest impact on potential interest rates and compares them to peers within a subgroup using important features to predict the risk of discrimination. Finally, with reputation, soft score (externally derived), and HMDA LAR data incorporated the application can forecast discrimination risk of an institution and county- state-, and national level trends.
In the hands of the consumer, this user-friendly platform powers the self-efficacy that brings equity closer to the norm, one deal at a time. For the CFPB, moving from annual bank submissions to real-time consumer reports means understanding changing interest rates as they’re quoted, informing agile and relevant decisions. Long term, as an agency, such valuable tools help the CFPB strengthen its position as a key consumer resource for fairness in lending and beyond.
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