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 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:
- HMDA public data
- Census public data
- Social media / news sources to determine soft indicators/reputation
- Private Data pipelined from CFPB
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 predicts 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.
Data Society leads government agencies and global enterprises alike to understand, and then unleash, the unlimited potential of their data-driven workforce.
At Graphs in Government on April 28th, Nisha Iyer, VP of Data Science and Engineering, spoke to the power of graph databases, specifically knowledge graphs, and how we use them on projects like the CFPB tech sprint to highlight areas that are usually very hard to connect – both schematically and ontologically – and pull information from these sources to help a specific population understand relevant and impactful data.