Data analytics can be a powerful tool, but only when people with deep expertise interpret the results and translate them into action.
Financial services companies can’t afford to get blindsided by fair lending risk. A single regulatory violation or negative headline can erode customer confidence of your customers, damage reputations, and result in fines and penalties.
The challenge is that many organizations might not know about their most significant sources of fair lending risk. Recent discussions and industry updates have pointed to a need for more understanding of nontraditional business models, including wholesale funding situations or working with fintechs. From the models to pricing and underwriting decisions, many financial services companies have not incorporated those risks into their fair lending monitoring and testing programs.
To try and quantify these unknowns, more organizations are using data analytics to identify fair lending risk. Data analytics tools can provide quantitative data related to banking practices, highlighting the gaps and risks in lending patterns and fair lending compliance efforts.
However, it’s important to understand the limitations of data as well as the benefits. Data analytics software can analyze data, but it can’t interpret the results and translate them into a practical, comprehensive fair lending compliance strategy. Human expertise, experience, and perspective are required to turn data analytics results into effective action.