Stratyfy reposted this
CEO and Co-founder at Stratyfy | Women in FinTech Powerlist | BAI Global Innovation Rising Star Award Winner | 2x NYC FinTech Women Inspiring FinTech Females Awardee
A lot moving in the world of Fair Lending lately. Two important developments in the past month that should be on your radar… (1) At the end of June, the Consumer Financial Protection Bureau (CFPB) released its annual report, showcasing their efforts and priorities in enforcing fair lending regulations. Some important highlights to be aware of: 🔦This quote: “Robust fair lending testing of models should include regular testing for disparate treatment and disparate impact, including searches for and implementation of less discriminatory alternatives using manual or automated techniques.” 🔦While this may not be groundbreaking news for those in the industry who are taking a proactive approach to fairness in lending, it’s a significant step forward. This is the first time the CFPB has articulated this standard in writing with such clarity and precision, setting a new benchmark for transparency and helping lenders understand what is expected to avoid being regulated via enforcement. 🔦The Bureau made it clear that it had used its supervisory authority to enter into memoranda of understanding with several financial institutions that had not met the standard and required that they demonstrate they have made compliance changes to address the failure by issuing Matters Requiring Attention (MRAs) to those institutions. 🔦The report also highlighted the increase in regulatory actions in 2023 including: 🚨Citing 189 institutions with violations of ECOA and/or Regulation B 🚨33 referrals to the DOJ involving discrimination in violation of ECOA (an increase of 175 percent in such referrals since 2020) by 5 agencies (FDIC, NCUA, FRB, OCC and CFPB) 🔦The CFPB stressed the need for institutions to document how they assess any disparities against the stated business needs, which is often overlooked in the industry. 🔦The report emphasized that lenders must develop a process to consider a range of less discriminatory models (LDAs), which has also been lacking in the industry (more to follow on this point in particular...)