Last month my colleague told me that we were participating in a CFPB #CFPBTechSprint and you have to drive that engagement. Having always been part of the Delivery organization, I had little experience of TechSprints. I’ve been working on creating HMDA reporting solution on the HEXANIKA SaaS platform so the reporting requirements and the HMDA data is not new to me.
We were looking at HMDA risks identification, reporting as requirement. CFPBs #CFPBTechSprint was an offer for us to validate the ideas with CFPB. The CFPBs #CFPBTechSprint was divided into 2 sections with specific problems statements;
1. Submission – Create additional tools for users on the HMDA Platform, using HMDA API, extend or enhance existing APIs, create new APIs that could further automate industry compliance systems and align them with filing requirements.
2. Publication – Identify and scope additional enhancements to HMDA data products and services, visualizations, or the development of our resources
Given the problem statements, our team thought we could ideally participate in the two areas and demonstrate our ideas.
For the Submission track, we perceived timely, good quality data submission to be the primary challenge. Also, additional insights for better risk management was also needed.
We did the POC for data submission using CPPB data submission APIs, and seamless interaction of data from our SmartJoin and SmartReg to CFPB data submission APIs. We demonstrated our innovative Smart platform which takes care of data management, where institutions can source the data from various other platforms, engines and combine the data, improve data quality during ingestion and prepare steps. Also, HMDA-LAR reporting capabilities. We also demonstrated our risk dashboards which tests the underwriting, pricing and redlining risks.
For Publication track, we discussed mostly new ideas to look for additional insights from HMDA data, and identify the covert disparity of lending, governance and using customer experience data as fair lending practice. We used CFPB data browser API to download the historical submission data and utilized peer group historical data to create Model to test statistical regression to calculate predictive action type on the LAR data of the institution. We also suggested that CFPB capture customer experience data (using survey tools and complaints data) – we used NLP algorithms to identify the keywords of happy/unhappy/Neutral feedback. This can closely monitor the user experience of protected group.
These ideas were appreciated by the panelists. We were supported with expert advice from Jason Richardson, who was part of our team. From the beginning, we’ve received keen interest in the ideas we were working upon. We also received support from Greg Ellis, assisting in our using customer experience in HMDA reporting. We appreciate the support and knowledge they shared with us. Prakhar, Kalyan, Purva, Bhaskar worked on the statistical regression Model and NLP processing. Ravi, Vijay, Priya worked on integrating HMDA APIs with HEXANIKA Solution. Arun and Yogesh contributed great ideas and articulate presentation skills. Avinash contributed by creating crisp presentations.
After a successful week at CFPBs #CFPBTechSprint, we believe this is just the beginning of our journey.
HEXANIKA is proud to announce the launch of a cloud-based Fair Lending SaaS Solution, SmartReg, bringing simplicity, smartness and efficiency to an ever-changing regulatory compliance environment.
1. Simple to Use: Significantly reduces the need of people to do things in excel or code
2. Smart Data Management: Automatically prepares data from core, lending and origination software all the way to reports, saving you time and money
3. Efficient Auditability: Provides 100% transparency for auditors and regulators
4. Efficient in Reporting: Deliver accurate and timely reporting while monitoring the regulatory risks
Stay tuned in…