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NFAIRLENDING CASE STUDY: Rockland Trust Uncovers New Opportunities While Reducing Redlining Risk with Nfairlending
Technology Category
- Analytics & Modeling - Real Time Analytics
Applicable Industries
- Finance & Insurance
Applicable Functions
- Business Operation
Use Cases
- Regulatory Compliance Monitoring
- Predictive Quality Analytics
Services
- Data Science Services
The Challenge
Rockland Trust, a bank with a mix of commercial and consumer loans in Massachusetts and Rhode Island, has grown significantly over the past decade, acquiring several banks and expanding its loan portfolio. With continued regulator and examiner scrutiny of banks’ Reasonably Expected Market Area (REMA), Allen Bernier, vice president compliance and fair lending officer at Rockland Trust, wanted to ensure his institution was taking all the necessary steps to ensure compliance. One of his chief tasks is ensuring that his bank doesn’t have unchecked Redlining risk. When using a competitor’s HMDA product, Bernier’s team would spend all day tweaking data and working on the formulas. This left them with very little time to focus on analyzing the most important thing—what the data actually meant.
About The Customer
Rockland Trust is a bank founded in 1907, underwriting a mix of commercial (70 percent) and consumer (30 percent) loans in Massachusetts and Rhode Island. The bank has grown significantly over the past decade, acquiring several banks and expanding its loan portfolio. The bank is valued at $13 billion and is located in Rockland, MA. It has been a customer of Nfairlending since 2016 and its primary examiner is the FDIC. One of the key tasks of the bank is ensuring that it doesn't have unchecked Redlining risk, a task that is overseen by Allen Bernier, the vice president compliance and fair lending officer at Rockland Trust.
The Solution
Rockland Trust wanted to find a cost-effective solution that would help it manage compliance, reduce compliance risk, and better understand its lending data. It found all that and more in Nfairlending, a secure, web-based solution that allows the bank to easily manage its fair lending compliance processes for HMDA and non-HMDA loans, with instant analysis of data and robust reporting that saves time and effort. Bernier uses the Nfairlending Redlining Analytics module to analyze his bank’s data to find communities that are undermarketed—a potential indicator of redlining. To ensure there are no REMA issues or redlining risk with his bank’s marketing practices, Bernier collects a list of zip codes where the marketing department sends direct mail advertising and plots heat maps of where they are sent. Using Nfairlending’s Redlining Analytics, he adds the zip codes into the analysis area parameters and draws a map by census or zip code. Comparing the two maps, he can determine if areas in or near low or moderate-income (LMI) communities are being excluded.
Operational Impact
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