CUs are turning to automation to prepare for upcoming regulation changes and rigorous data scrubbing requirements.
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Regulatory reporting compliance is top of mind for all financial institutions – especially as the Dodd-Frank 1071 ruling was enacted in March 2023, requiring covered financial institutions to collect and report small business lending data to the CFPB. While the final ruling increased the minimum volume threshold and exempts all but the several hundred largest credit unions, similarities between 1071 and existing HMDA reporting requirements present increasingly difficult challenges.
For 1071, qualifying institutions must quickly begin to accumulate, sift through and properly report all relevant data, but it is easier said than done. Lenders must accurately collect more than 20 additional data points from all small businesses, increasing the amount of time needed for every lending opportunity. Manual verification is fraught with human error, necessitating frequent checks-and-balances, and information can easily slip through the cracks. Credit unions anticipate having to staff up significantly and create new and comprehensive processes to ensure 1071 compliance, similar to their experiences when rolling out HMDA reporting in the past decade.
However, even if financial institutions hire double or triple their usual number of compliance professionals, the sheer cost of compliance will impede profits – and still won’t guarantee data integrity. While the CFPB provides materials, tools and compliance data info sheets to help financial institutions understand and plan for fair lending data requirements, best practices for small business lending is a foreign idea for credit unions. They must be educated about what this data entails, how to report it and how to ensure their data satisfies the rigorous requirements.
As banks and financial technology institutions have more experience with small business loans, many have already taken the automation initiative when it comes to compliance. Credit unions have a longer way to go; in response to this monumental data shift, credit unions are turning to automation to prepare for upcoming regulation changes and rigorous data scrubbing requirements.
Manual Data Scrubbing
Credit unions must evaluate internal compliance processes to tackle all compliance reporting in a way that reduces risk and operational costs. In order to thrive in an increasingly competitive financial landscape, they must adapt to newer technology and software and consistently find ways to smooth out processes.
Automation technology can accomplish many goals but perhaps the most impactful is the elimination of manual data scrubbing. Manual data verification is untenable as staff pressure increases with higher loan volume, which usually leads to management throwing more bodies at the problem. However, this is an unsustainable solution as more compliance professionals rarely improve data integrity or speed up the review process. Additionally, keeping staff busy with low-level compliance tasks prevents them from engaging with more high-level tasks for your institution.
By integrating machine learning into existing compliance processes, credit unions can transform the tedious and monotonous task of manual verification into an efficient and streamlined automated service, providing quality data in accordance with regulatory requirements every time. Automation saves time by auto-classifying, auto-extracting and assembling relevant content from mortgage, commercial and consumer documents for review. It can also extract data automatically from verified docs, reduce the risk of missed or delayed legal correspondence regarding customers’ collection status, and accurately document audit trails with time stamps and chain of custody, ensuring everything is accounted for without human interference.
Integrating a modern document automation platform to automate manual tasks that create risk and limited scalability keeps staffing costs low and liability to a minimum. Unlike compliance staff, which are prone to human error and inconsistencies, automation can immediately report HMDA and 1071 data field inconsistencies between loan documents and their LOS, ensuring staff only looks at true outliers in data. In drastically limiting manual discrepancy identification and eliminating costs and quality issues associated with outsourcing or offshoring, credit unions can dramatically increase capacity without needing additional headcount. This is an important cost-saving element during a downturn when loan originations are low and profits are marginal as it allows credit unions to maintain the same level of accuracy with all data.
Institutions can achieve 100% accuracy in HMDA and 1071 reporting via a human-trained machine and easily embed machine learning into existing workflow via open APIs. By cutting out many tedious compliance processes, credit unions could see their review process times reduced from 90 minutes down to five minutes per loan, ultimately reducing the operational cost by 95%. This allows staff to review more loans and provide better, quicker service to members. Automation improves every single process and provides quality data for HMDA and 1071 every time, making machine learning integration a must for all credit unions going forward.
It is time to prepare for future growth and alleviate labor challenges in a toughened compliance labor market. By seriously tackling the ever-changing regulatory demands across consumer and commercial lending, credit unions can build incredibly robust compliance systems that tackle intensifying financial and data integrity pressures.
Tyler Barron
Tyler Barron is Chief Revenue Officer for Encapture, a Dallas, Texas-based provider of an intelligent automation platform to companies including financial institution
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