Into the Future of Regtech | Reduce Compliance Costs Using Diffing

Reducing compliance costs using diffing

To streamline regulatory change management, one must have knowledge and understanding of how regulatory rules are written and published.  Various agencies produce regulations for financial institutions. For example, the Consumer Financial Protection Bureau (CFPB) publishes rules as mandated by the Dodd-Frank Wall Street Reform and Consumer Protection Act.  The CFPB’s rulemaking process begins with research and is enhanced by public input, in the form of field hearings, consumer and industry roundtables, advisory groups, and small business review panels.  Proposed rules are published to the industry and given the opportunity to comment on the rules potential impact. The comment period for most proposed rules is approximately 90 days. Then the comments are reviewed by the regulatory agency, and a Final Rule is issued with an effective date that the industry must comply with the rule.

Reducing compliance costs using diffing

As a recent example, the Home Mortgage Disclosure Act (HMDA) proposed rule was 573 pages of text.  Once released compliance staff must read the proposed rule and prepare summaries. Summaries are then discussed with Compliance Committees, Senior Management, or the Board of Directors, to formulate commentary to be sent to the CFPB during the comment period.  The final rule for HMDA was published on October 15, 2015, with an effective date of January 1, 2018, allowing two years to address compliance. The final published rule was 797 pages in length.

Reducing compliance costs using diffing

So what changed?

Reducing compliance costs using diffing

To discover what proposed rule content was included in the final rule, altered, or removed entirely, compliance staff must now read the final rule and determine the specifics.  That’s 1,370 pages of reading material. This process could be improved with a concept called diffing.

In computing,  diff utility is a data comparison tool that calculates and displays the differences between two files.  Computer algorithms could analyze the proposed rule, compared to the final rule, and tell compliance professionals what has changed.  This approach helps shorten the final rule review process, allowing a financial institution more time to develop policies and procedures to comply with the rule by the effective date. Such an approach could further take advantage of Natural Language Generation (NLG) to provide a summary narrative of the changes to users.

In addition to proposed and final rule difference, there are often changes and amendments to final regulations either prior to their inception or afterwards.  Once regulations are published, sometimes it comes to light that a particular section was vaguely worded and requires enhancements or clarification. The CFPB may decide to issue amendments to their rules.  In the current regulatory environment, the CFPB commonly amends regulation before the effective date. Many financial institutions have already read the final rule and designed their implementation planning by this time and now have to return to reading amendments to determine what changes to their action plan must be made.  

Time is already precious when working toward compliance, and the frustration of the rules being constantly in a state of flux can be an enormous obstacle.  For example, the 2013 Integrated Mortgage Disclosure Rule Under the Real Estate Settlement Procedures Act (Regulation X) and the Truth and Lending Act (Regulation Z) have been amended several times both prior and after the effective date.  The effective date was October 13, 2015, and the proposed rule was issued July 9, 2012, with an extended comment period expiring November 6, 2012.  There were amendments on February 19, 2015, and a delay of implementation published on July 24, 2015, before the effective date. Since its inception corrections were released on December 24, 2015, revisions to supplementary information on February 10, 2016, amendments on August 8, 2017, and May 2, 2018.  A software that analyzes the differences and changes between these alterations and amendments can help financial institutions meet the regulatory deadlines more efficiently, consistently and cost-effectively.

Compliance.ai is a leader in RegTech, leveraging AI and Machine Learning to increase the time to value for Compliance and Legal professionals. Discover more about Compliance.ai’s solutions for Compliance professionals here. If you are interested to learn more about Compliance.ai’s Diff Utility tool, please contact us at support@compliance.ai.

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