The financial industry is facing increased regulatory pressure, likely to be exacerbated by the long-awaited Dodd-Frank 1071 changes looming over the horizon. During this tentative waiting period, compliance leaders are already dedicating themselves to making the implementation of the new regulations as smooth as possible for their institutions— but nothing is for certain yet. Just like when the new HMDA regulations were introduced that left lenders scrambling, leaders must start thinking about potential issues and curveballs sooner rather than later.
As chief credit officers and bank directors struggle to predict the myriad of ways increased scrutiny and stricter rules could impact their performance, profits and labor force, they are progressively turning to machine learning and automation tools in order to stay competitive in the financial space.
It’s predicted that the 1071 regulations are going to result in a significant, proportional labor increase— many banks are predicting that their compliance teams will need to nearly double. As loans become more taxing to sift through with traditional methods – compliance staff reviewing each individual application manually in line with HMDA standards – teams will soon become overwhelmed by the sheer volume of applications and additional data fields to review.
These extra staff will need additional training, and it is possible the Dodd-Frank act will have a relatively short implementation period in which to do so. Financial institutions are left with ballooning labor costs, which detract from other profitable avenues in the short and long term.
Deana Stafford is a Senior Vice President and Director of CRA and Fair and Responsible Lending at First National Bank Texas, a large community bank headquartered in central Texas. Deana’s colleagues have brought on automation to help with HMDA scrubbing, and now with the upcoming 1071 regulations, automation has also been on Deana’s mind.
“We have already added one full-time staff citing 1071 and the expansion of CRA data collection and reporting after reading the proposed rules, but there is no way we can double our staff,” says Deana. “Automation is the best long-term solution.”
Automation eliminates the need for additional staff to take care of tasks machine learning can do with near-perfect accuracy, such as comparing data fields from a source document to the system of record, while drastically reducing human error and unnecessary risk. By embedding automated systems into already existing infrastructure, lending compliance capacity can be increased without additional headcount and false positives can be mitigated by having specialized staff only manually reviewing exceptions and low-confidence results. Financial institutions utilizing machine learning can keep labor costs stable while drastically decreasing compliance risk.
Maintaining data integrity and ensuring reporting satisfies HMDA and CRA criteria is crucial, and the task becomes more difficult as regulations become increasingly stringent. With these regulations comes the necessity of implementing strong controls in order to mitigate compliance risks in the fair lending space.
Manual processes such as scrubbing for data points across documents, LOS, LAR and error identification when sorting through loan documents, decrease data quality because of human inefficiencies and mistakes, which negatively impact report dependency. And for a financial institution, providing quality data is paramount; they know compliance cannot be skimped on, no matter the cost. As a result, a key pain point among financial institution leaders is the staggeringly high compliance costs that will accompany the 1071 regulations. Machine learning systems not only limit spending costs on labor, but they also maintain high data standards and prevent the sky-high fees that failure to comply with said standards can incur.
Digital transformation and automation systems in the loan space make all these issues virtually disappear. Financial institutions can have peace of mind knowing that they have 100% compliance review penetration on their loans— exponentially more reliable than traditional sampling methods— thus reducing compliance risks, increasing quality and lowering overhead costs.
Leaders are starting to raise questions about how to solve the difficulties 1071 will create for their institutions, and many are realizing that machine learning is the best solution to their looming data compliance problem. In order to ensure complete data integrity and stay ahead of the compliance curve, automation is an appealing alternative to traditional methods. With compliance hurdles successfully removed, bank directors and c-suites can set their sights on proceeding confidently with their growth initiatives.
“In a fast-growing bank, none of us have time to manually scrub the data. We have to be able to get out of the minutia, look at the big picture and focus on where we’re going strategically. The more processes we can automate, the better,” adds Deana. “Automation frees up that time for so many other great things that we can achieve; allowing more time for reviewing community development opportunities and for determining how we can do more for the communities that we serve.”
Will Robinson is CEO at Encapture, an intelligent automation platform providing banks with documentation efficiency. Using machine learning and AI technology, Encapture helps banks reduce compliance risks associated with Fair Lending Guidelines, reduce overhead costs, and improve profitability in a volatile market. For more, please visit https://encapture.com/.