Ensuring regulatory compliance standards can be expensive, and this is especially true in banking, financial services and insurance industries (BFSI) where compliance spend has increased to unsustainable levels. Globally, LexisNexis research estimates that financial services companies paid nearly $200 billion in compliance costs in 2020.
That’s a lot of money, and, unfortunately, a lot of this expense is unnecessary. Compliance is still, for many organizations, a highly manual process, and manual processes are rife with errors. Additional numbers from LexisNexis bear this out: Of that aforementioned, almost $200 billion in compliance costs, nearly 60% went toward paying for labor. The other 40% went to technology. I’d argue, however, that these expenditures probably didn’t go to the right places.
Compliance Debt: A Vicious Cycle
The conventional approach for BFSI leaders looking for solutions within compliance has been to add more people, more training, more quality control layering, more fuzzy logic matching, more monitoring, more searches. Many leaders view these as their best available methods to mitigate their risk, both in terms of quality and cost. Essentially, the only way to fix the problem has been to continuously throw more time and resources at it.
In the short-term, this may provide immediate benefit. Unfortunately, what most leaders don’t account for is the technical and organizational debt (compliance debt) that far outweighs the benefits originally sought. Think of a team introducing a new technology tool or new process to catch and fix errors produced by human labor; this is an example of compliance debt. As that debt grows, they invest more in people and technology, creating a vicious cycle of taking on more debt and not being able to pay it off — and this keeps accumulating.
Reducing Compliance Debt
In order to correct for this, banking leaders must find a way to “pay down” this debt, reducing costs and, more importantly, reducing risks for the organization. They need to look beyond lightweight, short-term tools and engage with technologies that incorporate real expertise and can tackle problem statements across processes. This requires not just strong technology but true collaboration across technology teams and business sponsors. Failing to do so only accrues more compliance debt.
Consider customer and enhanced due diligence, for example. Banks are already allocating large amounts of time and money toward monitoring news databases for negative reports during the customer onboarding process and throughout the life of a customer relationship. It’s a process that’s laborious, expensive and traditionally heavily manual. Even when done well, it’s still surprisingly inaccurate. Traditional, rules-based automation struggles to streamline this important task because the data is so unstructured. Compliance debt can come in the form of analysts missing a screenshot, missing a material result, not saving results according to procedure, having weak disposition language, etc.
Leading financial institutions (FIs) are adopting approaches to thoroughly identify any adverse news coverage about an existing or prospective customer and satisfy regulators (who are becoming increasingly particular about this work). By finding a new approach that functions across all customer and enhanced due diligence areas, these FIs are able to search more news yet still generate success metrics like reducing manual work, improving accuracy and reducing false positives.
Other compliance processes have similar heavily manual situations. Look at economic sanctions alert review, identification of beneficial owners and other KYC data that is currently typically contained in reams of paper documents. Each of these compliance areas have typically been addressed by an ever-increasing manual presence or incremental technologies — and all of which are increasing the compliance debt. But by taking a transformative approach by implementing a solution that works across these many problems, organizations are finding ways to pay down their compliance debt.
Solving For The Bigger Picture
Yet compliance debt is just a subset of a larger problem: operational debt. Many areas outside compliance still see operational challenges as problems that can be solved by simply adding more staff or tools — but that thinking leads to these broader issues. By applying the right technologies, organizations can build out processes that allow them to achieve more, do it faster and also break these costly cycles.
Another banking example: Mortgage origination is a document-heavy process that, when handled traditionally (manually), requires teams of analysts and underwriters to complete multiple tasks, such as verifying applicants’ incomes. With all this staff, it’s also time-intensive and expensive.
In the insurance industry, First Notice of Loss (FNOL) procedures are very similar. This process requires documents that come from disparate sources in multiple formats. Customers pressure insurers to handle claims quickly, and insurers do their best to manage a high number of claims. Making sure it’s all done smoothly is critical to customer retention.
These examples can be paired with other similar cases across the organization which require a more holistic, transformative view rather than incremental approaches with limited long-term improvement.
Finding The Way Out
Any organization can recognize that they are in detrimental cycles. Many even recognize that technology is a way out. But not all technology is the same. Institutions must reinvent and reconfigure traditional processes to address cost, compliance and capacity challenges, yet avoid compliance and other operational debt and allow enterprises to meet increasing pressures more effectively and efficiently. That is best accomplished by augmenting, not adding to, their workforce.
By choosing transformational tools to augment their analyst teams, banks, financial services and insurance companies can tackle multiple concerns more efficiently and more effectively.
This article originally appeared on Forbes.com.
of Intelligent Automation
of Intelligent Automation