By Stuart Brock, Director, Seal Software
In the decade following the global financial crisis, banks have faced a flood of new laws and regulations. The pace of change has been furious. Banks have been forced to hire more and more bodies to manage large, enterprise-wide efforts in an attempt to simply stay ahead of regulatory enforcement actions and the ensuing fines and penalties.
As a result, banks have spent more than $321 billion[1]on enforcement actions, fines and settlements since 2008. This year alone, banks have paid $1 billion in Unfair and Deceptive Acts and Practices (UDAP) penalties, $109.5 million in fines for foreign exchange (FX) practices, $5.3 million to settle Office of Foreign Assets Control (OFAC) allegations and $4 million to settle Securities and Exchange Commission (SEC) charges[2].Banks spend $270 billion per year on compliance. Some 10 percent or more of most bank operating costs can be attributed to compliance, and some estimates have regulatory costs doubling by 2022.
Going forward, banks must develop strategies that cut costs, but still allow them to maintain robust compliance programs. To do this, they are increasingly turning to artificial intelligence and analytics technologies to support the ongoing development and deployment of their compliance programs. These technologies are faster, incredibly cost-effective, and able to support key aspects of a robust compliance program, including:
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Risk Assessment– Traditionally, risk assessments are conducted by large teams of people scouring business documents to identify compliance obligations. In a fraction of the time, AI technologies can identify documents containing relevant compliance obligations where those documents are already stored across the enterprise without the need to collect or migrate the documents to a central repository. These technologies then facilitate the mapping of those obligations to the appropriate laws, rules and regulations so that the impacted business units can more fully understand their compliance obligations.
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Regulatory Requirements Library –Most enterprise compliance programs consist of some type of library of applicable regulatory requirements and associated governance and controls. Maintenance of these libraries is largely an exercise requiring manual review and updates on a recurring basis. Most updates are performed annually given the level of manual resources needed. AI Technologies can nearly instantly identify library components that require updating whenever there are external or internal changes. This allows these libraries to be updated in real-time supporting more timely compliance by impacted business units.
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Responses to Regulators – Banks are often required to provide regulators with data both on an ongoing and ad hoc basis. In some instances, such data must be provided within 48 to 72 hours. This forces banks to track known data points and anticipate additional data that might be requested by regulators. In the past, banks struggled to timely provide data for ad hoc requests where that data was not already tracked. AI technologies allow banks to address these ad hoc regulatory requests as they are received and within the significantly compressed timelines.
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If you are a bank compliance officer looking to maintain or improve compliance while also controlling costs, where do you start?For many, getting into compliance requires employing armies of reviewers to read through each contract, flagging and prioritizing those that need remediation. It’s a costly and inefficient process, typically stretching out over weeks and often months. It’s also unavoidably prone to error, especially at a level where thousands upon thousands of items must be reviewed.
As a result, companies are now turning to analytics to discover the contractual documents that apply to current regulations, and to understand what terms are contained in them so they can be properly processed and brought into alignment. Artificial intelligence (AI) is in many ways a game changer in this regard. It has taken this activity out of the costly, unreliable domain of manual processing.
Automated approaches, that employ AI for contract analysis and intelligence, can be pointed at the various places, where contracts and agreements are suspected to reside. The technology can not only identify relevant contracts, but then go to work using a series of algorithms to make them easily classified and searchable.Advances in AI also mean these tools can be taught to correspond insight with both the direct requirements and the indirect implications of current regulations.
The long list of topics that must be addressed for compliance are enough to keep business leaders awake at night. Particularly for organizations with contracts and other fragmented data sitting across multiple silos. All owned by different business units, lacking a complete enterprise view. Machine learning techniques -a type of AI – are the only practical means of getting actionable visibility into the contract-based data that is flowing into and out of an organization.
The imperative facing banks and the financial sector is to put real power into the hands of those who need reliable information, so they can make smarter and more strategic decisions about compliance. This is an industry that has always been at the leading edge of technology adoption, and AI is no different. Competitively, we see contract analysis using AI more and more vigorously applied to meet growing regulatory challenges, making it clear that the time to act is now.
References:
[1]https://www.bloomberg.com/news/articles/2017-03-23/banks-trimming-compliance-staff-as-321-billion-in-fines-abate
[2]https://www.bankersonline.com/penalty