Staying compliant with regulations is paramount to institutions and a top priority for C-suite executives. Financial institutions in particular are frequently under regulatory scrutiny from their jurisdictional regulators such as Securities and Exchange Commission (SEC), Office of the Comptroller of the Currency (OCC), Federal Deposit Insurance Corporation (FDIC) and Federal Reserve Board (FRB) in the U.S. These regulators continue to reinforce core banking governance, risk management, capital adequacy and liquidity management regulations (i.e., Dodd-Frank) implemented in the wake of the global financial crisis in 2008. Failure to comply with regulations could result in fines that reach millions of dollars and damage a bank’s reputation. According to a recent study, the largest U.S. banks have incurred a total of close to $200 billion in fines and penalties over the past 20 years [1]. As a result of regulatory actions, often in the form of Matter Requiring Attention (MRA’s) or Consent Orders (CO’s), financial institutions are taking extra caution to stay on top of the regulatory compliance requirements. In recent years, banks have made significant investments to scale and increase headcount in order to support their Risk and Compliance function and are increasingly turning to emerging technologies to remediate regulatory compliance risks.
Why Are Emerging Technologies Increasingly Being Used To Remediate Compliance Risks?
Data is critical for decision-making, not only for financial institutions but also for jurisdictional regulatory bodies to monitor system-wide risks. Banks produce a litany of data in their operations and for regulatory reporting. Regulatory reporting requires the collection and aggregation of terabytes of institutional data among various data sources and systems. Without properly controlling and governing data, regulatory reports may contain inaccuracies that lead to compliance, audit or regulatory scrutiny down the road.
Financial institutions are experiencing increased regulatory attention in areas of Data Management & Governance, Cybersecurity, and Compliance Reporting, to name a few. Enforcement actions often highlight concerns about deficiencies in institutions’ internal controls and their data integrity practice. Improving data governance and reporting quality is an integral part of banks’ corrective action plans to remediate the regulatory compliance risks.
To that end, many banks have resorted to establishing vast data-remediation programs with hundreds of dedicated staff involved in mostly manual data-scrubbing and normalization activities to analyze, prioritize, and remediate data issues. These data-quality remediation efforts consume a significant amount of time and resources, creating massive backlogs. As a result, banks are increasingly relying on emerging technologies and data analytics to help them better address data issues and remediate compliance risks. Emerging technologies, such as Machine Learning (ML) and Nature Language Processing (NLP), can improve institutions’ risk compliance operations by identifying insights, reducing errors rates, and improving data quality from vast troves of data.
How Will Natural Language Processing Be Leveraged To Improve Data Quality And Internal Controls?
NLP is a growing area of AI in part assisted by rapid growth in computing power and data handling capacity at financial institutions. NLP can be used, along with operational controls, to improve data quality and remediate regulatory compliance risks.
Successful application of NLP, in the case of data quality control and regulatory compliance reporting, could entail capturing unstructured document content, classifying and enriching data from model training, identifying data anomalies, and flagging them for correction
Automation Turn unstructured data into a more usable form, using document automation and digitalization technologies.
Data Enrichment Add data categories, linkages, and reference data to the baseline data captured, and from model training & learning algorithms, to make it more actionable.
Search and Discovery Flag data for user review and actions, using predictive discovery capabilities.
One NLP use case, for instance, is to improve data quality and regulatory reporting in retail and commercial lending space. At a typical bank, business lending is a standard process. It involves a fair amount of standardized paperwork and manually intensive tasks to capture and maintain lending facility information. Different systems are used by loan officers and operational staff to capture and maintain the information, introducing data issues in the supply chain. NLP can be effective in automatically categorizing loan documents and extracting relevant information from them. Learning algorithms can be set up and trained to identify data issues with a high degree of accuracy between what is captured systemically and what users manually enter into systems. Data issues can be flagged for user intervention.
Speed to Market: Build vs. Buy
As financial institutions embrace risk remediation through process automation and the adoption of emerging technologies, they are often faced with the Build vs. Buy decision and the challenges of having the right resources and capabilities to execute the project. Technology vendors have attracted most of the technical talent. Likewise, several professional services firms have made sizeable investments in the last few years and introduced a suite of compelling product and service offerings.
Looking to accelerate the solution delivery, banks are left with fewer options other than forming strategic relationships and buying products/services from technology vendors or professional services firms, while attempting to build out their in-house capability and talent. As a result of the market dynamics, partnering with the right technology vendor and/or service provider is as crucial as adopting a suitable technology solution offering. Emerging technologies such as NLP, when combined with the right strategic technology partner and/or service provider and a strong set of control frameworks and operational processes, can be a viable solution in a bank’s regulatory compliance risk mitigation plan.
About Monticello
Monticello Consulting Group is a management consulting firm supporting the financial services industry through deep knowledge and expertise in digital transformation, change management, and financial services advisory. Our understanding of the competitive forces reshaping business models in capital markets and digital banking are proven enablers that help our clients drive innovative change programs to be more competitive and gain market share in new and existing businesses.