Artificial Intelligence (AI) has arrived and is set to transform the financial services industry in the upcoming years. Many AI projects have successfully transitioned from a one-off proof of concept to organization-wide strategic scaling to enable business strategy and sustainability. Growth in AI spending continues to accelerate as firms manage to generate significant returns on investments (ROI) and further their transitions from strategic scaling to industrialized growth. According to one leading market intelligence provider, International Data Corporation (IDC), worldwide spending on cognitive and AI systems is forecasted to reach $77.6B in 2022[1], more than three times the $24.0B estimated in 2018. Gartner, another major IT research firm, predicts the business value created by AI will reach an astonishing $3.9T in 2022. Given these impressive returns, it is easy to see why successful institutions continue to invest in AI solutions.
Planning for Regulatory Change
As we march forward in 2020, amidst the global COVID-19 pandemic, the financial sector remains a bright spot for AI expansion. LIBOR transition and other key financial services initiatives require significant legal agreement remediation efforts. Financial institutions maintain a wide array of legal agreements that define the relationship and rights between their firms and their lending and derivatives counterparties. For large firms, legal agreements range from standard credit card contracts for retail clients to highly customized, one-off agreements for esoteric structured finance arrangements. In addition, most financial institutions do not maintain a single central repository for all their borrowing and derivatives counterparties. Most firms don’t have efficient solutions in place for identifying and updating large numbers of legal agreements subject to review and amendments. However, this is a key requirement to achieve compliance with current regulatory efforts, prepare for the upcoming LIBOR transition, or manage changes required due to Brexit.
Many U.S. financial institutions are currently addressing the costly and complex challenge posed by the transition away from USD LIBOR[2] benchmarks. The U.S. LIBOR transition alone impacts over $200T in notional exposures that are governed by vast numbers of legal agreements. At one leading U.S. financial institution, LIBOR transition will require the identification, review, and amendment of over five million individual legal documents.
Accelerating Speed and Accuracy
The process of identifying and amending millions of documents can be tackled in several ways. Firms can perform a manual review of all in-scope documents, conducted by their own resources or in collaboration with law firms. A strictly manual review process is often costly and error prone. Accurately identifying all the terms that need to be amended in bespoke and customized legal agreements requires significant experience and skill, which typically translates into high resourcing costs. Yet despite the high costs, manual reviews typically continue to suffer from inconsistent results and high incidents of errors and omissions.
AI solutions have been developed to specifically assist firms with the legal documentation review process. Several solutions have gained traction in recent years, facilitating the arduous and costly process of “re-papering” financial contracts. The ability to consistently and accurately process millions of lines of contractual language and produce auditable results gives the leading AI tools an important advantage. At the same time, it is critical to understand that not all AI solutions are created equal and not all tools are equally adept in addressing the major contract remediation challenges.
Getting Smart
Current-state AI solutions do not offer fully automated re-papering capabilities but they can still greatly reduce the overall effort required. This is particularly beneficial for large financial institutions that have the ability to scale the technology across a large number of contracts with similar characteristics and structure.
Once a financial institution has identified the full-scope population of agreements subject to review and amendments, AI re-papering tools can be deployed to great effect. The automated extraction of key contract data fields such as effective date, termination date, legal jurisdiction, and contract language allows firms to accurately categorize legal agreements. This helps in the prioritization of additional review work and the elimination of agreements that are not subject to amendments. Although humans continue to provide the criteria and intelligence required to manage a large-scale contract remediation effort, AI tools execute the cumbersome and time consuming reading and scanning tasks.
Successful implementation of AI solutions involves multiple training cycles. Initial AI extraction results typically contain significant levels of errors and discrepancies. During the training cycles, AI tools need to learn how to consistently read and interpret legal agreements with similar attributes and characteristics. This exercise is critical to the success of any AI tool. In a recent project managed by Monticello, the initial accuracy of a newly introduced AI tool yielded 40% accuracy. After a careful assessment and further training cycles, the accuracy level rose to over 90%. At this stage, AI tools are capable of executing repetitive and time-consuming tasks, but they very much depend on humans to provide them with criteria and hands-on training to achieve successful outcomes.
A Range of Possibilities
The market for AI tools and services is highly fragmented. Several vendors (e.g., eBrevia, Seal, Kira, Brightleaf) are looking to provide end-to-end solutions that include adjacent services such as contract lifecycle management, electronic discovery, and document storage. Smaller niche vendors (e.g. della ai, Apttus) tend to focus on a core competency such as contract re-papering and/or contract data extraction. These smaller firms often work in partnership with outside consulting firms to deliver larger scale implementations to their clients. Depending on their specific needs, financial institutions may benefit from additional savings by purchasing bundled products and services. Other firms may be better off purchasing specialized capabilities and tools and integrate those into their existing agreement repositories. Regardless of the approach, AI is transforming the way we manage contracts and a necessity to stay competitive in today’s marketplace.
About Monticello
Monticello Consulting Group is a specialized service provider with deep knowledge and expertise in regulatory reform along with business and digital transformation. Our understanding of the competitive forces reshaping business models in capital markets, lending, payments, and digital banking are proven enablers that support our clients to remain in compliance with regulations, innovate to be more competitive, and gain market share in new and existing businesses. By leveraging our core practice areas covering Digital Transformation, Change Management, and Financial Services Advisory, Monticello continues to guide its clients in the deployment of the latest technologies while managing some of the most transformational programs of change.
[1] Columbus, Louis. 2019. Roundup of Machine Learning Forecasts and Market Estimates for 2019. March 27. https://www.forbes.com/sites/louiscolumbus/2019/03/27/roundup-of-machine-learning-forecasts-and-market-estimates-2019/#44c1f4d37695
[2] The New York Federal Reserve n.d. Transition From LIBOR Accessed April 23, 2020 https://newyorkfed.org/aarc/sofr-transition.
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