Next-Generation Case Management for Financial Crime Risk Management

Context: taking a central role

Case management plays a pivotal role within compliance at banks and financial institutions. In a complex regulatory landscape, organizations rely on robust case management systems to identify, investigate and resolve potential fraud and financial crime. The meticulous documentation and analysis involved in case management ensures that each case undergoes a thorough investigation, enabling analysts to make informed decisions about cases and determine their regulatory reporting obligations.

By centralizing information into a unified enterprise case manager, fraud and financial crime teams can remove siloes, unify their view of risk and gain efficiencies of scale. Effective case management enhances a financial institution’s ability to efficiently and effectively investigate, prevent and report risky and improper behavior, safeguarding both the institution and its customers from illicit activities.

Market dynamics

Several key trends have led to the centralization of case management capabilities within client lifecycle risk management (CLRM).

• Cost savings and efficiencies have become a priority. The advent of a data-rich environment and more intricate technical infrastructures, with layered databases, feeds, analytics and cloud architectures, has propelled the need for the next generation of enterprise case management solutions. Integration has become a priority, as financial institutions seek to streamline operations. Case management serves as the central hub, enabling seamless integration with third-party services and solutions tailored to each institution’s unique needs. Cloud-based platforms, containerization and flexible application programming interfaces (APIs) have emerged as key enablers, allowing for a modular approach to integration and ensuring adaptability to evolving demands. Technology vendors have responded by expanding their partnerships and integration capabilities, offering financial institutions a customizable array of solutions so they can navigate the complexities of compliance and operational efficiency effectively.
• Broad-based integration is a necessity. Digitization has brought with it the need for flexibility. The technological environment has deepened, as individual and corporate sources of data have grown in both depth and availability. Interpreting corporate structures is now a common requirement across both fraud and financial crime; other areas of business, such as operations and legal, have specific requirements, including integrating transaction processing, account maintenance and customer service, and centralizing legal document management. Institutions have looked toward their workflow and case management functions as the central point around which all of these can be managed effectively.
• More demand for connectivity. As case management has assumed a more central role, the demand for connectivity with a diverse range of third-party services and solutions has escalated. Each institution possesses a unique set of requirements, ranging from integrations with biometric or ID verification (IDV) systems to managing cross-border activities and interfacing with trading systems. Cloud computing, containerization and flexible APIs have emerged as pivotal strategies to facilitate seamless integration of a wide array of plug-ins. Moreover, vendors have prioritized expanding their networks of partnerships and integration strategies, offering a customizable array of solutions to cater to individual institutional needs through an ‘a la carte’ arrangement.

Enterprise case management: evolving functions

Within this rapidly changing environment, with more connectivity and deeper integrations, case management functions themselves are evolving to keep up:

• Workflow optimization. Low-code and no-code solutions have democratized workflow functions, allowing individuals to focus on more complex tasks. Enabling individuals to build their own tasks and adjust parameters to suit the idiosyncrasies of their own vertical, institution or business line has meant that financial institutions are more able to configure their case management frameworks to their own specific requirements, including being able to integrate a wider array of capabilities, or pass data-like corporate structures between different business lines. This essential process of ‘de-skilling’ can be seen as the first step on an automation journey that AI can potentially enable moving forward.
• Generative AI. The growth of large language models (LLMs) and generative AI (GenAI) has opened an array of potential strategies for case management solutions. On one level, these can be seen as the logical extension of the low-code/no-code paradigm: providing a way to summarize complex information or give detailed instructions to a framework using simple instructions or even prose. However, there are also several clear risks: the sheer flexibility of an LLM means that it cannot simply be plugged into analytics. The second is that they can be used for pulling complex information together and summarizing. In this case, the risk can be that many of the new GenAIs are predictors rather than search and retrieve functions. This can lead to the errors popularly known as ‘hallucinations’. GenAI is clearly a powerful tool for investigations, as it can synthesize all pertinent case data effortlessly; however, it is essential that proper guardrails are put in place to ensure data privacy and security.
• Alert and case summary. Summarizing complex data from several sources can be challenging. GenAI can effortlessly synthesize large and fragmented datasets to deliver an alert or case summary in advance of an investigator opening it. This can remove a significant amount of manual time and effort from an investigation.
• Report generation. GenAI can be utilized to craft suspicious activity report (SAR) narratives. These can often be difficult and time-consuming to do manually but can be built relatively swiftly with GenAI. It is important to ensure that each piece of information in the report is auditable and can be traced back to its source information to avoid the risk of hallucinations or other errors in the AI.

Within both areas, audit and explainability are crucial. Regulators and stakeholders in institutions need to know that the data they are viewing is accurate. Knowledge of how these requirements work, and the common ways in which they can go wrong, is crucial.

Enterprise case management: looking to the future

Case management has grown in complexity as more capabilities and functional areas have been integrated into its frameworks, and the function must evolve to meet new challenges. New GenAI technology can provide significant automation benefits, if it is built to be explicable and traceable. Although disruption is commonplace within the technology space, remaking case management functionality will not be a task for compliance neophytes. Knowledge of the rigors of compliance and the complexities of regulations is perhaps more important than technological expertise in this process. The synthesis of technology and expertise will inform the next generation of case management.

The need for evolution in case management also underscores the importance of adaptability in the face of changing regulatory landscapes and emergent threats. As regulatory requirements become increasingly stringent and sophisticated, case management systems must be agile. Moreover, the proliferation of digital channels and transactions is amplifying the volume and complexity of data that must be managed and analyzed. As a result, the next generation of solutions must be equipped with advanced analytics capabilities to sift through vast datasets, identify patterns indicative of potential financial crimes and facilitate timely intervention. By leveraging cutting-edge technology alongside deep domain expertise, financial institutions can enhance their resilience against financial crime while optimizing operational efficiency and regulatory compliance.