6 Common Pitfalls of Building an AI Agent for Financial Crime Compliance In-House 

While many banking and financial services organizations may have what would be considered an effective compliance program, most programs are very inefficient, relying on manual processes and large amounts of human capital. Hiring more people — outsourcing, offshoring or temporary workers — simply will not fix the foundational challenges that have existed in financial crime compliance (FCC) operations for the past 20 years.

It’s not a matter of “if” organizations will adopt AI solutions to strengthen their AML/KYC operations, it’s a matter of when. There’s a shift currently underway and many organizations are recognizing that AI and automation technology can manage and mitigate AML and sanctions risk more effectively while increasing efficiency and productivity. This, in turn, frees employees to focus on riskier customers, transactions, and higher-value work.

AI agents are automations that can execute complex, multistep workflows across digital environments and apply reasoning. They are a combination of traditional AI/ML, GenAI, and human collaboration. They can make decisions, plan, and adapt to achieve predefined goals. These AI agents automate many of the manual and repetitive tasks that compliance analysts have traditionally handled, freeing up millions of hours for investigators to focus on higher-value investigations to strengthen AML/KYC programs.

While many banks and FIs are considering AI Agents, some attempt to build their own AI agents in-house. This is a very lengthy, high-cost proposition and one that fails to give organizations a regulatory-ready, end-to-end solution that can solve their customer satisfaction, revenue impact, and staffing challenges.

Reasons In-House Builds Fail

1.      Complexity: Building an end-to-end automated solution with system integration, cascading ensemble models, and model governance to solve this problem statement often can take up to a year. Machine learning model development requires data collection, cleansing, experimentation, multiple internal compliance reviews and testing.

2.      Total Cost of Ownership (TCO): The cost to develop, build, configure, test, train and maintain machine learning models with integrations and environment provisioning will exceed the cost of volume-based pricing.

3.      Significant time and investment: Up to 90% of internal builds fail to materialize because these projects are difficult and require significant financial investment and time. Once a cascade of models is designed, annotated, built and tested, most internal machine learning projects fail at this step and can’t overcome this barrier. Internal builds do not address degradation over time.

4.      Model explainability: A top challenge for financial institutions is ML explainability and model risk management (MRM). Industry research is still early on ML explainability which requires dedicated research to make it practical and conforming to regulatory requirements.

5.      Addressing built-in Case Management and Human-in-the-Loop capabilities: Internal builds often fail to recognize case management and human in the loop capabilities. This results in high cost of maintenance and bespoke exception processes. Creating an equivalent custom case management system is an entirely separate project above and beyond the machine learning project.

6.      Finely tuned models: Building and maintaining a machine learning model limited to a bank’s own data requires years of alerts before finely tuning a usable model.

Why Build When You Can Buy Proven, Trusted, Pre-Built AI Agents?

WorkFusion helps organizations overcome all of these challenges by delivering pre-built or customizable AI Agents. We ensure an institution’s MRM by providing explainable AI in all our products. Each WorkFusion AI Agent’s AI and ML is a “glass box” model, allowing your compliance team to easily understand and clearly explain to regulators. This satisfies regulatory examiners who view transparent MRM as essential to AI and ML deployments. Typical implementation for a WorkFusion AI Agent takes approximately 6-12 weeks.

According to Deloitte, “Today, FIs have multiple options when it comes to implementing and adopting digital capabilities and leveraging cognitive technologies like AI and natural language processing. By taking a buy approach, FIs can benefit from these cutting-edge capabilities quickly, typically within 12 months, and apply them to solving issues and market demands. They also can typically take advantage of these breakthrough technologies without having to hire subject-matter experts, who are in short supply. Vendors have laid all the complex groundwork through their productization processes.”

WorkFusion’s AI Agents are already proven and trusted at leading organizations around the world, including 4 of the top 5 US banks. They are substantially reducing the work time needed to resolve the endless flow of low-value customer and transaction “alerts.” By underpinning automation of many of the manual and repetitive tasks compliance analysts have suffered through for decades, AI Agents are freeing up millions of hours per year so that investigators can focus on actual risky activity and enable financial crime compliance executives to better allocate people and budgets. These AI agents are ensuring AML officers have greater confidence that work is done consistently, backlogs don’t accumulate, and compliance with policy and regulations is significantly strengthened.

If you would like more information on WorkFusion’s AI Agents, please schedule a demo.