Webinar Recap: The Secrets to Productivity in FinCrime Operations

The webinar on The Secrets to Productivity in FinCrime Operations focused on addressing common productivity challenges in financial crime compliance using AI-driven tools. It was moderated by Rogier Binsbergen and featured two prominent experts: Theresa Bercich, Chief Product Officer and Co-Founder from Lucinity, and Phoebe Kao, Head of Compliance C+AI from Microsoft.

The discussion emphasized overcoming fragmented systems, automating routine processes, and integrating AI to streamline operations in compliance teams. Each speaker provided insights into leveraging technology to address the rising complexities of financial crime and regulatory pressures.

Key Insights From The Webinar

In this webinar, experts from S&P Global, Lucinity, and Microsoft explored how AI can enhance productivity in financial crime operations. Here are the areas of discussion and respective expert insights from the meeting:

Introduction: The Significance of Productivity in Fincrime Operations

Rogier introduced the topic by referencing Gartner Research, which shows that 37% of businesses are looking for more integrated, end-to-end AML platforms. Additionally, 23% prioritize reducing total cost of ownership (TCO), and 20% focus on higher detection rates and accuracy. Rogier highlighted that improving productivity in AML operations, particularly in case management, is crucial. Even small productivity gains can significantly outweigh the costs of adopting new AML platforms, making the discussion highly relevant for organizations looking to improve compliance and efficiency.

Question: What are the greatest barriers to compliance team productivity?

Theresa Bercich explained that the RegTech landscape has undergone a significant transformation due to the advent of generative AI, particularly with tools like ChatGPT, which introduced the public to AI in an accessible way. This shift is driven by two main factors: increased global regulatory pressure on financial institutions and the growing complexity of financial crime. Criminals are leveraging advanced technologies, making both detection and investigation more difficult.

Theresa emphasized that this has led to a “productivity crisis,” where fragmented data, manual processes, and high compliance costs impede efficiency. According to Gartner Research, about 37% of businesses are actively seeking end-to-end AML platforms to integrate their processes, while 23% prioritize reducing Total Cost of Ownership (TCO). Furthermore, 20% of respondents from the same research stressed the importance of higher detection rates and greater accuracy.

AI is central to resolving these challenges by improving productivity, reducing manual tasks, and enhancing accuracy in investigations. Theresa cited various solutions, such as using transformers (the underlying technology behind generative AI) to automate repetitive tasks, boost consistency across processes, and improve detection rates. She referenced studies from Gartner and McKinsey, affirming that integrating AI into compliance processes will be essential for financial institutions to stay ahead of the ever-evolving criminal tactics and regulatory demands.

Theresa emphasized that generative AI, if used correctly, can address fragmented systems, enhance workflow automation, and empower investigators with more actionable intelligence.

Question: What are the solutions for the top two barriers identified in the poll, which are manual and time-consuming processes, and fragmented systems and siloed data?

Theresa agreed with the poll results, highlighting that the challenges related to manual processes and fragmented data align with what Lucinity has observed. She confirmed that technology offers solutions to these issues by significantly boosting productivity within financial institutions.

She emphasized the need to leverage AI and automation to combat these problems effectively. She discussed how workflow automation and configurable AI systems can streamline tasks, making investigators and compliance teams far more productive. She further noted that using AI ensures consistency across tasks, reduces human variability, and helps integrate data sources across different platforms, preventing the siloing of critical information.

Theresa elaborated on the benefits of AI in financial crime investigations. She mentioned that automation of repetitive tasks, such as gathering data from disparate systems, allows analysts to focus on decision-making rather than time-consuming manual work. Through Luci, Lucinity’s AI-powered tool, users are empowered to make more informed decisions in less time by seamlessly pulling insights from various sources, creating a single view of data without the need to replace legacy systems.

This helps reduce the burden on analysts, decreasing case handling time and improving accuracy across investigations.

Question: How is AI transforming the compliance industry, and what advancements are seen from Microsoft's OpenAI?

Phoebe Kao, from Microsoft, explained that AI offers huge potential in transforming compliance, primarily through automation and cost reduction. According to her, studies show that 6 out of 10 compliance officers experience burnout, and 4 out of 10 feel anxiety from the stress of their roles. AI helps alleviate this by automating laborious and repetitive tasks, enhancing job satisfaction.

She differentiated OpenAI and Azure OpenAI. While OpenAI focuses on AI research and development, Azure OpenAI provides enterprise-grade features, including content safety, responsible AI usage, and regulatory compliance.

Phoebe also noted significant improvements in document processing and regulatory analysis, particularly for vendor onboarding (KYC/KY3P). AI streamlines processes by aggregating data faster and automating workflows for compliance teams.

With GitHub Copilot, 88% of developers reported higher productivity, and task completion became 55% faster. Phoebe expected similar improvements for compliance practitioners, as AI takes on the manual tasks, leaving humans to focus on decision-making and higher-value tasks.

Question: How can financial services companies address their challenges with siloed and fragmented systems in compliance workloads?

Theresa highlighted that financial institutions need to tailor compliance processes to their unique structures. Rather than replacing legacy systems, it's essential to adopt platforms that integrate with existing fragmented data sources and provide a unified view. This approach allows firms to boost productivity without overhauling infrastructure. Furthermore, leveraging generative AI can automate tasks, enhance data consistency, and streamline investigative processes, reducing reliance on manual interventions. By designing platforms with future scalability, companies can adapt as regulations or data requirements evolve, ensuring long-term flexibility and collaboration.

She explained that this solution can sit atop existing data sources, complementing what’s already in place. It streamlines workflows and provides automated insights. This approach avoids the pain of "rip and replace" strategies and allows for seamless case management across various alert types such as KYC, AML, or fraud investigations. Having flexible, scalable systems that can adapt to evolving needs is key to success, alongside the ability to standardize processes via automation and AI to minimize human errors.

Theresa elaborated on how these platforms offer cross-departmental collaboration by creating a unified data space where financial crime teams can work efficiently without data falling through cracks. The generative AI capabilities provide automated decision-making aids and workflow standardization, reducing manual labor and human variability.

Question: What methods and frameworks do you recommend for making generative AI outputs more explainable to business users?

Theresa explained that creating explainable AI starts with building a transparent framework. Simply adding a chatbot or similar tool won’t ensure explainability. Instead, financial institutions need a configurable system where users can access audit logs, see how decisions are made, and understand the AI’s rationale. Tools like configuration studios help map out data sources and AI processes, providing transparency.

Phoebe added that explainability depends on the audience. For end-users impacted by AI (like credit applicants), explainability involves detailing why certain decisions were made. For internal users, the focus is on observability—monitoring data and understanding how the AI model operates across various layers. This means continuously assessing the performance and fairness of AI models while ensuring that results are easily interpretable.

Both agree that separating explainability into internal and external channels is crucial for tailoring the communication of AI’s decisions.

Question: How do you train new analysts or other colleagues to go from more junior to experts or to maybe even reinvent themselves when AI is being used to do more manual tasks with case investigations, for example, or other operational tasks?

Theresa explained that when training new analysts, the cognitive load of learning processes, like evidence collection and report writing, can be high. AI, like Lucinity's Luci, can streamline these tasks, allowing analysts to focus on decision-making from the start. By automating manual processes, junior analysts can better understand why decisions are made, rather than just learning the steps.

Lucinity’s knowledge graph helps map regulations to tasks, providing real-time feedback and explanations, ensuring both junior and expert analysts stay informed. This allows them to become proficient decision-makers faster, focusing on important insights rather than repetitive procedures. The AI ensures standardization of workflows, reducing variability and promoting consistent outcomes.

Phoebe added that AI is not replacing humans but empowering them. To bridge the gap between manual and AI-driven tasks, analysts can engage in continuous learning through accessible resources, including courses and YouTube tutorials. She highlighted the importance of taking small, manageable steps to master AI tools, ensuring that analysts remain at the forefront of the evolving industry.

Both Theresa and Phoebe agree that AI provides a foundation for analysts to grow into experts, automating mundane tasks so they can focus on higher-level strategic work and decision-making, all while reducing burnout and enhancing job satisfaction.

Question: Can you share some thoughts around best practices for integrating AI into existing compliance systems or ML systems?

Phoebe emphasized the importance of workflow integration when augmenting AI with existing compliance systems. She highlighted that workflows should be designed to smoothly guide tasks from beginning to end. Timely updates and notifications are crucial for practitioners, ensuring they focus only on critical anomalies. This reduces the need to monitor systems 24/7. Senior management also benefits from insightful analytics, allowing for informed decision-making. These workflows enhance productivity by automating routine tasks and reducing human error, ensuring AI's integration is efficient and human-friendly.

Theresa agreed and added that cross-departmental collaboration is key, as compliance teams often struggle with fragmented systems. AI platforms, such as Lucinity’s, allow financial institutions to create customized solutions that work with existing systems, avoiding costly “rip-and-replace” strategies. The integration of AI automations standardizes processes, reduces repetitive work, and ensures that investigators receive all the data and insights they need without leaving the system. For example, AI-generated summaries and analysis can significantly reduce manual workload while maintaining data consistency. This approach helps analysts focus on high-value tasks rather than mundane, repetitive ones.

Question: What methods and frameworks do you recommend for making generative AI outputs more explainable to business users?

When discussing methods and frameworks for making generative AI outputs more explainable, Theresa highlighted the importance of configurability and transparency within AI systems. This approach allows AI frameworks to provide non-technical users with the necessary tools to understand how decisions are made. By including audit logs, clearly showing data sources, and creating explanatory workflows, business users can trace AI outputs back to specific inputs and rationales. Furthermore, ensuring a configurable interface enables firms to tailor the system to their unique compliance processes and evolving business needs.

Phoebe added that explainability must target different audiences, distinguishing between external users (such as customers or applicants) and internal users (like risk officers). She suggested that maintaining observability throughout AI processes helps users monitor data flow, detect patterns, and ensure that outputs are justifiable. This can include frameworks such as NIST's AI Risk Management or using guardrails to continuously evaluate the performance and reliability of the AI model. By leveraging broadness assessments and manual reviews, organizations can ensure compliance with ethical AI guidelines while also providing transparency into how AI decisions are made, thereby reducing the sense of AI as a "black box."

Lucinity’s Demo: Enhancing Productivity with the Power of GenAI

In the Lucinity demo, Theresa Bercich highlighted several key features of the Luci AI platform, focusing on how it enhances the productivity of analysts in financial crime investigations:

1. Case Management: Luci aggregates cases and auto-generates summaries, providing key insights and highlights for investigators.
2. Configurable AI: Luci’s outputs, including negative news searches, are customizable based on each institution’s unique data.
3. Automation: Tasks like address checks, money flow analysis, and request-for-information (RFI) creation are automated, improving efficiency.
4. Cross-Platform Integration: Luci works across various systems, offering web-based plug-ins that streamline investigations by integrating with legacy systems.

Theresa also showcased how Luci generates real-time analytics and visualizations of financial transactions, allowing investigators to analyze money flows and make informed decisions without having to switch between platforms. Through Luci's interface, investigators can easily standardize workflows, ensuring compliance and audit trails, while also enabling the quick generation of reports and summaries.

Moreover, Theresa explained how Luci’s plug-in enables users to generate actionable insights, create graphs, transaction summaries, and even capture screenshots - all of which can be seamlessly added to case management systems. This integration is designed to streamline investigative workflows without the need for tedious manual processes like copy-pasting data between platforms.

Conclusion: Wrapping Up on a Light Note

Rogier wrapped up the conversation with a light-hearted question. He asked Theresa and Phoebe to share their favorite AI prompts that the audience could try at home.

Theresa revealed that she often asks AI to correct her written work. Specifically, she finds AI helpful for refining her German emails, especially for her son's kindergarten.

Phoebe uses AI to help sort her emails based on urgency and even to practice conversations with her teenage daughter, improving their communication.

Summing Up: Key Insights into Boosting Productivity in Financial Crime Operations

This insightful webinar brought together industry experts to address how financial crime operations can be made more productive using AI, with a particular focus on AML (Anti-Money Laundering) efforts. Led by Rogier Binsbergen from S&P Global, with expert insights from Theresa Bercich, CPO at Lucinity, and Phoebe Kao, Head of Programmatic Compliance at Microsoft, the session delved into how AI can address challenges such as siloed systems, manual processes, and fragmented data.

Key Takeaway Points From The Webinar:

1. Increasing Demand for Integrated AML Solutions: Gartner research indicates that 37% of businesses are actively seeking integrated, end-to-end AML platforms. Businesses are also prioritizing reducing total cost of ownership (TCO) and improving detection rates for higher accuracy.
2. Automation and Efficiency Gains: Lucinity's platform demonstrates how AI can automate repetitive tasks, reduce manual workloads, and increase overall productivity in financial crime operations. The automation reduces case handling times and operational costs, allowing analysts to focus on more critical, value-driven tasks.
3. AI and Job Satisfaction: AI can mitigate burnout among compliance officers by automating tedious, labor-intensive tasks, improving overall job satisfaction. The seamless workflow integration of AI tools like Luci AI can relieve compliance teams from mundane tasks and free them to focus on critical decision-making.
4. Data Fragmentation and Siloed Systems: The biggest barriers to productivity in compliance teams, highlighted by the audience polls, include fragmented data, manual processes, and a lack of actionable insights. Lucinity's platform addresses these pain points by providing a unified interface that integrates existing legacy systems, offering greater visibility and more efficient workflows.
5. Responsible Use of AI: Microsoft and Lucinity emphasize the importance of explainability, transparency, and responsible AI. Phoebe highlighted how Azure OpenAI incorporates safeguards to ensure AI-generated outcomes remain secure and ethical, aligning with global regulatory standards like the EU AI Act.
6. Best Practices for AI Integration: Successful integration of AI into financial crime operations requires tools that fit within an organization’s unique systems and regulatory requirements. Customizable platforms like Luci AI allow for seamless interaction across legacy systems without the need for extensive restructuring.
7. Future Trends: Both Theresa and Phoebe predict rapid advancements in AI technologies, transforming compliance and risk management further. They envision a collaborative AI-human ecosystem where AI handles routine tasks, and humans drive strategic decision-making.

The webinar emphasized that AI is not just about automating tasks but empowering analysts to work smarter - improving efficiency across financial crime operations. With platforms like Luci AI, the future holds immense potential for even greater productivity and accuracy in compliance functions.

For a deeper dive into these insights, watch the full webinar recording here: Webinar Link.

Meet The Speakers

Theresa Bercich: As Lucinity's Chief Product Officer, Theresa highlighted how AI-driven tools such as Luci improve the productivity of compliance officers by automating repetitive tasks, reducing errors, and enhancing workflow efficiency. She stressed that Luci integrates seamlessly into existing legacy systems without needing replacements, making it a flexible and cost-effective solution for financial institutions.

Phoebe Kao: With over 20 years of experience in risk management, Phoebe shared Microsoft's perspective on AI in compliance. She highlighted the Azure OpenAI collaboration and explained how AI transforms document processing, vendor onboarding (e.g., KYC), and customer analysis. Importantly, Phoebe highlighted that AI can significantly reduce burnout among compliance professionals by automating laborious tasks, leading to improved job satisfaction.

Rogier Binsbergen, Director at S&P Global, guided the session with thoughtful questions and seamless transitions. He kept the conversation focused on productivity challenges in financial crime compliance while engaging the audience through live polls. Rogier's expertise in risk assessment allowed him to highlight critical areas for AI and compliance improvements.