Valley Bank Financial Crime Compliance with AI

Customer Account Monitoring

At its core, financial crimes compliance involves monitoring customer accounts and transactions. Valley Bank has been diligently searching through customer accounts and transactions to uncover activities indicative of specified unlawful activities, such as tax evasion and narco-trafficking. 

Identifying such activities is crucial for financial institutions in their obligation to report suspicious activities to the federal government.

Historical Evolution

Over time, this process has undergone significant transformation. In the earlier days, compliance officers would sift through many wires to identify potentially suspicious transactions. 

They had to rely on their expertise and experience to spot anomalies manually. This was a time-consuming and labor-intensive process.

Introduction of AI and Models

The game-changer came with the introduction of AI and data-driven models. Valley Bank now employs models that make automated decisions based on predefined thresholds and standard deviations. These models act as an initial filter, reducing the volume of data to be scrutinized. 

AI, in turn, sits on top of these models, enhancing the accuracy and efficiency of the entire process. This has made the compliance process significantly easier, eliminating the need for manually combing through sheets of data.

Demystifying the Models

Understanding how these models work is crucial to comprehending the AI revolution in compliance with financial crimes. Our expert sheds light on the inner workings:

AI Models and Language Models

When asked if GPT or similar language models power the models, the response is clear: “No, the language models haven’t hit us yet.” While AI plays a significant role, it’s not driven by language model tools like Documents with GPT but by data-driven analytics and traditional AI algorithms.

Data-Driven Models

These AI models primarily rely on historical data to make predictions. They are trained on vast datasets containing information about transactions, customer profiles, and financial activities. The AI identifies patterns and anomalies, contributing to a more efficient and accurate monitoring process.

Human Input and Oversight

While AI is a powerful tool, human oversight remains essential. AI acts as a filter, significantly reducing false positives and highlighting potential issues. Compliance officers, guided by AI-driven insights, play a crucial role in making the final judgment. This approach enhances the quality of analysis while making the process more efficient.

Challenges and Noise Reduction

Despite the significant advancements, our expert acknowledges that AI in financial crime compliance is still a “blunt instrument.” The technology has its limitations. It operates based on predefined rules and thresholds. The AI relies on the historical dataset; if a new type of activity emerges, it might not be adept at identifying it initially. 

However, the model improves over time with continuous updates and learning. The key advantage is the substantial noise reduction. The AI narrows down the data to a more manageable subset for human examination, eliminating much of the irrelevant information.

Fine-Tuning for Specific Risk Tolerance

Fine-tuning is one of the most critical aspects of AI implementation in financial crimes compliance. The risk tolerance of each bank varies. What might be suspicious for one bank may be entirely legitimate for another. 

For instance, with its significant presence in the real estate and diamond industries, Valley Bank has a unique risk tolerance. This means that AI models need to be calibrated according to the specific risk tolerance of the institution.

Team Dynamics and Adaptation

Valley Bank boasts over 70 professionals dedicated to financial crime prevention. To make the AI transition work, the team is divided into five specialized groups. This segmentation helps in focusing on specific aspects of compliance more effectively.

Segmentation for Better Focus

Different teams are responsible for distinct aspects of compliance. For example, one team monitors high-risk customers, while another handles suspicious activity monitoring. This division ensures that teams become specialists in their designated areas.

Challenges in Multi-Team Setup

However, operating in this multi-team setup comes with its own set of challenges. Managing models across different teams requires stringent governance, validation, and validation processes. Changes in one model may impact another, necessitating thorough testing to maintain integrity.

The Crucial Role of Data and Training

The success of AI relies on the quality and quantity of data and robust training of models. Valley Bank employs a rigorous approach to these factors:

Annual Model Retraining

Models are retrained annually, with updates when significant changes occur. Events like the pandemic or the emergence of new financial instruments require the models to adapt to changing patterns.

Data Relevance

Ensuring that models only utilize data relevant to the time when an alert is generated is vital. Subsequent information may not be meaningful, as a customer’s risk profile may change over time.

Avoiding Overfitting

Overfitting, where a model is too finely tuned to historical data, can be problematic. Avoiding this requires balancing the model’s exposure to data, ensuring it remains relevant and adaptable.

The Multifaceted Role of Data in AI

When asked about the most valuable piece of data in financial crimes compliance, the response is clear: no single data point is more valuable than the others. The strength of AI lies in making connections within vast datasets, helping compliance officers see patterns and anomalies that might otherwise remain hidden. 

The AI model works as a creative assistant, pointing out potential issues that may elude a human observer.

AI: Making Compliance More Efficient and Creative

AI is not about replacing humans in compliance but making their roles more efficient and creative. By automating mundane tasks, AI enables compliance officers to focus on in-depth analysis and decision-making. Moreover, AI can work creatively by uncovering hidden patterns and irregularities within data.

The Journey Towards AI in Compliance

Valley Bank’s journey with AI in financial crimes compliance serves as an inspiring example. Their story offers several lessons:

Problem-Centric Approach

Begin with a specific problem to solve. Don’t see AI as a panacea but as a tool to tackle specific issues effectively.

Communication is Key

Regular and transparent communication with your team is vital. Highlight the successes and the wins to keep your team motivated.

Continuous Adaptation

Be prepared for experimentation and adaptation. AI implementation is not a one-size-fits-all approach. What works for one bank may not work for another. Flexibility is key.

Final Thoughts

As the financial landscape continues to evolve, the role of AI in financial crimes compliance remains vital. AI is a powerful assistant, but it doesn’t replace the human touch. A combination of human expertise and AI efficiency is the way forward.

Valley Bank’s journey exemplifies the ongoing transformation, challenges, and opportunities. AI isn’t just about automating processes; it’s about empowering teams to focus on what they do best: making informed decisions and detecting financial crimes more efficiently and creatively. 

The lessons from Valley Bank’s experience underscore the importance of problem-centric, adaptable, and human-AI collaborative approaches to compliance.

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