NHF Reduces False Positives in AML Screening by 15%

NHF Reduces False Positives in AML Screening by 15%

Executive Summary

New Horizons Financial (NHF), a growing RIA firm managing over $5 billion in assets, struggled with a high volume of false positives generated by their AML screening system. These false positives consumed significant time and resources, diverting compliance staff from focusing on genuine threats. By strategically refining the parameters and thresholds within their existing NICE Actimize platform, NHF successfully reduced false positives by 15%, significantly improving efficiency and freeing up valuable compliance resources.

The Challenge

New Horizons Financial (NHF) experienced a rapid increase in client onboarding and transaction volume over the past two years, leading to a corresponding surge in alerts generated by their anti-money laundering (AML) screening system. While essential for regulatory compliance, a disproportionate number of these alerts proved to be false positives, requiring manual review and investigation by the compliance team.

Specifically, NHF's AML system, NICE Actimize, flagged an average of 450 transactions per month as potentially suspicious. After a thorough review, the compliance team determined that approximately 360 of these alerts were false positives – instances where legitimate transactions triggered alerts based on pre-set parameters. These false positives represented a significant drain on resources. Each false positive required an average of 45 minutes of investigation, including reviewing transaction details, contacting clients for clarification, and documenting the findings.

This translates to approximately 270 hours per month spent investigating false positives. Assuming an average hourly cost of $75 for a compliance officer, the annual cost associated with investigating these false positives amounted to over $243,000. This figure doesn't even account for the opportunity cost – the time the compliance team could have spent on higher-value activities such as enhancing AML policies, conducting training, and investigating genuinely suspicious activity.

Furthermore, the high volume of false positives created unnecessary friction for clients. In some cases, clients were contacted multiple times regarding seemingly innocuous transactions, leading to frustration and potentially impacting client satisfaction. For instance, a client transferring $15,000 from their brokerage account to their personal checking account to cover a home renovation project was flagged due to the size of the transaction. While within regulatory guidelines, the repeated contact and investigation surrounding such transactions strained the client relationship. This also created operational inefficiencies, as front-office staff spent time addressing client concerns related to these AML inquiries, further diverting resources.

The challenge for NHF was clear: reduce the volume of false positives generated by their AML screening system without compromising the effectiveness of their overall AML program and maintaining adherence to stringent regulatory requirements. The firm needed to strike a delicate balance between minimizing disruptions and ensuring robust protection against financial crime.

The Approach

NHF recognized that a fundamental shift was required to address the high rate of false positives. Instead of blindly accepting the default parameters of their NICE Actimize system, they adopted a data-driven approach focused on understanding the specific nuances of their client base and transaction patterns. The approach involved the following key steps:

1. Data Analysis & Segmentation: NHF began by conducting a comprehensive analysis of their transaction data, spanning the previous 12 months. This analysis involved identifying the key drivers of false positives, such as transaction size, geographical location, and counterparties. They segmented their client base based on various factors, including income level, investment strategy, and transaction history. This segmentation allowed them to identify specific client groups that were disproportionately affected by false positives. For example, high-net-worth individuals with frequent international transactions were often flagged due to the inherent complexity of their financial activities.

2. Parameter Tuning & Threshold Adjustment: Based on the data analysis, NHF identified specific parameters within NICE Actimize that were contributing to the high rate of false positives. These parameters included transaction size thresholds, geographic risk scores, and keyword filters. They carefully adjusted these parameters, taking into account the characteristics of their client base and the regulatory requirements. For example, they increased the transaction size threshold for certain low-risk clients who frequently engaged in large transactions. They also refined the geographic risk scores to better reflect the actual risk associated with specific countries and regions. This involved cross-referencing the system's default scores with up-to-date data from reputable sources, such as the Financial Action Task Force (FATF) and the Office of Foreign Assets Control (OFAC).

3. Scenario Refinement: NHF reviewed and refined the scenarios used by NICE Actimize to detect suspicious activity. They identified scenarios that were overly broad or triggered by common, legitimate transactions. For instance, a scenario that flagged all transactions involving cash deposits over $5,000 generated a significant number of false positives because many clients regularly deposited cash for legitimate purposes. NHF refined this scenario to focus on transactions involving cash deposits over $10,000 combined with other suspicious indicators, such as unusual transaction patterns or connections to high-risk jurisdictions.

4. Testing & Validation: After making the necessary adjustments, NHF conducted rigorous testing to ensure that the changes did not inadvertently increase the risk of missing genuine suspicious activity. They used historical transaction data to simulate the impact of the new parameters and scenarios. They also conducted regular audits to monitor the effectiveness of the changes and identify any potential gaps in their AML program. This involved comparing the number of alerts generated before and after the changes, as well as reviewing a sample of transactions that were not flagged to ensure that they were not overlooking any genuine risks.

5. Continuous Monitoring & Improvement: NHF recognized that AML compliance is an ongoing process. They established a system for continuously monitoring the performance of their AML screening system and making further adjustments as needed. This involved regularly reviewing the number of alerts generated, the percentage of alerts that are false positives, and the time it takes to investigate suspicious activity. They also stayed abreast of changes in regulatory requirements and industry best practices.

Technical Implementation

The technical implementation involved directly modifying the configuration of the NICE Actimize platform. Access control was crucial, with changes restricted to designated compliance officers with appropriate permissions. Here's a breakdown of the key adjustments:

• Parameter Adjustment: The team adjusted the transaction velocity parameter. Previously, any client moving more than $50,000 within a 7-day period automatically triggered an alert. Analyzing historical data revealed that many high-net-worth clients routinely transfer sums exceeding this amount for legitimate investment purposes. The threshold was raised to $75,000 for clients classified as low-risk based on their KYC profile and transaction history. The adjustment was made via the "Parameter Configuration" module within NICE Actimize.

• Geographic Risk Scoring: NICE Actimize assigns risk scores to different countries based on factors like corruption, terrorism financing, and money laundering risks. NHF’s team compared Actimize's scores to the FATF and OFAC lists, updating the scores based on the latest information. For example, a specific jurisdiction had been assigned a default score of "High Risk". Based on updated reports indicating improved regulatory oversight, NHF downgraded this to "Medium Risk" in the "Geographic Risk Matrix" within Actimize. This was implemented after verifying with external sources and documented appropriately.

• Keyword Filtering: The team also refined keyword filters used to identify potentially suspicious transactions. The original keyword list included terms like "offshore," which triggered alerts for legitimate international investments. They added contextual filters to the "offshore" keyword. To be flagged now, it also had to include "anonymous" or "shell company." Keyword refinements were configured in the "Scenario Design" module.

• Scenario Logic Modification: The original scenario logic identified any transactions involving cryptocurrency as high-risk. After further research and client analysis, they determined a threshold of $5,000 as a risk trigger, modifying the scenario to only flag transactions exceeding this amount. This change was carefully implemented using the "Visual Modeler" tool within Actimize to prevent errors. The underlying code was also reviewed by a senior compliance analyst to ensure accuracy.

All configuration changes were tracked using Actimize's built-in audit trail functionality, ensuring compliance with regulatory requirements. Backups of the system configuration were performed before and after each modification to facilitate rollback in case of errors.

Results & ROI

The refined AML screening parameters implemented by NHF yielded significant positive results, demonstrably improving efficiency and reducing operational costs.

• False Positive Reduction: The most significant outcome was a 15% reduction in false positives, decreasing from an average of 360 per month to approximately 306. This reduction translated to a substantial decrease in manual review workload.

• Time Savings: With 54 fewer false positives to investigate each month, the compliance team saved an estimated 40.5 hours (54 false positives * 45 minutes/false positive). This freed up valuable time for the team to focus on more strategic activities.

• Cost Savings: The 40.5 hours saved per month translated to an estimated cost savings of $36,450 per year (40.5 hours/month * $75/hour * 12 months). This represents a significant return on investment for the time and resources invested in refining the AML screening parameters.

• Improved Efficiency: The reduction in false positives allowed the compliance team to focus on genuine suspicious activity, leading to more efficient investigations and improved detection rates. The time spent investigating true positives increased by approximately 10%, allowing for more thorough investigations and a greater likelihood of identifying and reporting suspicious activity.

• Enhanced Client Satisfaction: The reduced number of unnecessary inquiries to clients resulted in improved client satisfaction and a more streamlined onboarding process. The number of client complaints related to AML inquiries decreased by 20% in the three months following the implementation of the changes.

• ROI Calculation: In summary, the firm saw a direct cost savings of $36,450 annually. Factoring in the increased efficiency of the team and more client satisfaction, the total benefit to the firm equaled $54,675 per year. This more than offset the time spent refining the rules.

Key Takeaways

The experience of New Horizons Financial offers valuable lessons for other RIAs seeking to optimize their AML screening processes:

1. Data-Driven Approach: Don't blindly accept default parameters. Conduct a thorough analysis of your transaction data to understand the specific drivers of false positives within your firm.
2. Segment Your Client Base: Recognize that different client segments may require different AML screening parameters. Tailor your approach to reflect the unique characteristics of each segment.
3. Continuous Monitoring & Improvement: AML compliance is not a one-time task. Establish a system for continuously monitoring the performance of your AML screening system and making adjustments as needed. Stay abreast of regulatory changes and industry best practices.
4. Collaboration is Key: Effective AML compliance requires collaboration between compliance, technology, and front-office teams. Ensure that all stakeholders are involved in the process and understand their roles and responsibilities.
5. Use Available Tools": You likely already have a powerful tool, such as NICE Actimize. Take the time to refine it.

About Golden Door Asset

Golden Door Asset builds AI-powered intelligence tools for RIAs. Our platform helps advisors reduce compliance burdens and optimize their processes. Visit our tools to see how we can help your practice.