Chapter 7: Test The AML Vendors

Evaluating AML Vendors For Best AML Features

This chapter will elaborate on how to evaluate the quick response, accuracy, and credibility of a screening solution vendor. Several critical factors must be considered, including data quality, global coverage, real-time update frequency, algorithm effectiveness, and integration capability. 

You can carefully evaluate every important aspect of the solution, from its ability to handle data and integrate it with your current infrastructure and user interface.

Vendors Due Diligence: AML Screening Accuracy and Reliability

Global Data Sources and Coverage

The Financial Action Task Force (FATF) emphasizes the importance of global data coverage in its recommendations for effective AML measures.

Your AML vendor’s solution must tap into a wide range of databases, including global sanctions lists (e.g., OFAC, UN, EU), Politically Exposed Person data, international leaks, and adverse media sources. Comprehensive data coverage ensures that the solution can identify high-risk individuals and entities across various jurisdictions.

Your AML vendor must provide advanced, well-structured, and high-quality data that facilitates seamless integration and searches. This data must be error-free and arranged for practical usage.

Frequent Updates For Accurate Data

The frequency of data updates is vital. Your AML vendor must update its databases in real-time or near real-time to ensure that newly sanctioned individuals or entities are quickly flagged. This reduces the risk of processing profiles involving sanctioned parties.

A study highlights that institutions with frequent data updates are better positioned to prevent financial crimes and stay compliant with evolving regulations.

Does Your AML Solution Find An Exact Match?

With all these aspects, the most important of all is your AML vendor returning exact matches for all the sanctioned entities, PEP names, and adverse media entries.

Ensuring that your AML vendor returns exact matches for each search from all sanctioned entities, PEP names, and adverse media entries is critical for maintaining effective AML compliance.

Precise matching is essential to streamline the AML compliance process, reduce false positives, minimize false negatives, show less confused matches, and enhance the institution’s ability to respond to genuine risks effectively.

Create Test Scenarios

Not every match is an exact match, learn to differentiate between partial match, potential match, confused match, and exact match. Generate test cases with customer names and other details likely similar to those on sanctions and watchlists, varying in specific aspects to represent different match types.

Get AML Solution That Helps You Navigate Through All The Name-Matching Scenarios To An Exact Match

Why Does Any Solution Need To Target An Exact Match?

AML compliance solutions often show potential, confusing, or partial matches due to data variability, incomplete information, algorithm sensitivity, and the use of aliases. Differences in spelling, transliteration, or formatting can cause systems to flag names or entities as non-exact matches.

A potential match requires a further review of additional information, such as date of birth, and location to give an exact match.

Despite these complexities, your AML vendor must offer exact matches to minimize false positives, allowing compliance teams to focus on genuine threats, and improving operational efficiency and resource management.

Accurate matches also ensure that financial institutions comply with regulatory requirements, avoiding penalties and maintaining their legal standing. Lastly, exact matching improves customer experience by preventing legitimate clients from being wrongly flagged, ensuring smooth onboarding and transaction processes.

Why Your AML Vendor Must Offer A Solution That Reduces The Maximum Number Of False Positives And Negatives?

Your AML vendor must offer an efficient solution that reduces the false positives and negatives to the minimum.

• Reducing false positives is where legitimate profiles are not incorrectly flagged as suspicious, prevents unnecessary investigations, and conserves resources, allowing compliance teams to focus on genuine threats.
• Reducing False negatives, on the other hand, is where high-risk entities or individuals are detected with exact matches through extensive screening data coverage and perfect algorithm functioning to retrieve that data. It is to be noted that often despite high-risk entity name presence in the database, the name is not retrieved due to a faulty algorithm leading to a false negative.

So, efficiently managing these errors is vital to ensure accurate risk assessments and maintain the integrity of financial institutions.

Get Extensive Database Coverage Supported By Functional Algorithms

AML Watcher’s Unique Customer Feedback Integration To Reduce False Positives

AML Watcher has developed a unique strategy to control and mitigate biases in the outcomes obtained from their proprietary database. And that is what truly sets AML Watcher apart from its competitors. Our AML compliance solution uses customer feedback integration to get exact outcomes.

Our customer feedback integration process has two crucial steps:

1. Evaluating and Comparing the efficiency of screening databases on key metrics False Negatives, False Positive, etc.
2. Analyzing reasons behind poor outcomes of these matrices and then actively working on their improvement.

In light of the customer feedback, we have highlighted three major issues: filtering issues, language issues, and research algorithm issues, which can result in false negatives and positives.

AML Watcher’s configurable fuzzy matching detects near and exact matches, filtering results by date of birth, location, and other unique attributes within a language or across various languages to lower false positives and negatives.

While the overall rate of false positives and false negatives, AML Watcher is significantly lower than contemporary AML vendors, this has not stopped us from further analyzing the reasons behind specific hits that didn’t return any matches or returned false matches, to ensure the accuracy of research retrievals.

Our recent POC comparisons have made this clear. Potential clients who compared their current AML vendors against AML Watcher based on key metrics saw a clear difference.

Based on the actual customer feedback, we have extracted some figures for exact matches, confused matches, false negatives, and false positives as compared to contemporary vendors.

• AML Watcher cuts False Positives by 44%
• Reduces False Negatives by 15%
• Cuts Confused Matches by 11%
• Boosts Exact Matches by 11%

Evaluate the Competency of Name Matching Algorithm

Assess your AML vendor’s ability to correctly screen, measure associated threats, and flag entities as per their distinct risk level. This involves adjusting the customized query parameters to see how the algorithm recognizes known high-risk entities and prevents false positives with low-risk or compliant entities.

Thus, this process should comprise assessing the system’s capacity to screen searches with the same names, aliases, name capitalization differences, hyphens, diverse nomenclatures, and diverse languages such as Arabic scripts or Japanese names.

At AML Watcher, our specially designed proprietary name-matching algorithm operates on the principle of Nothing Gets Lost in Translation to reduce false positives and false negatives. As opposed to the other matching algorithms it aggregates, on a deeper level of text analysis, the inputs analyze the text as sequences of characters rather than trying to match the whole input of the query.

This approach reflects the drawbacks of contemporary systems, thus ensuring that our fuzzy logic technique is efficient in more than 80 languages.

AML Watcher Offers Accurate Results On Real-World Compliance Data With Its  Proprietary Matching Algorithm

Our proprietary search algorithm evaluates text at a granular level. It is designed to present higher appropriate outcomes by effectively distinguishing relevant information from irrelevant data while minimizing false positives and negatives.

Bespoke Configuration and Adaptability

Test the solution’s adaptability and customization capabilities as per your specific AML requirements and risk parameters. Evaluate your AML vendor as your business operates across multiple jurisdictions, you must adjust risk thresholds to meet varying regulatory requirements and risk levels in each region.

Different business operations might have different risk profiles, so the ability to customize at a granular level is essential.

Last but not least, assess its scalability by progressively increasing screening data volumes and user counts to ensure the system maintains performance under growing demands with maximum accuracy and speed. This comprehensive check guarantees the solution can evolve alongside your business growth, maintaining efficiency and compliance.