The Imperative Role of Data Quality in Watchlist Screening

Do you know the state of your organization’s data when it comes to watchlist screening? Are you confident that your data is robust enough to accurately identify potential risks and ensure compliance with regulations? If these questions raise doubts or uncertainties, then this article will pique your interest. At FinScan, a trusted leader in Anti-Money Laundering (AML) compliance solutions, we understand the importance of robust data quality practices to ensure accurate financial crime risk management and enhance operational efficiency. In this article, we shed light on why assessing and improving data quality should be a top priority for Chief Compliance Officers (CCO).

The risk of poor data quality in watchlist screening

The process of screening records against watchlists is a critical and meticulous process that relies heavily on a thorough comparison of data derived from two primary sources: customer lists and compliance lists. This essential comparison serves the purpose of identifying any potential matches between the individuals or entities in the client applications and those found on the watchlists. However, for the screening process to be truly effective, it is crucial for the data being compared to be complete, consistent, accurate and fit for the purpose of watchlist screening.

One pivotal issue that frequently arises is the occurrence of multiple names within the records (see Table 1). Common screening algorithms do not break out conjoint names within a record and do not screen names outside the name field leading to names within the records being overlooked and escaping examination. This usually happens when processing client applications that involve joint accounts or when additional names are concealed within other fields, such as the address field. It is vital that all names undergo comprehensive screening as failure to do so exposes the business to non-compliance risk and severe consequences.

Table 1

Example of records with additional names not being screened.

  Names not being screened

name date_of_birth address country
John Smith, Jane Johnson 1985-07-12 123 Main Street USA
Samantha Carter (aka Samantha Johnson) 1990-04-25 456 Elm Avenue Canada
Robert and/or Emily Jameson 1978-11-03 789 Oak Lane UK
Michael Anderson 1982-09-18 C/O William Johnson, 567 Pine Street Australia
Michelle Lee 1995-02-08 890 Maple Drive, DBA Brian Johnson, Inc Germany

Additionally, the screening algorithm’s ability to accurately detect individuals and entities on watchlists is further hindered by incomplete data, irregularities, and the presence of extraneous noise words (refer to Table 2). These factors together can lead to the generation of false negatives, where potential risks evade detection. To overcome these challenges, organizations must prioritize the profiling, cleansing and standardization of data. The accuracy of the screening process is improved when consistent data formats are applied, duplicate entries are eliminated, and extraneous noise words are expunged. This enables efficient risk detection and minimizes the likelihood of false positives and false negatives.

Table 2

Data quality challenges evident in customer lists.

  Noise word
  Date of birth in the wrong format
  Country name in the wrong format
  Dummy data
  Missing information

name date_of_birth address country
Estate of John Doe 1985-07-12 123 Main Street USA
Jane Johnson   456 Elm Ave United States
Esq. Robert Smith 03/11/1978 789 Oak Ln UK
Mrs. Sarah Anderson 1982-09-18 Pine St Australia
Michelle Lee N/A 1900-01-01 890 Maple Drive  

Many organizations tend to underestimate the importance of evaluating data quality when deploying a watchlist screening solution. A lack of understanding of the data makes the screening algorithm ineffective leading to compromised risk detection and potential compliance breaches. Recognizing the potential issues and resolving any existing data quality problem is important to ensure a robust and effective watchlist screening process.

Driving operational efficiency through data quality

Another benefit of prioritizing data quality in watchlist screening is the improved operational efficiency gained through a reduction in the number of false positive alerts. Encountering false positives is a commonplace phenomenon in watchlist screening, due to the inherent variability in the record matching process. However, an excessive number of false positives should serve as a red flag, prompting immediate action to address data quality issues. Reducing the time associated with investigating false positives will also enable your team to concentrate their efforts on higher-risk customers.

One of the main factors leading to the excess of unnecessary alerts is the existence of duplicate records in a client application or when consolidating data from multiple client applications (Table 3). Conventional watchlist screening solutions often screen each record individually, irrespective of duplicates, leading to staff members reviewing the same alerts repetitively. This redundancy not only creates frustration but also wastes precious resources.

Table 3

Duplicate records in client applications leading to unnecessary alerts.

source client_id name date_of_birth address country
Core Banking jD34kLm97x John Doe 1980/05/10 123 Main Street US
Core Banking x7D2nLj56k John Doe 1980/05/10 US
Core Banking j7mD5Lk6n2 John Doe 123 Main Street US
CRM Lx4jDk37m9 John Doe 10-May-1980 123 Main Street United States
CRM 9L56kDj27x John Doe 10-May-1980 United States
CRM 2L6kDx93jm John Doe 123 Main Street United States
Wealth App m98kD4j3Lx John Doe 10-05-1980 123 Main Street United States

In addition, high false positive rates can be traced back to data inconsistencies and missing information which may impact the results from screening rules. By fixing data quality challenges head-on and understanding the state of the data, it becomes possible to tighten the screening rules without compromising the ability to detect legitimate alerts.

By recognizing the impact of data quality on reducing false positives and streamlining the screening process, organizations can achieve higher operational efficiency, optimize resource allocation, and improve the overall performance of their watchlist screening.

The prevalence of data quality issues

The importance of data quality in watchlist screening is paramount and has implications for every business. For organizations with a substantial client base, the lack of proper data preparation for screening puts them at risk of non-compliance. A single overlooked sanctioned individual or entity could subject a business to severe penalties and harm its reputation.

At FinScan, we have assisted many organizations across a range of industries to prepare their data for watchlist screening. Throughout these projects, we have examined the occurrence of data quality issues (Table 4). While not all data quality issues carry the same degree of risk, those that pose a greater threat to the organization are particularly concerning.

Table 4

Frequency range of data quality issues identified during FinScan’s watchlist screening data quality assessments on client databases.

  Risk of missing true alerts
  Increase in false positives

Data quality issues Low range High range
Additional names in name field 0.12% 7.00%
Additional names in address field 0.99% 3.00%
Missing first name 3.00% 6.00%
Missing last name 1.00% 3.00%
Missing country 1.00% 48.00%
Missing date of birth 44.00% 59.00%
Duplicates 2.00% 80.00%
Non-standardized country value 0.10% 52.00%
Non-standardized date of birth value 1.00% 10.00%
Dummy data 3.00% 11.00%
Name contains noise words 2.00% 12.00%

To assist organizations in identifying potential data quality challenges, the following warning signs are provided as indicators that the data requires attention:

  • Previously missed true alerts: Instances where genuine alerts were not detected.
  • Additional names in records not being screened: Failure to screen all names individually, potentially leading to oversight and increased risk exposure.
  • Abnormally high or increasing false positive rate : An alarmingly high number of false positive alerts, indicating a need to refine screening processes and address data quality issues.
  • Duplicate alerts: Repeated alerts generated due to the presence of duplicate records, which can result in unnecessary workload and inefficiencies.
  • Non-standardized date of birth and country: Inconsistencies in the formatting or representation of date of birth and country information, which can impede accurate matching and screening.
  • Missing information: Missing data in name, date of birth, and country fields. Instances where critical data elements are incomplete or absent, compromising the effectiveness of watchlist screening.

Based on our experience, data quality issues permeate watchlist screening. Organizations wishing to enhance their watchlist screening performance and results will start with an assessment of their data. The risks tied to overlooking these challenges are notably high, underscoring the need for swift action. The encouraging aspect is that resolving data quality concerns is neither overly complicated nor financially burdensome. With FinScan’s expertise, we can rapidly assist in remedying these issues.

Next step to address data quality in watchlist screening

The first step in the process of tackling data quality issues is to undertake a thorough data quality assessment within your database. This critical diagnostic review will illuminate the scale of the problem and pinpoint specific data quality issues that impact your watchlist screening process.

Data quality assessment: Within a brief period, we can conduct a thorough profiling of your data and provide a detailed assessment report with an overview of the data quality challenges impacting your watchlist screening efforts. Equipped with this knowledge, you will gain invaluable insights into the extent of the problem, as well as practical and effective solutions to resolve and correct these issues efficiently.

When it comes to navigating the intricacies of AML compliance and data quality, partnering with an experienced and trusted advisor is paramount. At FinScan, we have over 40 years of expertise in AML compliance and more than 50 years of experience in data quality management. Our demonstrated track record of successfully assisting numerous organizations in achieving improved data quality for compliance serves as a testament to our expertise and proficiency.

About the author

Steve Marshall is the director of FinScan Advisory Services. He brings more than 40 years’ experience in the area of risk management, specializing in anti-money laundering (AML) compliance. Having served in a number of roles at US and global financial institutions, Steve honed his skills navigating the complex landscape of regulatory compliance in financial services. His reputation as a trusted advisor to organizations worldwide was further solidified in his subsequent role as a principal in the financial crimes enforcement group at a Big 4 firm, where he guided the successful implementations of AML programs within the banking and financial services sector.

At the helm of FinScan’s Advisory Services, Steve leverages his wealth of experience to assist organizations in establishing robust AML programs. Recognizing the vital role that data quality plays in driving effective watchlist screening, Steve emphasizes the critical importance of utilizing good data in conjunction with cutting-edge technology to drive AML program effectiveness.

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