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False Positives: Definition, Causes & Solutions

In various screening methods, the challenge of “False Positives” arises – situations where a match of criteria is mistakenly identified. These undesirable situations are not only frustrating but also cause significant additional effort. False Positives lead to delays in meeting obligations and identifying potential risks. This raises the question: Why are they so persistent, and how can we effectively address this challenge?

What are False Positives?

In principle, a “False Positive” occurs when, during a review or test, a match of criteria is mistakenly identified that is not actually present. In contrast, “False Negatives” are situations where an actual match is not detected or erroneously negated in test results. False Negatives not only entail additional workload, as is the case with False Positives, but could also have significant consequences. This is especially true if a problematic business contact goes unnoticed.

To illustrate, consider the following example: A screening tool is employed to assess business relationships for risks associated with politically exposed persons (PEPs). A “False Positive” could occur in this scenario if the screening process erroneously identifies a business relationship with a PEP when there is no actual connection. This could lead to unnecessary reviews and resource wastage, especially if the falsely identified individual has no involvement in any risky activities.

Causes of False Positives in Risk Screening

Regardless of the type of risk screening, the possibility of False Positives exists. While the adjustment of matching engines is flexible, they often begin with a broad approach to capture potential risks early on. A too restrictive setting could lead to overlooking suspicious business relationships or transactions. The use of a comprehensive filter ensures that a wide range of potential risk factors is considered, simultaneously increasing the challenge of False Positives.

The more names or data points that need to be matched, the higher the number of results and, consequently, the likelihood of False Positives. Additionally, data quality significantly influences the screening process. The following sources of errors can lead to missed alerts or false hits:

  • Incorrect spellings, typos, and fonts from around the world
  • Missing birth dates
  • Missing addresses

Verifying these results often requires manual work, which is a time-consuming task and places a burden on organizational resources. Various factors contribute to complicating the identification of False Positives. Unclear data sources, a lack of standardization of information, as well as technical challenges in data integration and validation, can lead to complications. The extent to which these factors hinder the identification of False Positives depends heavily on individual circumstances.

Consequences and Approaches to Solutions

The consequence is that only a small fraction of potential customers, clients, or transactions is monitored with the level of detail, frequency, and precision required for increasingly thorough compliance checks today. Nevertheless, this is entirely achievable, especially with high data quality and optimal list matching.

The quality and reliability of data processed by screening software (e.g., partner screening) become crucial. Screening providers like Pythagoras Solutions rely on high-quality data providers to drastically reduce the incidence of False Positives in queries. The data providers connected to the screening software are customizable, offering both unstructured data (articles from licensed and reputable news sources, see Adverse Media) and structured data (including PEP lists, sanctions lists, Adverse Media, and other datasets).

Solutions for list matching focus on reducing False Positives in two main areas: the optimal configuration of solutions and the meaningful contextualization of the scanning process.

Efficient Compliance Checks: Pythagoras Partner Screening

Pythagoras Solutions’ Partner Screening offers advanced features to enhance precision and efficiency. A precise match is facilitated by comprehensive knowledge of various spellings and fonts. The integrable Native Character Screening further extends this functionality by recognizing non-Latin characters, allowing for global applicability that simultaneously meets local requirements.

The tool’s intelligent search technology enables an exact comparison of your partner data with defined reference data, delivering precise and consistent results irrespective of data volume, type, and quality. Additionally, the case management tool supports structured alert processing, allowing you to maintain control over all business relationships and document each step according to regulatory and compliance requirements.

This enables companies to conduct their compliance checks more efficiently and effectively, avoiding the expenditure of valuable resources on the manual verification of potential False Positives.


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