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12 Data Quality Challenges
That Affect Name Screening

1. Spelling Variations and Typographical Errors

Misspellings, keyboard errors, OCR misreads from scanned documents, and inconsistent transliterations all result in legitimate entities being missed or incorrectly flagged. These are especially common in names transliterated from non-Latin scripts, such as Arabic, Cyrillic, or Chinese, where there’s no single “correct” spelling.

Example:

  • Rachid El-Haddad ↔ Rashid Al Haddad

  • Natalya ↔ Nataliya

2. Phonetic Similarity

Names that sound alike but are spelled differently can easily escape detection unless systems account for phonetic matching. This is common in multicultural regions or where names are adapted to different alphabets or local pronunciation.

Example:

  • Shawn ↔ Shaun

  • Tariq ↔ Tareek

White Structure

Individuals often operate under different names—nicknames, shortened forms, or aliases. Criminals may also deliberately adopt alternative identities to evade detection.

Example:

Alejandro López ↔ Alex Lopez

Mohammed Saeed ↔ Abu Faisal (known alias)

4. Name Order and Cultural Structure

Naming conventions vary significantly across cultures. In some regions, the family name comes first; in others, middle names carry legal importance; some cultures use patronymics rather than surnames at all. Screening systems that assume “First Last” structures often misinterpret valid matches.

 

Example:

  • Nguyen Thi Minh ↔ Minh Thi Nguyen

  • Ivan Sergeyevich Petrov ↔ Sergei Petrov

5. Compound, Split, or Truncated Names

Names with multiple parts may be entered inconsistently or split across fields. Hyphens, prefixes (e.g., de, bin, von), or suffixes (e.g., Jr., III) may be dropped, merged, or truncated—especially when data is migrated between systems.

Example:

  • Maria del Carmen Rivera ↔ Maria Rivera

  • Thompson & Whitaker LLP ↔ T&W LLP

6. Inconsistent Formats and Field Mapping

Personal details like dates of birth, addresses, and national IDs may be stored in varying formats depending on the source system or region. This inconsistency makes it difficult to correlate records across systems.

Example:

  • 03/07/1982 (March 7 in US) ↔ 07/03/1982 (7 March in UK)

  • 123B Elm Street, Apt. 5 ↔ Apt 5, 123B Elm St.

3. Nicknames, Aliases, and Alternative Identities
7. Multilingual and Multiscript Challenges

Names may appear in original script or transliterated form—or both. Variations caused by multiple transliteration standards (e.g., Pinyin, Wade-Giles) can fragment identity matching.

Example:

  • 张伟 ↔ Zhang Wei ↔ Chang Wei

  • يوسف عمر ↔ Yousef Omar

8. Use of Titles, Honorifics, and Religious Identifiers

Titles like Dr., Imam, Sheikh, Reverend, or Ph.D. can appear inconsistently and interfere with name parsing. Systems not configured to identify and normalize these can misclassify or miss records entirely.

Example:

  • Dr. Fatou Ndiaye ↔ Fatou Ndiaye

  • Sheikh Al-Rahman ↔ Al-Rahman

9. Incomplete or Outdated Records

Stale customer records—names not updated after marriage, change of citizenship, or legal renaming—can skew results. Screening systems depending on old data can either miss true risks or create noise from no-longer-relevant matches.

Example:

  • A customer now listed as Alicia Müller still appears in legacy systems as Alicia Lopez.

Gender-neutral names, gender-specific honorifics, and regionally unique naming practices introduce ambiguity. Some cultures do not use surnames at all, or change names after certain life events (e.g., religious conversion, marriage).

Example:

  • Chris (could be Christopher or Christine)

  • Fatima bint Khalid ↔ Fatima Khalid

10. Gender and Culture-Based Challenges
11. Common Name Ambiguity

Names like Mohammed Ali, John Smith, or Li Wei generate thousands of potential matches. Without strong secondary identifiers (DOB, ID number), it’s difficult to resolve these with precision.

 

Example:

  • Ali Khan generates 1,200+ potential hits in multiple jurisdictions.

12. Deliberate Obfuscation and Manipulation

Bad actors exploit known weaknesses in screening systems by altering spellings, using special characters, or substituting homoglyphs (characters from other alphabets that look similar).

Example:

  • Оmar Farouk (Cyrillic “О”) ↔ Omar Farouk (Latin “O”)

  • James Smith entered as J@m3s Sm1th

The result: missed matches, false positives, and redundant alerts that slow down investigations and increase operational burden. When screening inputs are unreliable, institutions face a constant trade-off between thoroughness and efficiency; often at the expense of risk accuracy.

In short, poor data quality is not just a technical inconvenience - it’s a systemic challenge that threatens the integrity of the entire screening process.

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