Name Alteration is the deliberate manipulation of an individual’s personal name—through legal name changes, aliases, slight misspellings, transliteration shifts, or nom de guerre—to sever or weaken an information trail that would otherwise expose sanctions hits, negative media, or prior suspicious-activity reports. Unlike wholesale identity fraud (which fabricates an entirely new persona), Name Alteration preserves most biographic markers—date of birth, nationality, biometric likeness—while subverting controls that rely heavily on exact‐string or near-string matching.
Criminal actors exploit several systemic blind spots. First, many screening engines prioritize orthographic exactness: a single dropped vowel, an inserted diacritic, or the reversal of given and family names can defeat automated filters [6]. Second, civil-registry procedures in some jurisdictions permit rapid, low-cost legal name changes; once new passports or driver’s licences are issued, downstream financial institutions accept them as authoritative [8]. Third, global inconsistencies in romanizing non-Latin scripts—e.g., Arabic, Cyrillic, Chinese—generate multiple legitimate renderings of the same name, giving launderers plausible deniability when one spelling is black-listed but another is not [10].
Operationally, Name Alteration frequently appears at the access-facilitation stage of money-laundering workflows. By onboarding under a subtly altered spelling, a politically exposed person or previously de-banked fraudster obtains fresh customer records and account numbers unlinked to historical alerts. Once an alias account is live, subsequent placement and layering transactions enter the financial system with a reduced baseline risk score, making downstream detection markedly harder.
Cultural practices reinforce the technique’s potency. Illicit actors may adopt Arabic kunyas (“Abu X”) [14] that obscure birth names in remittance instructions. In Spanish-speaking contexts, fluid ordering or omission of maternal surnames can create legitimate-looking variants of the same individual. In East Asian contexts, the same Chinese characters may be romanized as Li Wei, Lee Way, or Li Wai depending on the transliteration standard employed. Unless screening systems normalize such variants, each representation can masquerade as a distinct, low-risk customer.
The technique’s strategic value is amplified by the growth of remote, automated onboarding and unsupervised e-KYC pipelines. Such workflows are vulnerable when tuned to exact-match confidence thresholds which can be gamed by minimal spelling changes that escape human review.