Alternative data new weapon against credit fraud

Companies training models on various forms of alternative data to enhance underwriting for new-to-credit customers

- AJINKYA KAWALE

Risk assessment platforms are increasingly using diverse data sources to help lenders identify groups attempting to access credit from regulated entities and to improve the process of evaluating new-to-credit (NTC) users.

Companies are training models on various forms of alternative datasuch as location details, third-party app usage, SMS data, payment transaction behaviour, and metadata-to enhance underwriting for NTC customers.

Fraud syndicate detection often stems from analysing alternative data patterns, such as multiple government IDs linked to a single mobile number, particularly in high-risk regions with a history of fraudulent activity.

"What we look for are aggregated insights. For example, whether a user is linked to five phone numbers, three of which are connected to four other permanent account number (PAN) cards.