The company says its Credit Washing and Synthetic Fraud Scores, announced Tuesday (Oct. 20), are the first-ever “adverse-actionable” solutions of their kind in the market, meaning lenders can make credit decisions and give consumers “applicable adverse-action reason codes explaining why they were denied credit,” capabilities previously unavailable.
“Synthetic identity fraud and credit washing present unique challenges because they can conceal risk behind seemingly legitimate consumer profiles,” James Garvert, executive vice president of global fraud and communications solutions at TransUnion, said in a news release.
“By bringing advanced fraud intelligence into explainable, FCRA-governed decisioning, we’re helping organizations uncover threats that might otherwise go undetected, make more confident decisions, and mitigate potential financial losses.”
Synthetic identity fraud occurs when a real person’s information is stolen and merged with falsified personal information to establish a new identity.
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Credit washing is the removal of legitimate, negative information from a person’s credit history, often through fraudulent credit disputes.
Together, they are among the fastest growing and most costly types of financial crime, TransUnion said, with company research placing total lender exposure to synthetic identity fraud at $2.4 billion as of June of this year.
“By combining real and fabricated identity elements to create seemingly legitimate consumer profiles, fraudsters can evade traditional detection methods, establish accounts and generate substantial losses,” the company said.
TransUnion says its new scores address these issues using the company’s data assets, advanced analytics and identity intelligence to help organizations identify these threats earlier, bolster decision-making and lessen potential financial losses before they appear.
PYMNTS Intelligence explored the challenges of identity verification amid the rise of artificial intelligence (AI) earlier this year in the report “Why Identity Silos Are Failing in the AI Era.”
“The first generation of digital identity was document-centric and event-based,” the report said. “Verify a person, open the account, authenticate at login, maybe challenge at checkout.”
The second generation involved additional signals, increased automation and more risk scoring, though still dependent heavily on static ideas of identity and human distinction.
“The third generation, now coming into view, is different,” the report added. “It assumes that any individual signal can be spoofed, that humans and machines will increasingly blur in digital channels, that agentic transactions will become the norm and that organizations must continuously score trust rather than episodically.”
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