The usual escalation between fraudsters and investigators started to break down when it came to digital documents: forgeries on digital account statements and employment records were becoming forensic and invisible to the human eye.
As a result, first-line risk teams were struggling to assess the authenticity of documents, and escalated cases were taking longer for financial crime investigators to resolve. To confirm the authenticity of documents, the teams were increasingly relying on outreach to the issuing institutions, which slowed down decisions — if they got a response at all.
This new situation threatened to bottleneck growth in an otherwise clockwork operation that prides itself on consistent iteration and execution.
Resistant AI was integrated as part of the application process and workflow triggers were tied to the various verdicts provided to each document.
Applications with “Warning” or “High Risk” documents triggered escalations to the Financial Fraud Investigation team.
If the forensic analysis demonstrated clear attempts at fraudulent manipulation, the application could be declined immediately. The team would then be freed up to log the results and analyze them for fraud trends to build their risk intelligence.
In less clear-cut cases where deeper investigations were needed, time spent on each case was shaved by 52 minutes.
Meanwhile, confidence in Resistant AI’s authenticity verdicts meant applications with documents that met that standard could automatically move on to underwriting stages. In these cases, document assessment was reduced to mere seconds.
Since it only takes 50-80 authentic samples to create new “Trusted” models of authenticity, Habito’s document type coverage was increased daily in production from the incoming stream of documents.