Financial institutions are rethinking how they measure identity verification (IDV) effectiveness. Traditional KPIs—chargeback rates, fraud losses, false positives—remain staples, but a new survey by Regula reveals a strategic pivot toward predictive risk analytics.

The survey, spanning the USA, UAE, Germany, and Singapore, highlights growing dissatisfaction with lagging indicators. Companies now prioritize real-time detection capabilities over post-fraud metrics. 'You can’t manage what you don’t measure early enough,' notes one respondent from a Singaporean bank.

Key metrics gaining traction include employee training effectiveness and system adaptability scores—signaling a focus on human-machine collaboration in fraud prevention. This aligns with crypto exchanges like Binance and Coinbase tightening KYC protocols amid regulatory scrutiny.

Notably, decentralized identity projects (e.g., Ethereum’s ENS, Polygon’s ID solutions) are being tested to reduce false negatives without compromising UX—a balance reflected in tracking both 'cost of fraud' and 'customer impact' metrics.