FNBO cuts fraud investigation time 35–40% and boosts analyst productivity 42% with Pindrop Fraud Assist

Community and regional banks like FNBO face escalating fraud volumes with lean investigation teams and limited flexibility to scale headcount. FNBO's fraud investigators — a team of just four to five analysts — were spending 30 to 35 minutes per case manually listening through recorded calls to reconstruct what happened. With a multilingual customer base spanning nine states, non-bilingual analysts faced additional friction on non-English calls, slowing reviews further. Case decisions also varied by analyst experience, producing inconsistent verdicts across the team. Rising caseloads with no path to add staff made the status quo untenable: slower investigations, uneven outcomes, and growing fraud exposure across a $35 billion asset institution.

FNBO piloted Pindrop Fraud Assist — a GenAI-powered case intelligence tool built as a native add-on to Pindrop® Protect, the fraud prevention platform already deployed in FNBO's contact center. Rather than introducing a separate vendor or parallel workflow, Fraud Assist embedded directly into analysts' existing case review interface. The tool applies large language models and generative AI to deliver real-time call summarization, converting 30-minute manual call reviews into seconds of reading. For non-English calls, automated multilingual-to-English translation removes language barriers entirely. Beyond call content, Fraud Assist surfaces enriched risk signals — device risk, location risk, and in-case voice matching against known fraudster profiles — giving analysts a consolidated intelligence layer without leaving their existing tooling. Deployment ran as a contained five-month pilot from August through December 2025.

Within the five-month pilot, FNBO's small fraud investigation team achieved measurable gains across every tracked dimension:

• 35–40% faster investigations: Average case time dropped by 11–13 minutes, compressing what had been 30–35-minute manual reviews
• 50% improvement in analyst accuracy: Both false positives and false negatives declined, tightening decision consistency across the team
• 42% increase in case productivity: The same four-to-five-person team handled significantly more cases without adding headcount

All productivity gains came through efficiency alone — no additional staff, no new vendor relationships, and minimal training overhead due to the tool's native integration into existing workflows.

• Embedding AI natively into existing fraud tooling — rather than standing up a separate platform — minimizes adoption friction and is particularly effective for small analyst teams with limited onboarding capacity.
• Automated call summarization is the highest-leverage intervention in phone fraud investigation; eliminating manual call replay accounts for the majority of per-case time savings.
• Multilingual AI translation unlocks full team productivity on non-English cases without requiring specialized staffing.
• Richer contextual signals (device risk, location risk, voice matching) improve decision consistency across analysts of varying experience, reducing outcome variance on ambiguous cases.
• A five-month contained pilot delivers sufficient ROI signal for lean teams before committing to full rollout.