Large U.S. Bank Saves Six-Figure Sum Solving for Unknown Sessions by Adding Arkose Labs to Security Stack

Find out how a leading financial institution slashed fraud detection costs and eliminated bot-driven account takeovers by stopping unknown sessions and restoring customer trust.

What You’ll Learn

• How unknown (empty) sessions became a major operational and financial burden Why blank or unprofiled sessions created visibility gaps, interfered with decisioning, and drove unnecessary downstream fraud-detection costs.
• How automated ATO attacks persisted despite a layered security stack How bots continued slipping past CDN, IAM, and identity-verification controls—compromising customer accounts and eroding trust.
• How Arkose Bot Manager added missing intelligence between existing systems How deploying Arkose between Akamai and ThreatMetrix delivered the behavioral, device, and anomaly-detection insights required to classify traffic accurately and stop advanced bots.
• How eliminating unknown sessions improved visibility and reduced noise How accurate session attribution freed risk teams from sifting through meaningless data, enabling precise decisions and stronger, faster threat response.
• How a defense-in-depth approach restored customer trust and reduced fraud costs How Arkose Bot Manager complemented the bank’s existing stack to strengthen ATO prevention and deliver meaningful operational savings.

FAQ

Why were unknown sessions such a significant problem for the bank?

Unknown or “empty” sessions occur when device profiling fails, leaving no usable signal to classify risk. The bank’s device vendor could not interpret these sessions, forcing the bank to pay for analysis that yielded no actionable insight. This added cost, cluttered fraud-detection workflows, and increased exposure to automated attacks hiding in the noise.

How did Arkose Labs reduce both unknown sessions and ATO attacks?

Arkose Bot Manager applied advanced behavioral analysis, device fingerprinting, and progressive challenge flows to distinguish legitimate users from bots and human fraud farms. Positioned between Akamai and ThreatMetrix, Arkose filled critical detection gaps and provided clean, high-fidelity traffic signals—allowing downstream tools to make accurate decisions.

What business impact did the bank achieve by adding Arkose Labs?

The bank dramatically reduced unknown session volume, restored trust by eliminating bot-driven account takeovers, and cut costly downstream fraud-detection workloads. With noise removed, analysts could focus on real threats instead of empty data—delivering substantial savings and a stronger, more reliable customer experience.