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Trends in Defense and Detection Tech – Upgrading Your Stack

Written by
Maranda Manning
Published on
November 26, 2025
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TLDR

AI-driven fraud is escalating, with synthetic IDs, deepfakes, and large-scale impersonation pushing financial platforms to evolve their defenses. Leading tools today include behavioral biometrics, real-time anomaly scoring, explainable AI, and liveness with deepfake detection. The most resilient platforms are upgrading their stacks with layered, adaptive security that pairs advanced detection with persistent authentication.

The escalation: AI makes fraud faster, cheaper, and more convincing

Fraud rings are using off the shelf AI to scale phishing, generate synthetic identities, and clone voices. A high profile case in Hong Kong showed how a spoofed video meeting tricked an employee into paying out $25M, a preview of large scale impersonation in production.

US regulators have already flagged these risks. FinCEN warns that generative models are used to create falsified documents, photos, and live video that bypass KYC checks. Synthetic identity exposure hit an all time high in 2024, with the Federal Reserve linking this rise to generative tools.

The human factor is weak. Research shows only a small fraction of people can reliably spot deepfakes.

What is working right now

Behavioral biometrics

Banks applying keystroke and interaction patterns detect account takeover earlier, with fewer false declines. Public case studies cite millions in monthly savings and detection rates above 90 percent.

Real time anomaly scoring

Card networks and issuers run risk scores in milliseconds to approve good users and stop bad ones. Mastercard’s Decision Intelligence, for example, analyzes more than 100 billion transactions a year with sub second scoring. Adaptive analytics platforms such as those deployed at major banks and credit unions have reported lower false positives and higher fraud detection.

Explainable AI

Risk teams and auditors need to understand why a model flagged an action. Transparent features, challenger models, and human in the loop review are becoming best practice for regulatory alignment.

Deepfake detection and liveness

Financial platforms are adding liveness checks and deepfake detection during onboarding and recovery flows. With research showing humans rarely spot fakes unaided, automated screening is critical in high risk steps.

Where today’s tech still struggles

• Synthetic IDs that mix real and fabricated data can pass document checks and velocity rules
• Low quality video such as low light, blur, or compression still reduces deepfake detector accuracy
• Model governance remains a challenge, explainability and fairness must be actively managed
• Recovery flows and device rebinding often remain weaker than login, creating takeover risk

Step by step: auditing and upgrading your fraud stack

  1. Map your critical journeys: open, pay, withdraw, add payee, recover account and flag legacy steps

  2. Baseline loss and friction: track approval rates, false positives, manual review minutes

  3. Strengthen identity proofing: add biometric liveness and deepfake defenses at onboarding and recovery

  4. Layer behavioral analytics: spot bots, remote access tools, and mule handlers during sessions

  5. Upgrade real time scoring: score every event in context, not just transactions

  6. Make models explainable: provide reason codes, drift monitoring, and challenger models

  7. Add persistent authentication: device bound passkeys and phishing resistant methods for recovery and rebinding

  8. Calibrate step ups: apply biometrics where risk scores spike, keep low risk flows seamless

  9. Close the loop on scams: add customer warnings and pauses for high risk push payments

  10. Measure and document: capture loss avoided, approval lift, and keep audit trails current

Examples in the field

• A major bank reported $2M per month in fraud savings using behavioral biometrics
• Mastercard’s Decision Intelligence processes 100B plus transactions a year with AI driven risk scoring
• Danske Bank and large credit unions reported improved outcomes after adopting adaptive analytics
• Financial institutions now rely on biometric onboarding and liveness to block deepfake KYC attacks

Designing for adaptation, not perfection

Attackers iterate, so defenses must too. The goal is layered, explainable, and adaptive security that keeps pace with both regulatory expectations and attacker innovation. By combining strong identity proofing, behavioral analytics, real time scoring, and persistent authentication, financial platforms can reduce fraud losses while improving approvals and customer trust.

Sources

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