Fraud Has Moved Outside the Bank. Protection Should Too

2026-07-01 · Source: HackerNoon · Field: Finance & Economics — Banking & Financial Services, FinTech & Digital Financial Services · Depth: Intermediate, short

Summary

Financial institutions face a growing challenge from sophisticated AI-enabled fraud, which has largely migrated from internal banking systems to external communication channels like messenger apps, social media, and email. Despite substantial investments in infrastructure protection, banks are losing ground, with US consumers reporting over \$10 billion in fraud losses in 2023. Leading banks such as JP Morgan and Revolut are adapting by developing comprehensive protection ecosystems, including consumer awareness programs, real-time prevention, interbank fraud signal sharing, and in-app call verification features. Revolut's app, for instance, identifies suspicious calls and uses machine learning to analyze user behavior. However, most of the banking sector lags, and existing systems often intervene too late. New regulations, including the UK's mandatory APP fraud reimbursement from October 2024 and the EU AI Act, are increasing accountability. The article suggests a future solution: a device-level "trust layer" that interprets communication signals to detect social engineering proactively, advocating for cross-industry data sharing.

Key takeaway

For Directors of AI/ML in financial institutions, your current fraud detection strategies are likely insufficient against AI-enabled social engineering. You must shift investment from solely infrastructure protection to developing or integrating device-level "trust layers" that monitor external communication channels. Prioritize solutions that facilitate cross-industry intelligence sharing, as this is essential for recognizing rapidly evolving global fraud patterns and protecting customers proactively before funds are transferred.

Key insights

Fraud has moved beyond bank perimeters, necessitating device-level protection and cross-industry intelligence sharing to combat AI-enabled social engineering.

Principles

• Fraud detection must extend beyond bank perimeters.
• Cross-industry data sharing is crucial for evolving fraud patterns.
• AI-enabled fraud requires AI-driven detection at scale.

Method

Implement a device-level "trust layer" that continuously collects and interprets signals from messages, calls, emails, and behavioral context to build event chains and detect social engineering before payments.

In practice

• Develop in-app features to verify call legitimacy.
• Analyze user behavior and transaction anomalies with ML.
• Participate in cross-industry fraud intelligence sharing.

Topics

• AI Fraud
• Social Engineering
• Financial Crime
• Fraud Detection
• Device-Level Security
• Cross-Industry Collaboration
• Regulatory Compliance

Best for: Executive, CTO, VP of Engineering/Data, Director of AI/ML, AI Security Engineer, Consultant

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Editorial summary, takeaway, and curation by AIssential. Original article published by HackerNoon.