Banks Detect Fraud Signals Through Customer Behavior Analysis
Banks are intensifying efforts to identify fraudulent activity by analyzing customer behavior during financial transactions.
Social Engineering
A recent report highlights a shift in fraud detection strategies as institutions address rising cases where victims are coerced into initiating payments. The ThreatMark Fraud Readiness Benchmark 2026 outlines evolving challenges, including the increasing use of social engineering tactics, complex reimbursement requirements, and the growing volume of fraud incidents.
Social engineering has become a primary method for attackers to exploit customer interactions. Nearly 55% of surveyed financial institutions reported that social engineering is a key factor in most fraud cases. Cybercriminals often impersonate bank staff or trusted entities to manipulate users into transferring funds. These attacks often bypass traditional security measures because victims use valid credentials and approve transactions independently. Fraud detection systems designed to flag stolen credentials, suspicious devices, or account takeovers may fail to recognize these scenarios as malicious.
To counter this, banks are focusing on behavioral analysis during customer sessions. Analysts monitor for signs of coercion, such as unusual hesitation, repetitive actions, or sudden large transfers to unfamiliar recipients. Behavioral intelligence tools are being integrated into fraud programs to establish baseline interaction patterns and detect deviations. These systems can identify anomalies like uncharacteristic transaction sizes or abrupt changes in user behavior. However, adoption remains limited, with only 18% of respondents deploying the technology and many others planning future implementations.
Reimbursement Policies
Reimbursement policies are also reshaping fraud response strategies. Authorized push payment (APP) fraud, where victims are tricked into initiating transfers, is gaining regulatory attention. In North America, 69% of institutions anticipate mandatory reimbursement rules for APP fraud within two years. Currently, 31% claim their organizations are prepared for such requirements. These regulations could shift financial liability to banks and payment providers, increasing administrative burdens related to claims processing, investigations, and customer communication.
Early detection of fraudulent activity is critical to preventing these costly processes.
Artificial Intelligence
Artificial intelligence is playing a growing role in fraud investigations. Over 91% of institutions believe AI significantly reduces the time required to resolve fraud cases. Applications include linking related alerts, compiling transaction timelines, and prioritizing high-risk cases. By automating routine tasks, AI allows analysts to focus on complex scenarios requiring human judgment. This efficiency helps institutions freeze funds or coordinate recovery efforts more rapidly after fraud is detected.
Blurring Lines
The lines between fraud and cybersecurity teams are blurring as both address overlapping threats. Phishing, malware, credential theft, and account takeover are common challenges for both disciplines. The report notes that 81% of fraud professionals now handle cybersecurity responsibilities, fostering closer collaboration between teams. Institutions are also leveraging external partners and intelligence-sharing networks to detect cross-organizational scam patterns. However, data privacy and compliance requirements remain critical considerations in these efforts.
Identifying Fraud Earlier
A major challenge lies in identifying fraud earlier in the payment process. Social engineering attacks often leave technical indicators intact, as legitimate users execute transactions. Behavioral signals provide an alternative layer of detection, prompting banks to scrutinize user actions during sessions. This approach focuses on whether behavior deviates from established norms and whether intervention is needed before a transaction is finalized.
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