Behaviourist Approach as the Need to Use Methods Based on Recognising Customer Behaviour in Support of the AML/CFT System

How behavioural analytics is reshaping AML/CFT risk detection

A behaviourist approach reframes customer due diligence by focusing on observable, measurable patterns rather than solely on static identity data. Financial institutions already collect transactional records, device signals and communication logs; combining these with psychological and behavioural insights turns disparate traces into a dynamic profile that helps spot potential money laundering and terrorist financing (ML/TF) risks earlier. Behavioural indicators can reveal stimulus–response chains, reinforced patterns and deviations from a customer’s normal profile that may signal preparatory or active involvement in illicit activity.

From static profiling to dynamic behavioural risk assessment

Traditional KYC establishes who a customer is and what they declared when onboarding; a behaviour‑centred model asks how the customer actually behaves over time. Transaction velocity, unusual timing, atypical device usage, rapid password changes, odd typing or touch dynamics, repeated small transfers, sudden geographic logins and other deviations can be translated into measurable variables. Using behavioural analytics and machine learning, obligated institutions (OIs) can convert those variables into individual risk scores, enabling ongoing, dynamic risk calibration and proportionate financial security measures.

Behaviour as signal and as context – psychological and situational layers

Observable behaviour must be read against psychological and environmental context. A nervous or evasive customer could be acting under duress, be inexperienced with digital tools, or be deliberately concealing criminal intent. Distinguishing these possibilities requires integrating behavioural traces with context: life events, business sector, product functionality, past transaction patterns and open‑source signals such as social media activity. The aim is not to determine guilt but to reduce uncertainty by mapping which behaviours are inconsistent with the declared relationship and which align with known ML/TF tactics.

Operationalising behavioural analytics in OIs

Operational implementation rests on three pillars:

1. define the normal profile,
2. detect deviation, and
3. translate deviation into action.

A normal profile is built from historical transaction patterns, product usage, device and login behaviour, and service choices for each customer. Behavioural analytics and User and Entity Behaviour Analytics (UEBA) create baselines and flag anomalies. Alerts are triaged by risk level, enriched with external data and subject to human review where required. For remote onboarding and digital services, keystroke dynamics, gesture and device motion patterns, session timing and velocity checks act as continuous authentication and risk signals.

Bastian Schwind-Wagner _Follow

"Behavioural methods turn routine technical and transactional traces into dynamic indicators of risk that can detect emerging ML/TF patterns earlier than static checks alone. When integrated responsibly with human review and legal safeguards, behavioural analytics and biometrics improve detection accuracy and enable better‑calibrated financial security measures.

Institutions must balance detection benefits with privacy, fairness and explainability requirements: behavioural signals should augment, not substitute, traditional due diligence, and governance must ensure models are tested, auditable and proportionate to the risk being mitigated."

Behavioural biometrics – capabilities and limits

Behavioural biometrics enhances identity assurance by evaluating how users interact with devices and services – typing cadence, touch pressure and rhythm, mouse or swipe behaviour, gait or device orientation. These signals make account takeovers and impersonation harder and feed AI models that detect deviations in real time. However, behavioural biometrics is probabilistic rather than deterministic: false acceptance and false rejection rates exist and profiles evolve, so biometrics must be combined with transactional, contextual and human analysis to reach robust conclusions.

Detecting ML and FT phases through behaviour

Customer behaviour can reflect different phases of ML/TF activity: reconnaissance, preparation, execution and consolidation. Examples include building new accounts with minimal data, routing funds through multiple jurisdictions or intermediaries, using numerous small transfers or repeated identical purchases, abrupt changes in transaction destinations, or rapid account closures. Behavioural signals can indicate acolytes or supporters of terrorism who provide funds or logistics, and may expose radicalisation when combined with open‑source indicators. Observing temporal patterns and escalation of unusual actions helps prioritise investigations and reporting.

Distinguishing criminal intent from non‑criminal causes

Not all anomalies mean criminality. Behaviour can be driven by stress, bereavement, low digital literacy, health issues, social pressure or coercion. A responsible behavioural approach filters determinants that are not causally connected to ML/TF – for instance, shyness, illness, or inexperience – to avoid false positives and bias. Structured engagement, targeted questions and enhanced due diligence can clarify motives. Where employees’ behaviour is suspicious, UEBA and internal monitoring support detection and remediation while respecting legal and privacy constraints.

Threat actors, professional launderers and misuse of OI channels

Some perpetrators are sophisticated professionals who exploit product features and regulatory gaps; others use front men, “mules” or unwitting third parties. Behavioural models must therefore capture not only direct actor traces but also signals consistent with external control or orchestration: passive account holders acting on others’ instructions, repeated use of accounts with inconsistent ties to declared activity, or patterns aligned with known mule recruitment and use. Detecting these patterns supports proportional escalation, from enhanced monitoring to suspicious activity reporting.

Regulatory, legal and ethical considerations

Expanding behavioural assessment raises legal and ethical questions: data minimisation, consent, proportionality, and the permissible scope of behavioural monitoring across jurisdictions. OIs should ensure that behavioural profiling is transparent in governance, that models are explainable for audit and supervisory review, and that the use of AI follows fairness, accuracy and privacy safeguards. Where national law permits, behavioural analysis can be a formally recognised element of KYC and ongoing monitoring; otherwise it should be implemented under clear policies that limit scope and protect rights.

Implementation recommendations

To make behaviour work for AML/CFT, OIs need high‑quality, labeled training data, cross‑functional teams combining data science, compliance and psychology, and feedback loops from investigations and FIUs to refine models. Behavioural analytics must be integrated into existing alerting systems, with human review calibrated to risk tiers and escalations aligned with statutory reporting obligations. Continuous model validation, governance around data retention and access, and clear documentation of decision logic are essential to reduce bias and ensure defensible outcomes.

Conclusions – behavioural methods augment, not replace, legal duties

Behavioural science and biometrics provide powerful, measurable signals that strengthen identification, verification and monitoring in AML/CFT frameworks. They enable a shift from static snapshots to continuous, adaptive risk assessment that better captures changing customer profiles and emerging threats. Nonetheless, these tools are part of a layered approach: behavioural evidence should be combined with transactional analysis, psychological insight and legal standards before drawing conclusions or applying measures that materially affect customers. Properly governed and transparently used, behaviour‑based methods materially improve detection efficiency and support more proportionate, timely financial security measures.

Dive deeper

• Research ¦ Kędzierski, M. (2025). Behaviourist approach as the need to use methods based on recognising customer behaviour in support of the AML/CFT system. Przegląd Bezpieczeństwa Wewnętrznego, 2025, 347-390. doi: https://doi.org/10.4467/20801335PBW.25.035.22652 ¦ Link ¦ licensed under the following terms, with no changes made: CC BY-NC-SA 4.0