Beyond prevention: The convergence of detection, investigation & organizational strategy

Key insights:

• • • Fraud management works best as a connected workflow — Aligning corporate fraud, AML, compliance, and investigation teams can strengthen visibility and response.

    • Monitoring must move beyond on-boarding — Existing customers require ongoing risk-based review, smart alerts, and transaction monitoring that can identify potentially suspicious behavior without overwhelming teams.

    • AI can accelerate investigations, but humans remain essential — AI-driven automation helps process data and prioritize alerts; however, skilled analysts are still needed to provide context, judgment, and industry expertise.

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Fraud prevention represents only the first step in comprehensive fraud management. Organizations must develop robust detection and investigation capabilities to identify fraudulent activity and respond effectively.

Indeed, the most successful organizations think about fraud management in a systematic way, says Andrew Pellington, a senior director in Risk & Fraud solutions at Thomson Reuters. “The most successful organizations think about fraud management in more of a workflow phase that moves systematically from initial prevention through ongoing detection and into detailed investigation,” explains Pellington.

Phases of organizational structures

Understanding how these phases interconnect and then building the proper organizational structures to properly execute them can help corporate risk, fraud & compliance teams create the foundation for effective fraud protection. These phases include:

1. Build organizational alignment across fraud and compliance functions

One of the most significant structural shifts in fraud management is the convergence of corporate fraud and anti-money laundering (AML) departments. Historically siloed, these functions are increasingly merging because fraud and money laundering are deeply intertwined. Fraudsters commit fraud, obtain illicit proceeds, and then need to launder those funds — effectively, two sides of the same coin, Pellington notes.

That means, financial and non-financial institutions can benefit from unified teams sharing data, processes, and expertise; and this convergence extends beyond AML and fraud to prevention, detection, and investigation phases. Organizations can gain competitive advantage when these functions share integrated toolsets, consolidated data sources, and cross-departmental communication. Before sharing knowledge across institutions, however, organizations must first establish robust information sharing across their own departments.

2. Establish monitoring systems for existing customers and accounts

As your organization moves through the fraud management workflow, the focus shifts from high-volume account opening activities to continuous monitoring of existing customers and account holders. This phase requires different tools, processes, and resources than does prevention.

Monitoring — both proactively and reactively — allows organizations to identify suspicious patterns and behaviors, then sophisticated systems must track transactions across time, identify deviations from normal behavior, and flag accounts for review.

Proactively, organizations should segment customers by risk level and establish review cycles: monthly for high-risk customers, semi-annual for medium-risk, and annual for lower-risk accounts. Reactively, they should deploy adverse media and sanctions alerts against public records, coupled with transaction monitoring models that specifically identify potential money laundering or structuring patterns.

“As you move through the monitoring, now you’re looking at your existing customers and account holders, and then you get alerts thereafter,” Pellington explains.

3. Implement alert systems and prepare for regulatory scrutiny

While effective monitoring generates alerts that bridge passive systems and active investigation teams, these alerts need to be calibrated to identify genuine fraud risks without overwhelming investigators with false positives. This requires regular tuning and coordination between technology and investigation teams.

Organizations should adopt scenario planning and war games to test their processes by simulating potential fraud cases, regulatory inquiries, and adverse media incidents. Fraud incidents are a matter of when, not if, Pellington says, and those organizations that proactively test their response processes — rather than waiting for actual events — will maintain regulatory confidence and demonstrate institutional readiness.

4. Leverage AI while maintaining human expertise in investigations

While AI-driven automation of some work processes is a big advantage, deeper dive investigations require specialized expertise that cannot be fully automated. This is where generative AI (GenAI) and agentic AI can create significant opportunities. Agentic AI can prescreen alerts and determine which warrant investigation; and GenAI can rapidly produce enhanced due diligence reports by pulling together transaction histories, communications, vendor relationships, and public records.

Automating this work frees specialized fraud analysts to focus on what humans do best — applying industry knowledge and making judgment calls. Indeed, investigation is equal parts art and science, Pellington explains, adding that AI excels at the science — processing data at scale, and humans excel at the art — understanding context, industry fraud typologies, and customer relationships.

5. Transform data into knowledge and wisdom

The final critical gap Pellington identifies is the journey from information to knowledge to wisdom. Organizations possess unprecedented volumes of data, yet many drown in it without extracting actionable intelligence.

More data doesn’t guarantee better decisions; and organizations must elevate information to knowledge, understanding what their peers are doing, what best practices exist, and which approaches work best for the organization. Wisdom then comes from sharing across institutions, learning from industry experts, and avoiding mistakes others have experienced. This requires deliberate peer learning and thought leadership engagement.

Preparing for the future of fraud

Fraud risks are evolving fast, and those organizations best positioned to keep up will be the ones that keep their teams connected, sharpen their investigative tools, and pair AI with human judgment to act faster and stay more resilient while proactively transforming data into actionable wisdom.

By implementing these five phases of fraud protection, organizations can improve their detection and investigation capabilities and create comprehensive fraud protection that evolves with emerging threats.

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You can find out more about ways to detect and prevent fraud in your organization here