IMF Says AI Can Win Fraud Fight if Banks Start Sharing Data

In today’s interconnected financial system, digital vulnerabilities do not remain localized. They propagate. Fraudsters now operate with the speed, scale and coordination of multinational enterprises, leveraging emerging technologies and regulatory fragmentation to exploit vulnerabilities faster than authorities can respond.

Against this backdrop, the conversations at the International Monetary Fund (IMF) and World Bank’s annual Spring Meeting this week are centered around how financial institutions can respond proactively to the digital fraud landscape.

The implication of the discussions is stark: the financial system has digitized faster than its governance frameworks have evolved. And until coordination, interoperability and data-sharing architectures catch up, the IMF and World Bank are alleging that fraud will continue to outpace enforcement.

The IMF’s call for deeper cooperation and data sharing is, in many ways, a recognition that the existing paradigm has reached its limits. The question is no longer whether collaboration is necessary, but how quickly and effectively it can be achieved.

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The Architecture Problem Behind Financial Fraud’s Explosion

The IMF’s latest technical work on digital public financial management focuses not just on fraud itself, but on the structural weaknesses in the global financial system that allow fraud to scale. What once appeared as a patchwork of isolated cyber incidents has evolved into a sophisticated, industrialized ecosystem of cross-border financial crime.

The challenge of combating digital financial fraud encapsulates a broader tension in the global economy: the mismatch between the borderless nature of digital systems and the territorially bounded nature of governance. Across both public and private financial systems, data remains fragmented and locked inside national jurisdictions, siloed within institutions, and stored in incompatible formats.

Even where large volumes of financial data exist, they are often incomplete, inconsistent or inaccessible across systems. How this tension is resolved will shape not only the future of financial security but also the broader trajectory of digital transformation.

After all, fraud networks exploit precisely these weaknesses. A typical scheme may originate in one jurisdiction, route through multiple intermediaries, and exit through another system before detection mechanisms are triggered. Each jurisdiction sees only a fragment of the activity.

Findings in “Identity at Scale: Where KYC/KYB Touchpoints Create (or Contain) Agent Risk,” a new report from PYMNTS Intelligence and Trulioo, highlight for firms the impact that continuous life cycle management can have in defending against AI-powered fraud.

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Data Sharing as Infrastructure, Not Policy

What distinguishes the IMF’s current stance is its emphasis on data sharing as infrastructure, rather than as a discretionary policy choice. Both the IMF’s report and the Spring Meeting takeaways highlight that technologies such as application programming interfaces (APIs), standardized data formats and interoperability frameworks are essential to enabling meaningful data exchange across institutions

Findings in the “2025 State of Fraud and Financial Crime in the United States,” a report produced by PYMNTS Intelligence in collaboration with Block, show that unauthorized-party fraud, driven by credential theft and account takeovers, now makes up 71% of fraud incidents and dollar losses. Nearly seven in 10 financial institutions surveyed (68%) have increased fraud-detection spending year over year, while the share citing cost as a barrier to adopting new fraud tools fell to 36% from 60% in 2024.

Artificial intelligence, machine learning and advanced analytics can dramatically improve fraud detection by identifying complex patterns across large datasets. The IMF noted that these tools enable more accurate forecasting, anomaly detection and risk assessment when applied to financial data, but also stressed that their effectiveness is directly proportional to the quality and breadth of the data they can access.

In fragmented systems, AI models are constrained by limited datasets and produce correspondingly narrow insights. In integrated systems, the same tools can identify cross-border fraud patterns that would otherwise remain invisible.

New technologies, the IMF report stressed, are not inherently effective; their impact depends on institutional readiness, data governance and implementation strategy. The report explicitly warned against “solution-in-search-of-a-problem” adoption driven by hype rather than measurable value.

And while there appears to be broad consensus on the need for greater cooperation, translating that consensus into actionable frameworks remains a formidable task. Differences in legal systems, regulatory priorities and technological capabilities present significant hurdles.

Moreover, incentives are not always aligned. Financial institutions may be hesitant to share data that could expose vulnerabilities or competitive information. Governments may be cautious about entering into agreements that could be perceived as compromising national interests.

But, ultimately, addressing the vulnerabilities of today’s digital fraud landscape requires a level of coordination that matches the scale and sophistication of the threat. Anything less could risk leaving the system perpetually one step behind.