The debate between building and buying AI agents for AML and CFT compliance has shifted dramatically in 2025. Earlier this year, financial institutions were split between developing their own AI agents or purchasing pre-built solutions.
Now, the tide has clearly turned toward buying, as banks and FinTechs realise that purpose-built AI agents not only accelerate compliance but also deliver superior value across operations, claims Workfusion.
WorkFusion has reported that 10 of the world’s top 20 banks have chosen to buy its AI agents to strengthen AML/CFT operations instead of building them internally. This pattern extends across regional banks, FinTechs, and digital-first financial platforms, highlighting a broader industry shift toward rapid implementation and proven performance. According to a June 2025 analysis by Boston Consulting Group (BCG), FinTech revenues grew by 21% year-over-year in 2024, compared to 13% the year before. BCG also noted that banks are setting higher revenue targets for new, technology-driven products and services, recommending that “technology resources should be allocated away from cost avoidance initiatives and toward projects that improve new features, enhance integrations, etc.” This advice has resonated with financial engineering teams, who are being asked to drive innovation—not maintain legacy systems.
For many in engineering or development roles, the reality of building an AI agent in-house is sobering. Self-builds are expensive, time-consuming, and prone to failure, particularly when the end goal is a regulatory-ready, end-to-end compliance solution. These projects often fall short in addressing critical operational challenges such as compliance accuracy, customer satisfaction, and scalability. As a result, the cost and complexity of internal builds are driving organisations to seek ready-made alternatives that can integrate seamlessly into their existing systems.
WorkFusion argues that buying AI agents doesn’t just reduce time to value—it creates geometric, rather than merely multiplicative, growth. To illustrate this, the company compares the two progressions: multiplicative growth follows a steady pattern (2, 4, 6, 8, 10…), while geometric growth accelerates far faster (2, 4, 8, 16, 32…). The latter, WorkFusion says, represents the compounding benefits customers experience when they deploy multiple AI agents across their compliance operations.
In practical terms, the advantages are clear. A top 25 US bank that partnered with WorkFusion successfully launched new revenue-generating payment products without expanding its compliance headcount, maintaining regulatory standards across business lines. Another customer, a top 10 US investment bank, used WorkFusion’s AI agent ‘Tara’ to review alerts in its payment sanctions screening. When the bank later launched a Banking-as-a-Service product, it redeployed Tara to power scalable payment screening, enabling the new business line to process 1.5m alerts annually with a 0.03% error rate—none of which were unexplainable.
This kind of performance, achieved with an existing AI agent, would be difficult—if not impossible—for internal development teams to replicate. The geometric value growth delivered by WorkFusion’s pre-built agents demonstrates that the buy-versus-build debate is, in many ways, over. Financial institutions are learning that investing in proven AI solutions is not only faster and more efficient but also transformative for compliance and business growth.
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