The financial industry is under growing pressure to modernise, particularly when it comes to fighting financial crime. In this context, artificial intelligence (AI) has become a powerful tool for improving anti-money laundering (AML) efforts and ensuring regulatory compliance. But for many institutions still running on legacy systems, implementing AI can feel like an uphill battle.
Symphony AI, a developer of vertical-specific AI applications, recently outlined best practices for a seamless AI integration into legacy financial systems.
Integrating AI into older systems is no longer just a long-term ambition—it’s an urgent necessity. However, challenges such as outdated infrastructure, data silos, regulatory complexity and high costs make the transition difficult. With the right strategy, though, firms can embrace AI without risking disruption or non-compliance.
One of the most effective approaches is to start small, using phased implementation. Rather than overhauling systems all at once, financial institutions should begin with controlled pilot programmes. These can use AI overlays to test the effectiveness of tools like generative AI, while avoiding full-scale changes to existing infrastructure. This approach reduces risk and ensures compatibility before scaling further.
Another key strategy is to use APIs to modernise without needing a complete system rebuild. API-based AI tools enable real-time fraud detection, payment screening, and risk scoring while leaving core systems untouched. For example, APIs can support name screening or detect suspicious payment patterns—enhancing AML efforts without the need for costly backend updates.
Scalability is also a concern when running AI models on outdated systems. Cloud-based AI platforms offer a cost-effective way to handle large volumes of compliance data, supporting real-time processing, faster AI training, and greater flexibility. By moving compliance workloads like AML and sanctions screening to the cloud, institutions can ensure faster updates and better security.
Effective AI deployment also depends on clean, unified data. Many legacy systems store compliance data in isolated silos, preventing accurate AI decision-making. To address this, financial institutions must prioritise data standardisation and integration. Structured data enables better transaction monitoring, more accurate risk scoring, and fewer false positives in AML case management.
From the outset, AI tools must be aligned with regulatory requirements. This includes AML laws, GDPR, and FATF guidelines. Solutions should offer audit trails, explainable decision-making, and support for customer due diligence (CDD).
Emerging AI solutions also include agentic AI—tools capable of making autonomous decisions within pre-set compliance boundaries. These models can flag high-risk transactions, trigger enhanced due diligence, or escalate cases automatically, reducing the burden on human analysts while maintaining compliance oversight.
Finally, working with third-party technology providers can help financial institutions adopt AI faster and more efficiently. Integrating external AML monitoring, sanctions screening, and fraud detection tools offers access to cutting-edge capabilities without having to build AI systems in-house.
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