How are financial services organisations using data to fight financial crime?

Financial services organisations are facing unprecedented pressure from increasingly complex financial crime threats. With the use of AI, these threats are evolving at speed and organisations are struggling to keep up with the pace. For organisations to remain on top, they must embrace a more integrated approach; combining data, AI and team collaboration to deliver the accurate threat detection needed.

The cost of financial crime

Use of AI as well as the explainability and mass usage of large language models is now a key concern for regulators. The regulatory regime is evolving quickly, as regulators look to maintain confidence in the financial services ecosystem in the face of the growing number of threats. The responsibility for financial crime prevention (including the growing cost to address) now sits firmly with financial services organisations.

Addressing the rapidly increasing cost of financial crime is one the biggest issues currently facing financial services organisations. In 2025, the annual cost globally was estimated to be $4.4 trillion, and rising quickly, according to a recent report from Nasdaq Verifin. This cost directly impacts financial performance and is therefore having a significant negative impact on profitability.

Centralising data

Data plays a critical role in detecting unusual or suspicious activity, but many financial services organisations find it difficult to utilise their data effectively. This is because it often sits within individual teams (security, fraud, financial crime or compliance) and is not always shared. As a result, many organisations are now moving towards a ‘Fusion Center’ approach, which is already common in the US. This approach is based on centralising data so that it is possible to detect and respond appropriately to any suspicious activity – and in doing so, increase detection accuracy and reduce false positives.

For organisations to successfully utilise this approach they need to deploy supporting technologies, such as a data platform. Data platforms can facilitate access to a range of disparate data sources allowing all teams to have access to all data they need. Role-based access can also be applied throughout, to ensure that individuals can only access the data that they have permission to see as part of their remit. This is exactly how C6 Bank uses Splunk to underpin its fusion centre, processing four million transactions per day, opening 96% of new client accounts with zero manual review, and maintaining an industry-leading fraud rate of less than 0.001%

Using data and AI to fight financial crime

Recent research from Feedzai suggests that 90% of organisations are already using AI to fight bad actors, although 87% concede that in doing so they are facing challenges with data management. AI is also being successfully used to improve financial crime detection. Machine learning is a well-established technique, but the growth in the use of natural language to interrogate data is enabling non-technical domain experts to interrogate data sources they may not otherwise have been able to access.

In addition, the relentless growth of agentic capabilities is delivering another step change in capability. The ability of agents to process routine tasks and detections will mean that risk scoring and anomaly detection will increasingly be delivered by agents governed by evidence, approvals and human accountability. This will free up analyst time to focus on the highest risk activities.

Looking ahead, by using emerging technologies such as MCP (Model Context Protocol), financial crime teams will be able to more easily bring external data sources into their analysis. With a likely move towards increased data sharing within the industry, financial services organisations will soon have all the tools they need to successfully fight financial crime using data.

Using connected data in combination with well-governed AI will empower any organisation deliver a faster and more accurate response. While there are differences in the type of financial crime / fraud experienced by each sub-vertical, the need to harness all relevant data is a challenge faced by all organisations.

Support from Softcat and Splunk

Successfully tackling financial crime requires more than technology alone. To do this effectively, there needs to be a combined effort of data, people, processes and platforms. We are perfectly positioned to help financial services organisations bring these pieces together to monitor and store data effectively, reduce false positives and detect threats that are relevant to the business in hand. Our Networking, Security and Financial Cime / Fraud specialists together with our Data Automation and AI specialists can help organisations assess their current capabilities, identify opportunities to better utilise their data and implement technologies to support them.

Keen to understand how Splunk and Softcat can improve your financial crime detection capabilities? Please click here to find out more. We can run design workshops and deploy professional services to help you define and deliver enhanced capabilities using the Splunk data platform.