Turning data into opportunity in financial crime prevention

Laura Eshelby, Head of Economic Crime at Clue Software

Financial crime has always been a threat to the global economy, but advancements in technology have given criminals new tools to adapt their methods. According to INTERPOL’s latest Global Financial Fraud assessment, technology is now “enabling organised crime groups to better target victims around the world”. As criminals become more sophisticated, the volume of data generated by these crimes continues to increase, making it increasingly challenging for investigators to conduct efficient investigations. In fact, 80% of investigators report being overwhelmed by the volumes of data now available, as the growing complexity of these cases creates more connections that are nearly impossible to analyse manually.

However advancements in technology can be leveraged to improve how we investigate more efficiently, the pace and accuracy of which we can identify and monitor threats- and helping us to collate and present our findings more effectively.

To get ahead of this growing threat, organisations must shift their perspective on data, viewing it as an opportunity rather than a problem. By leveraging technology and prioritising data sharing and collaboration, they can manage financial crime more effectively and enhance their overall response.

Our historic relationship with data

Increasing volumes of data are significantly complicating financial crime detection efforts. Investigators are now tasked with navigating vast amounts of both structured and unstructured data and current systems often struggle to manage this load. As a result, crucial information can be overlooked, making it harder to identify malicious activity.

Not only this, but large amounts of data are making investigations more time-consuming and less effective, with investigators frequently missing critical insights. The challenge of connecting the dots across disparate datasets is leading to potential blind spots in investigations. This inefficiency not only delays case resolutions but also increases operational costs, as more resources are needed to sift through data and piece together insights. Overall, across both the private and public sectors, an estimated  US$1.28 trillion is spent annually to combat financial crime, reflecting the high costs of this data-driven challenge.

Data as an opportunity

However, while the growing volume of data can be overwhelming, it presents a significant opportunity for investigators in the fight against financial crime. By viewing the increasing amounts of data in financial crime as a resource, rather than a burden, organisations can enhance their ability to detect and prevent criminal activity.

Advanced technologies, such as AI, enable investigators to analyse numerous datasets, identifying hidden patterns and extracting valuable insights that may have initially been overlooked. Utilising predictive analytics can also anticipate potential harmful risks, allowing organisations to proactively address threats and prevent financial crimes before they occur. In the UK, for example, the banking industry has been working with the Financial Conduct Authority to test new ways of predicating patterns of fraudulent activity, and using this insight to strengthen controls to respond.

By fully leveraging the potential of data, investigators can transition from reactive responses to a more strategic and preventative approach that ultimately strengthens their defences against financial crime.

The link between technology and the human element

AI significantly aids investigators by automatically identifying and classifying key information within large datasets, such as inboxes, file storage, and phone downloads of suspects. Given the vast amounts of digital data associated with a case, investigators lack the resources to manually search through everything. Coupled with the historic issues associated of disclosure documents, technology can really take the pain out complex processes. 

With automated entity recognition, AI can be trained to spot key investigative information, extract it and present it in a meaningful way, helping to uncover new lines of enquiries. Additionally, investigators can also teach AI to continuously and automatically cross-reference any new material with existing data, ensuring nothing is overlooked. On top of this, AI can benefit the operational efficiency and productivity of those operating financial and economic crime services. For example, AI can be used to develop rules based on data analytics, helping to quickly identify links in data and the potential risks it may have. It can also help to speed up investigative processes, using large language models to sift through vast amounts of data, either for pre-investigation analysis or post-investigation case and file management for evidential and disclosure purposes.

However, despite these advancements, there is still a place for human investigators, with AI assisting, not replacing humans. While AI handles the heavy lifting of processing and analysing vast amounts of data, humans are still needed to fact-check and interpret AI findings. AI helps to scale ever-increasing amounts of data, eliminating the time-consuming stages of looking over evidence by hand.

Indeed, the introduction of AI into large systems will free up vital time for investigators to perform much needed pre- and post-case analysis, providing vital insights back to the business on risk indicators and recommendations for control hardening.

The answer lies in data sharing and collaboration

In the world of intelligence and investigations, viewing data as an opportunity requires breaking free from siloed approaches. By doing so, organisations can more efficiently leverage shared resources and specialised expertise, exchanging knowledge and trends to enhance early detection and prevention of financial crime. This wider picture enables a more comprehensive understanding of the current risks and threats. We’ve seen this in a recent private-public partnership announced by the NCA which focuses on data in banking sectors to better identify criminality. Banks such as Barclays, NatWest, Lloyds and Santander are now providing data to the NCA where there are red flags and indicators of risk, and then joint multi-discipline teams of data scientists and intelligence analysts review it. This improves the overall understanding of risk and allows them to act where required.

Fostering a broader and more collaborative dialogue among organisations is key to pooling data and aligning with integrity and regulatory goals. Open communication and close partnerships will allow for compliant solutions that meet the diverse objectives of all parties involved, ensuring a more cohesive approach to tackling financial crime. Over the last few years, the Home Office published the Economic Crime Plan 2, which centres on public and private partnerships and builds on previous outputs such as National Risk Assessments on money laundering and terrorist financing. This has led to reforms of the UK’s Suspicious Activity Reporting regime and Money Laundering regulations, as well as legislative reform with new laws passed including the Economic Crime and Corporate Transparency Act and the Online Harm Safety Bill. Overall, the plan shows the importance of partnering across sectors with data sharing at its heart.

To further enhance this collaboration, it’s essential to adopt proven frameworks, technologies and partnerships that enhance data sharing while addressing associated risks. Learning from other sectors, such as the legal and medical fields, which have successfully integrated cross-organisational collaboration and technology, can provide valuable insights for financial crime prevention efforts.

The future of financial crime prevention

Ultimately, there is an urgent need to address data deluge in the fight against financial crime. Organisations need to view increasing amounts of data as an opportunity to utilise technology to correctly detect and in turn mitigate threats. By breaking down silos and adopting a more collaborative approach, organisations will be able to gain new insights, detect hidden patterns, protect consumers and effectively combat financial crime. We need to embrace technology now, as a secure financial industry will be through data convergence and collective and pooled intelligence.

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