A proposal prioritising data sharing across the UK was accepted in the government’s spring budget. There was a particular focus on greater access to, and sharing of data, which could be a big step forward in helping to counter the rise of fraud. Read on to find out why.
Fraud is on the rise in both the consumer and business arenas and fraudsters are becoming increasingly intelligent in their attacks. Fortunately, the right data can be used to identify and prevent incidents. However, a poll at the recent NHS Fraud Conference highlighted there are currently barriers to data sharing, in particular culture, and the fear of misunderstanding regulatory compliance.
It’s important that the government overcomes these issues and looks to share data more widely. Broader access and the potential to combine additional external data sets, should increase the speed and frequency at which it can uncover fraudulent activity and counter the rise.
So how are fraudsters operating in this space? And where should the government be looking?
Concealment Tactics
Fraudsters often employ complex ownership and control structures to conceal the beneficial ownership of companies (those who ultimately own or control an entity). By doing this, they evade taxes, hide sources of wealth, and engage in money laundering.
Data sharing, coupled with advanced analytics, can empower teams to expose these intricate networks, and assist in countering fraudulent activities.
The types of fraud often covered up by complex ownership structures are wide ranging. From investment schemes and phoenix activity (where a company is liquidated to avoid paying its debts, in many cases where they had no intention of paying), to fraudulent loans, false invoicing and more. These convoluted arrangements enable scammers to exploit legal loopholes, evade authorities, and deceive unsuspecting victims.
Example: Understanding Complex Ownership Structures and Fraud
To illustrate the potential of data sharing in detecting fraud, consider the simple scenario shown below, involving Company A and its beneficial owner, John.
With insight into beneficial ownership information and analytics, connections can be easily uncovered at scale, offering invaluable insights for fraud prevention.
Company A is entirely owned by John, making him the sole beneficial owner. Furthermore, Company A owns Company B in its entirety. By tracing the ownership, it becomes evident that John indirectly owns Company B as well, effectively having 100% beneficial ownership in both companies.
Often the structures set up by fraudsters are often much more complex than this, with multiple companies and levels of control and ownership.
However, with insight into beneficial ownership information and analytics, connections like this can be easily uncovered at scale, offering invaluable insights for fraud prevention.
Countering Fraud in Business Loans
The significance of data sharing becomes particularly evident in the context of government loan schemes like BBLs and CBILS. The UK Government disbursed over £47 billion through these schemes, and subsequent revelations have highlighted the potential for fraud due to the lack of stringent due diligence when the loans were handed out.
Overlaying loan data with beneficial ownership information can significantly enhance visibility and expose potentially fraudulent activities. The need for comprehensive data analysis and automation is emphasised to avoid such situations in the future, as taxpayers may be burdened with covering significant losses due to fraud and business failure.
Combining Additional Datasets
In addition to beneficial ownership data, other datasets like VAT numbers and payment history can also prove instrumental in countering fraud. While these datasets are often protected from general use, utilising VAT data at a summarised level and linking it through a universal identifier such as Dun & Bradstreet's D-U-N-S® number can reveal discrepancies and anomalies that may indicate fraudulent activities. By comparing businesses of similar size and industry type, deviations in turnover and costs can be identified, warranting further investigation.
Making Data Accessible
Sir Patrick Vallance's Pro-Innovation Regulation of Technologies Review emphasised the importance of integrating public datasets to support services. The review recommended greater industry access to public data and prioritised data sharing across the public sector to improve policymaking, enhance safety, ensure consistency, and deliver improved services.
Trusted parties, such as the Office for National Statistics' Integrated Data Service, can play a role in making relevant data accessible to public sector analysts and researchers. Augmenting this data with comprehensive commercial datasets, such as Dun & Bradstreet's, can provide enhanced insights for better fraud prevention and overall risk mitigation, as well as driving other benefits such as public service enhancement.
Where Commercial Data Can Help
In summary, the proposal for wider data sharing across the UK Government presents a valuable opportunity to counter fraud and enhance public services. By leveraging comprehensive datasets, advanced analytics, and trusted platforms like Dun & Bradstreet’s, organisations can gain transparency into complex ownership structures, identify potential fraud indicators, and prevent fraudulent activities. Collaborative efforts and the integration of data from various sources are both essential to mitigate the risks associated with fraud and ensure a safer and more secure business environment.
Dun & Bradstreet offers solutions that can significantly contribute to fraud prevention efforts. D&B Beneficial Ownership (UBO) provides comprehensive insights into beneficial owners and ownership structures using the most extensive and trusted source of commercial data. Additionally, D&B Finance Analytics offers tools to assess portfolios, identify risks like late payments and delinquency, and clients leverage our proprietary scores and ratings for informed decision-making.