Tue, May 7, 2024
In recent years, businesses have seen a great increase in the volume of data generated in their business operations, whether it be related to transactions, third-party management or general business operations. There have also been vast improvements in the ability of technology to analyze the data generated effectively to provide insights across a wide range of business risks, including fraud and corruption prevention. Regulators have kept up with these developments, and there is now an expectation for businesses to use technology in their risk management programs, but, in practice, the effective use of this technology is way behind the curve.
A recent Anti-Fraud Technology Benchmarking Report published by the Association of Chartered Fraud Examiners (ACFE) and analytics software company SAS outlined that 91% of respondents were using data analysis techniques as part of their anti-fraud program. The highest-used techniques were reported to be exception reporting and automated red flags, and the most common software used was spreadsheets and visualization software, audit testing technology and in-house software. In Kroll’s experience, however, even where organizations are harnessing data and using technology, the software being used is oftentimes outdated and not suited to effectively detecting issues. Organizations are repeatedly suffering losses or facing regulatory action that could likely have been prevented had they been using appropriate data and technology in their risk management program.
A further issue raised in the Anti-Fraud Technology Benchmarking Report relates to limitations to staff skillsets in implementing technology. In Kroll’s recent experience with clients, we have found that while risk and compliance departments are very knowledgeable about their fraud and corruption risks, they can struggle to build the appropriate analytical tests to ensure their risks are being appropriately monitored. This can be for a number of reasons, including:
Interestingly, in assessing the effectiveness of 18 anti-fraud controls, the ACFE’s Occupational Fraud 2024 report identified “proactive data monitoring/analysis” amongst the most effective controls in reducing fraud loss (50%) and fraud duration (50%). We believe many organizations can reduce their fraud by reinforcing their risk programs with the use of technology and data analytics.
To ensure they are properly protected against the increased sophistication in the ways internal and external parties commit fraud and corrupt acts, organizations must properly consider how they are leveraging data and technology to detect these types of risks. Key to this is ensuring the relevant functions are adequately resourced to be able to effectively analyze and interrogate the relevant data. This can be a daunting task for many organizations, and Kroll would recommend that organizations start by identifying their highest risk areas, implementing a robust testing program to demonstrate the usefulness of any data and technology used, and importantly, incorporating learnings from the experience into the risk program as its coverage increases. An effective program should take the following into account:
The data relevant to identifying fraud and corruption risks are often sourced from systems that themselves are used by multiple teams. We have found that as well as improving an organization’s risk management program, harnessing the data across your organization can bring other benefits such as identifying potential cost-savings and inefficiencies to the wider organization. For example, when transactions and vendors are being reviewed for fraudulent spend, other cost-saving opportunities can be recognized, and when employee expenses are being reviewed for potential corrupt payments, inefficiencies in the spend on business development can be identified. Demonstrating these additional benefits can be a useful way for risk and compliance professionals to secure budget for the application of data and technology components within their risk programs and to ensure engagement from a wider set of stakeholders in the programs’ outputs.
AI has proven transformative, particularly in combating financial crime. However, the effectiveness of AI hinges significantly on the quality of the underlying data. Read Kroll’s latest article to learn why high-quality, well-structured data is key to harnessing AI’s full potential in the fight against growing financial crime.
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