Companies are using advanced data analytics to enhance their risk functions, but there are challenges in implementing it.
In an age of digital transformation, new technologies such as artificial intelligence (AI) and machine learning offer businesses deeper and more complete insights than ever before. However, this transformation can also create new legal, compliance and fraud risks. In response, companies are using forensic data analytics (FDA) to mitigate these risks and optimize their risk management strategy.
The latest biennial EY Global Forensic Data Analytics Survey, based on the responses of 745 executives from 19 countries, examines how organizations are using FDA to manage risk and outlines the challenges they must overcome in order to do so effectively.
The benefits of FDA
The previous EY surveys in 2014 and 2016 suggested that companies were holding back on FDA investment because of reservations about cost and lack of confidence in the underlying technologies. This sentiment has changed.
Instead, the respondents to the latest survey expressed a strong belief in the value of FDA and its wide-ranging benefits. Improved risk assessments rank the highest at 88%, closely followed by the ability to detect risk in large data sets, at 87%. These results are not surprising given the use of FDA among internal audit professionals in making audit selections and assisting legal and compliance professionals with regulatory-related activities.
Cost reduction was cited by 55% of respondents as the main benefit of FDA. This is clearly important to this year’s respondents, and the comparable figure in the 2016 survey was only 42%. FDA has a cost, as with any risk management process, but if the benefits outweigh the costs then it is seen as a worthwhile exercise.
Demand for resources and skills
While companies are investing in a range of advanced technologies, this on its own is not enough; data analytics and AI require human input. While the routine collection, management and analysis of data is increasingly being completed by machines, employees must possess the knowledge to develop the algorithms and interpret the outputs.
The EY survey results demonstrate that few companies are confident that they currently have the requisite skill sets to do this. Only 13% of the respondents felt that their company’s FDA technical skills were very mature, while just 12% believed that it had the appropriate data analytics or data science expertise.
Moreover, in order to ask the right business risk questions, an FDA program needs to combine the right data sources in an intelligent manner. These sources include both structured and unstructured data. However, analyzing large volumes of data collected from disparate sources continues to be a challenge and requires better integration.