In the new era of digitalisation, there are emerging challenges relating to the new technology risks while fraud risk increases, adopting new and innovative methods in line with technological progress. In this environment, audit should develop data analytics skills that are beyond traditional risk monitoring and fraud detection tools to meet stakeholders' evolving expectations. Clustering is an unsupervised Machine Learning (ML) algorithm (i.e. an algorithm that learns and improves from experience, without input from users) that looks for patterns in data by dividing it into clusters. These clusters are created such that the points are homogenous within the cluster and heterogenous across clusters. Clustering is commonly used in market segmentation and several areas of marketing analytics as well as in fraud detection.

Data has become the backbone of business decision making: Organizations generate a huge amount of data regularly. According to estimates, the total amount of data created, copied, captured and consumed in the world is likely to reach 149 zettabytes by 2024, as compared to two zettabytes generated in the year 2010. Data science has proven to be a powerful tool to extract meaningful insights from this large chunk of data. These insights help organizations in determining any prominent changes that are to be made basis the changing consumer behavior, shortcomings of previous solutions, forthcoming challenges and competition analysis. Most of the organizations today rely on strategic decisions backed by technology.