Audit analytics – Getting it right
Industry tailored audit analytics are an imperative
As data continues to grow at exponential rates, analytics have emerged as new competitive differentiators. Businesses are successfully implementing analytics to achieve improvements in productivity, sales, profits and other key performance metrics.
Conventional auditing approaches can no longer keep pace with the ever-growing information influx. Audit analytics, however, make it possible to analyse entire sets of financial transactions increasing the value of the external audit by helping to validate low risk transactions efficiently and by revealing more granular insights for higher-risk transactions.
Beyond conventional audit analytics solutions that are applied today consistently throughout all locations worldwide, the Audit and Assurance practice has developed industry specific audit analytics solutions to run laser sharp audits. These are enhanced with key metrics and benchmarks to provide clients with additional insights as to how their processes and controls compare to industry standards.
Audit analytics for telecommunication companies
Large and highly complex data flows in the telecommunications industry (particularly in the billing and revenue cycle) make audit analytics ideal for performing a more efficient and effective audit. However, audit analytics in this area is effective only if it is tailored to client-specific billing and revenue data flows. Additionally, the analytics solutions need to consider the high pace of change in client environments and evolve continuously.
In the example of the billing and revenue cycle, a state-of-the-art analytics solution utilizes predictive techniques and allows the data analyst to model revenue scenarios. For example, when analysing billing transactions for retail contracts, the data analytics solution should utilise not only past billing transactions but also existing contract plans, sales promotions and campaign data to prepare revenue expectations in line with accounting standards.
Based on contract plans and contract lifetimes, the data analytics solution enables an initial estimation of revenues for a predefined period to be made. Further, by using promotions and campaign data, the predictions can be further refined to account for existing and future churn rates, which might affect revenue. The analytics-enabled auditor can then compare these predictions with actual revenues and draw a precise conclusion about recognised revenue.
Whereas industry-tailored data analytics techniques can be applied effectively to retail revenue streams, the analytics tools need to be further enhanced for revenue streams from corporate and wholesale business data. For these client segments, traditional substantive procedures still play an important role in auditing due to the nature and complexity of large contracts.