Predictive analytics have succeeded to improve the efficiency of various business processes markedly. One rapidly growing application area is internal audit. Analytics enable monitoring and auditing to shift from small samples to analyses that are based on overall data and automation, to continuous auditing. This way we can even better respond to the management’s real-time expectations and also to the needs related to the supervision of regulations and fraud detection.

The traditional methods of internal audit have mainly been based on manual data review, assessment and small samples. In the monitoring of increasingly digital and international processes, this is not sufficient: in audit processes based on samples and excels, risks tend to increase to a critical level. The journey toward internal audit that meets the needs of today begins when data from various sources is stored in databases, from where data mining is easy, error-free and quick.

Big data and predictive analytics

Big data consists of a variety of data forms and the time and place bound properties of the data. The big data that is essential from the perspective of internal audit may include the data concerning the time and place, recorded in the logs and cookies of the company’s services and internal processes, but also the discussions and comments concerning the company on social media. The greatest benefit is achieved when big data is linked as part of the traditional data package and the organisation’s common data basis.

The number one tool for internal audit is predictive analytics. Particularly as data quantities grow, it provides significant opportunities to increase the efficiency of and improve the audit. Analytics enable us to shift from small samples to analyses that are based on overall data and automation, to continuous auditing. With the help of predictive analytics methodology, we can automatically monitor the implementation of various regulations, objectives and practices, while also detecting risks and frauds.

Fraud detection

Fraud detection is used to prevent both internal and external frauds. Based on the fraud cases detected with the help of analytics we can build monitoring models to scan corresponding cases from the data flow. This way those attempting to harm the organisation through their actions will be caught before any damage is done.

Analytics improve the process of getting answers to desired questions by identifying and listing the problems automatically. It can also list the proposed decisions to the problems. With the help of various prediction models we are able to see what will happen if operations are not changed or what the best and worst possible scenarios are. Prediction models also help us to list the decision options and make predictions on the impacts of different decisions, like: what if.

Automated information security audits

The time of interview and user right listings in information security audits is over. Today, analysis models provide an access to the user history of systems and error identification direct from the systems and their log data.

When routine data searches and definition monitoring are automated, internal auditors have time left to focus on the development of operations and more strategic duties. At its best, the real-time monitoring enabled by predictive analytics and the ability to see the future makes the internal audit function a more integral part of the business and everyday business management. 

Antti Syväniemi is the CEO of Houston Analytics Oy[P2] . His expertise areas are leadership models using analytics and intelligent strategy processes.

Houston Analytics is an analytics company started by experts on data-driven leadership. By linking data to business decision-making Houston Analytics is guiding its customers to market leaderships in their industries.