Enormous amounts of structured and heterogeneous (unstructured) data are generated as part of your daily work. By analysing your data in a targeted manner, KPMG can support you in identifying signs of criminal or other actions detrimental to the company as well as process and control weaknesses preventively and reactively and counteract damage.
Our library of several hundred fraud algorithms and analysis methods such as "machine learning" enables the targeted detection of such incidents.
This type of data audit allows risks to be identified and minimised and white-collar crime to be detected and prevented. This approach makes use of the fact that behaviour that deviates from the norm leaves traces in the digital space, which in turn can be uncovered with the appropriate technical means and know-how.
Our work is based in particular on various analysis models:
e.g. analyses of fraudulent activities as part of audit and compliance audits
For example, forecasts for harmful actions, e.g. for financial and insurance companies
For example, analysing structured and unstructured large amounts of data from heterogeneous data sources for a holistic view
e.g. use of computer-based, cognitive techniques and methods to identify white-collar criminal transactions and behaviour (e.g. machine learning)
Our technology makes it possible to display and analyse digital transaction data from your ERP system (e.g. SAP) in order to identify potential process weaknesses (efficiency) or process and control weaknesses (risks). Your data is used to visualise your processes transparently based on the actual data and compare them with the target process. Inefficiencies or undermining of processes can thus be effectively identified and prevented.
Partner, Forensic, Global Head of Forensic
KPMG AG Wirtschaftsprüfungsgesellschaft
Partner, Audit, Regulatory Advisory, Forensic
KPMG AG Wirtschaftsprüfungsgesellschaft
