Automated data analysis for auditors in SAP

Automated data analysis and Process Mining for Compliance in SAP.

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PROCESS AUDIT FOR SAP AT THE TOUCH OF A BUTTON

Data analysis has never been easier for internal and external auditors, as well as specialist departments. Apply for an SAP user, install zapliance and get started. We have already automated the SAP-related data and process audit from data extraction to the zap audit report for you.

Thanks to the latest data science methods and intuitive user guidance through the integrated auditing procedure, you are free to concentrate on the most important job again: auditing the essential findings.

WHY ZAPLIANCE?

Save time

Automated data analysis and ready-to-go audit questions make for an efficient route to a successful SAP audit.
zapliance automates all data analysis steps that can be automated:

  • Data extraction
  • Process Mining
  • Data analyses addressing 135 audit questions

Focused on the essentials

Your SAP audit starts only once the results of the automated analysis are available.

In the subsequent audit, you will be guided interactively through the integrated data analysis auditing methodology.
You benefit from:

  • Effective reduction of false positives
  • Unique auditing methodology applying Professional Judgment
  • Focus on the main findings

Pioneering Audit Digitization

We are developing the future of SAP auditing together with you. Through a unique and unprecedented combination of science, development and testing, as well as your feedback and suggestions, we are continuously working on:

  • Interactive user guidance through the test methodology
  • Free implementation and improvement of indicators
  • Integration of machine learning (e.g. for duplicate payments)
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WHAT DISTINGUISHES ZAPLIANCE FROM
PROCESS MINING?

Process mining is a concept on everyone's lips today. Auditing departments and external auditors are currently engaged in intensive discussion about the value contribution of the technology.

As standard, process mining is very complex, time-consuming and produces unclear process models. What the auditor should focus on remains completely unclear for the time being. So we use different methods for the automated reconstruction and subsequent visualization of the processes, whereby the audit question is always in the foreground. In this way, you are only shown circumscribed process models with a clear focus on compliance violation.

Furthermore, the process visualization is based on the latest findings of usability research, is continuously evaluated and improved by means of eye tracking. This ensures interactive user guidance and one of the most advanced visualizations of your SAP processes.

Get started today with the most advanced form of data analysis in SAP and draw a line under discussion on the value contribution of audit and process mining now!

AUDIT DIGITIZATION

Audit professionals must keep pace with the ongoing digitization of their environment and develop their own strategies for handling this.

Digitization of business processes and internal control systems opens up new opportunities for audit automation and audit digitization. This leaves audit professionals free to focus on the key aspects of their work.

The shape of the audit of the future will be influenced by attitudes to the value of benefits offered by automated analyses as compared to professional judgment based on professional experience and knowledge of specific industrial and commercial sectors. Optimal use of both will enhance quality of intelligence gleaned from the audit.

The bottom line for audit professionals is “Digitize or die”.

Prof. Dr. Markus Nüttgens, Hamburg Research Center for Information Systems, University of Hamburg

OUR PROMISE TO YOU

Time savings in SAP data analysis

Time saving

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Mature data analyses from a single source

Mature
data analysis

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Intuitive user guidance

Intuitive
user guidance

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Data security for SAP data

Data security

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zapliance develops data analysis for the automated audit of SAP and the visualization of compliance violations in process models.

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