Use Case: Expense Appraisal Automation

Reduce operational costs and increase processing efficiency of expense appraisals with Intelligent Process Automation

The problem for finance departments

Our client, one of the largest professional service providers in the world, provides on-site advisory services. Every week, the finance department receives thousands of expense submissions – manually processing so many expenses efficiently and without error requires a large team of experienced appraisers. For example, for German, Switzerland and Austria, an appraisal team of double-digit size works full-time on this task and still there are delays and errors in the process, which not only frustrates employees but also increases compliance risk.

Our solution

Inspirient’s probabilistic reasoning technology – combining text analysis, business logic, and supervised machine learning – was added as a layer of intelligence on top of Robotic Process Automation (RPA) technology to fully integrate within the expense appraisal process and achieve full end-to-end automation.

Why Inspirient?

Reduce operational costs by at least 70%, increased process accuracy and reduce decisioning time to an instant with Inspirient’s Automated Analytics platform, which can be configured and trained in a matter of hours to perform human-like activities.

Typical Efficiency Gain

Efficiency gain is modeled via the FTE resources currently performing the process that is to be automated.


0
FTE days gained / year
€0
Savings / year

Would you like to achieve superior efficiency through automation?

The Inspirient Automated Analytics Engine automates the entire data analytics process end-to-end: From the assignment of input data, pattern and outlier detection, automated visualization of patterns, weak points and opportunities to automatic generation of textual explanations and recognition of the underlying relationships and rules. Most other analytics solutions rarely include these textual explanations and observations regarding the underlying data relations, which are both critical to provide a deeper level of analysis and more actionable conclusions.

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