Reducing the complexity of financial networks using network embeddings.

Area of Science:

• Financial auditing
• Data science
• Network analysis

Background:

• Untrustworthy financial information, exemplified by scandals like Enron and Petrobas, poses systemic risks to economic stability.
• Increasing complexity of business transaction data makes it challenging to identify core processes and detect financial inconsistencies.
• Traditional auditing methods struggle with large, complex datasets, necessitating advanced data-driven approaches.

Purpose of the Study:

• To develop a method for efficiently extracting primary business processes from large transaction datasets.
• To leverage network embedding techniques for identifying meaningful clusters representing business processes.
• To enhance the accuracy of predictive models for detecting financial inconsistencies.

Main Methods:

• Applied network embedding techniques to transaction data, creating vector representations of network nodes.
• Utilized clustering on these vector representations to group nodes into meaningful primary processes.
• Constructed predictive models using extracted process flows to identify data inconsistencies.

Main Results:

• Successfully extracted primary business processes, yielding clusters comparable to those identified by financial experts.
• Demonstrated the effectiveness of network embedding in uncovering underlying business process structures.
• Developed improved predictive models capable of detecting financial inconsistencies more efficiently.

Conclusions:

• Network embedding offers a powerful, data-driven approach to enhance financial auditing.
• The methodology facilitates the identification of key business processes and the detection of anomalies.
• This work supports the evolution of financial audits towards more modern, technology-driven practices.