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Measure cross-sectoral structural similarities from financial networks.

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Summary

This study introduces a novel method to analyze financial transaction data, revealing structural similarities between companies. This approach enhances auditing by detecting bookkeeping changes and understanding client relationships for improved financial stability.

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Area of Science:

  • Computational finance
  • Network science
  • Auditing analytics

Background:

  • Auditing plays a critical role in financial stability within a dynamic global economy.
  • Assessing the trustworthiness of financial data is paramount for market integrity.
  • Existing methods may not fully capture complex inter-firm relationships.

Purpose of the Study:

  • To develop a novel method for measuring cross-sectoral structural similarities between firms.
  • To leverage microscopic real-world transaction data for auditing insights.
  • To explore the application of network representations and embeddings in computational audit.

Main Methods:

  • Deriving network representations of companies from transaction datasets.
  • Computing network embedding vectors for each company's transaction network.
  • Analyzing over 300 real transaction datasets to identify patterns and similarities.

Main Results:

  • Significant changes in bookkeeping structures and client similarities were detected.
  • High classification accuracy was achieved for various auditing-related tasks.
  • The embedding space effectively clustered similar companies while separating different industries.

Conclusions:

  • The developed network-based approach provides valuable insights for computational auditing.
  • The method accurately captures relevant structural aspects of financial networks.
  • This approach has potential applications beyond individual firms, including country-level risk assessment.