Detection of Anomalies in Large Scale Accounting Data using Deep Autoencoder Neural Networks
Type
conference paper
Date Issued
2018-08-01
Author(s)
Editor(s)
Research Team
AIML Lab
Abstract
Learning to detect fraud in large-scale accounting data is one of the long-standing challenges in financial statement audits or fraud investigations. Nowadays, the majority of applied techniques refer to handcrafted rules derived from known fraud scenarios. While fairly successful, these rules exhibit the drawback that they often fail to generalize beyond known fraud scenarios and fraudsters gradually find ways to circumvent them. To overcome this disadvantage and inspired by the recent success of deep learning, we propose the application of deep autoencoder neural networks to detect anomalous journal entries. We demonstrate that the trained network's reconstruction error obtainable for a journal entry and regularized by the entry's individual attribute probabilities can be interpreted as a highly adaptive anomaly assessment. Experiments on two real-world datasets of journal entries show the effectiveness of the approach resulting in high f1-scores of 32.93 (dataset A) and 16.95 (dataset B) and less false positive alerts compared to state of the art baseline methods. Initial feedback received by chartered accountants and fraud examiners underpinned the quality of the approach in capturing highly relevant accounting anomalies.
Language
English
Keywords
deep learning
machine learning
artificial intelligence
accounting
HSG Classification
contribution to scientific community
Refereed
Yes
Publisher
Cornell University - arXiv
Start page
1
End page
19
Pages
19
Event Title
GPU Technology Conference - Silicon Valley
Event Location
San Jose, CA, USA
Event Date
March 26th - 29th, 2018
Subject(s)
Division(s)
Contact Email Address
marco.schreyer@unisg.ch
Additional Information
Code available at: https://github.com/GitiHubi/deepAI
Eprints ID
258111
File(s)![Thumbnail Image]()
Name
1709.05254.pdf
Size
1.99 MB
Format
Adobe PDF
Checksum (MD5)
1ee0608435421c7e5df23552ffacaf99