Detecting Anomalies in Financial Data Using Machine Learning Algorithms

Bookkeeping data free of fraud and errors are a cornerstone of legitimate business operations. The highly complex and laborious work of financial auditors calls for finding new solutions and algorithms to ensure the correctness of financial statements. Both supervised and unsupervised machine learni...

Full description

Saved in:
Bibliographic Details
Published in:Systems (Basel) 2022-10, Vol.10 (5), p.130
Main Authors: Bakumenko, Alexander, Elragal, Ahmed
Format: Article
Language:English
Subjects:
Accounting
Algorithms
Anomalies
anomaly detection
Auditing
Auditors
Audits
Automation
Datasets
Deep learning
Enterprise resource planning
Evaluation
Financial statements
Forensic accounting
Fraud
general ledger
Information systems
Informationssystem
Machine learning
Random sampling
Safety and security measures
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Bookkeeping data free of fraud and errors are a cornerstone of legitimate business operations. The highly complex and laborious work of financial auditors calls for finding new solutions and algorithms to ensure the correctness of financial statements. Both supervised and unsupervised machine learning (ML) techniques nowadays are being successfully applied to detect fraud and anomalies in data. In accounting, it is a long-established problem to detect financial misstatements deemed anomalous in general ledger (GL) data. Currently, widely used techniques such as random sampling and manual assessment of bookkeeping rules become challenging and unreliable due to increasing data volumes and unknown fraudulent patterns. To address the sampling risk and financial audit inefficiency, we applied seven supervised ML techniques inclusive of deep learning and two unsupervised ML techniques such as isolation forest and autoencoders. We trained and evaluated our models on a real-life GL dataset and used data vectorization to resolve journal entry size variability. The evaluation results showed that the best trained supervised and unsupervised models have high potential in detecting predefined anomaly types as well as in efficiently sampling data to discern higher-risk journal entries. Based on our findings, we discussed possible practical implications of the resulting solutions in the accounting and auditing contexts.
ISSN:2079-8954
2079-8954
DOI:10.3390/systems10050130