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GTC ON-DEMAND

Finance
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Detection of Financial Statement Fraud using Deep Autoencoder Networks
Abstract:
Explore how auditors are applying deep learning to detect "anomalous" records in large volumes of accounting data. The Association of Certified Fraud Examiners estimates in its Global Fraud Study 2016 that the typical organization loses 5% of its annual revenues due to fraud. At the same time, organizations accelerate the digitization of business processes affecting Enterprise Resource Planning (ERP) systems. These systems collect vast quantities of electronic journal entry data in general- and sub-ledger accounts at an almost atomic level. To conduct fraud, perpetrators need to deviate from regular system usage or posting pattern. This deviation will be weakly recorded and reflected accordingly by a very limited number of "anomalous" journal entries of an organization. To anomalous journal entries, several deep auto-encoder networks are trained using NVIDIA''s DGX-1 system. The empirical evaluation on two real-world accounting datasets underpinned the effectiveness of the trained network in capturing journal entries highly relevant for a detailed audit while outperforming several baseline methods.
 
Topics:
Finance, Finance - Quantitative Risk & Derivative Calculations
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8343
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