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Abstract:

We'll explore how auditors can be misguided or fooled by adversarial accounting records or adversarial financial transactions. Recent discoveries in deep learning research revealed that learned models are vulnerable to adversarial examples, or a sample of slightly modified input data that intends to cause a human and/or machine to misclassify it. Such examples exhibit the potential to be dangerous because they could be specifically designed to misguide auditors or an accountant. Securing accounting information systems against such attacks can be difficult.

We'll explore how auditors can be misguided or fooled by adversarial accounting records or adversarial financial transactions. Recent discoveries in deep learning research revealed that learned models are vulnerable to adversarial examples, or a sample of slightly modified input data that intends to cause a human and/or machine to misclassify it. Such examples exhibit the potential to be dangerous because they could be specifically designed to misguide auditors or an accountant. Securing accounting information systems against such attacks can be difficult.

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Topics:
Finance - Deep Learning, Advanced AI Learning Techniques
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9361
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Abstract:

This session will explore how auditors can be misguided or "fooled" by adversarial accounting records or adversarial financial transactions. Recent discoveries in deep learning research revealed that learned models are vulnerable to "adversarial examples," or a sample of slightly modified input data that intends to cause a human and/or machine to misclassify it. Such examples exhibit the potential to be dangerous, since they could be specifically designed to misguide auditors or an accountant. Securing accounting information systems against such "attacks" can be difficult. In this talk, we'll explain why such "adversarial examples" are of vital relevance in the context of fraud detection and financial statement audits. We will demonstrate how autoencoder neural networks can be trained in an adversarial setup to generate "fake" accounting records or financial transactions. Such financial transactions might be misused to "attack" an organization's internal control system or obfuscate fraudulent activities. The training of such examples was conducted by training several adversarial autoencoders using NVIDIA's DGX-1 system.

This session will explore how auditors can be misguided or "fooled" by adversarial accounting records or adversarial financial transactions. Recent discoveries in deep learning research revealed that learned models are vulnerable to "adversarial examples," or a sample of slightly modified input data that intends to cause a human and/or machine to misclassify it. Such examples exhibit the potential to be dangerous, since they could be specifically designed to misguide auditors or an accountant. Securing accounting information systems against such "attacks" can be difficult. In this talk, we'll explain why such "adversarial examples" are of vital relevance in the context of fraud detection and financial statement audits. We will demonstrate how autoencoder neural networks can be trained in an adversarial setup to generate "fake" accounting records or financial transactions. Such financial transactions might be misused to "attack" an organization's internal control system or obfuscate fraudulent activities. The training of such examples was conducted by training several adversarial autoencoders using NVIDIA's DGX-1 system.

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Topics:
Finance
Type:
Talk
Event:
GTC Europe
Year:
2018
Session ID:
E8212
Streaming:
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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.
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.  Back
 
Topics:
Finance, Finance - Quantitative Risk & Derivative Calculations
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8343
Streaming:
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