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Found 12 papers, 5 papers with code
FedTabDiff: Federated Learning of Diffusion Probabilistic Models for Synthetic Mixed-Type Tabular Data Generation
Realistic synthetic tabular data generation encounters significant challenges in preserving privacy, especially when dealing with sensitive information in domains like finance and healthcare.
FinDiff: Diffusion Models for Financial Tabular Data Generation
The sharing of microdata, such as fund holdings and derivative instruments, by regulatory institutions presents a unique challenge due to strict data confidentiality and privacy regulations.
Federated Continual Learning to Detect Accounting Anomalies in Financial Auditing
Our empirical results, using real-world datasets and combined federated continual learning strategies, demonstrate the learned model's ability to detect anomalies in audit settings of data distribution shifts.
RESHAPE: Explaining Accounting Anomalies in Financial Statement Audits by enhancing SHapley Additive exPlanations
However, in unsupervised DL as often applied in financial audits, these methods explain the model output at the level of encoded variables.
Federated and Privacy-Preserving Learning of Accounting Data in Financial Statement Audits
In this work, we propose a Federated Learning framework to train DL models on auditing relevant accounting data of multiple clients.
Continual Learning for Unsupervised Anomaly Detection in Continuous Auditing of Financial Accounting Data
This work proposes a continual anomaly detection framework to overcome both challenges and designed to learn from a stream of journal entry data experiences.
Multi-view Contrastive Self-Supervised Learning of Accounting Data Representations for Downstream Audit Tasks
International audit standards require the direct assessment of a financial statement's underlying accounting transactions, referred to as journal entries.
Leaking Sensitive Financial Accounting Data in Plain Sight using Deep Autoencoder Neural Networks
Nowadays, organizations collect vast quantities of sensitive information in `Enterprise Resource Planning' (ERP) systems, such as accounting relevant transactions, customer master data, or strategic sales price information.
Learning Sampling in Financial Statement Audits using Vector Quantised Autoencoder Neural Networks
The audit of financial statements is designed to collect reasonable assurance that an issued statement is free from material misstatement 'true and fair presentation'.
Adversarial Learning of Deepfakes in Accounting
Second, we show that adversarial autoencoder neural networks are capable of learning a human interpretable model of journal entries that disentangles the entries latent generative factors.
Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks
We demonstrate that such artificial neural networks are capable of learning a semantic meaningful representation of real-world journal entries.
Detection of Anomalies in Large Scale Accounting Data using Deep Autoencoder Networks
Learning to detect fraud in large-scale accounting data is one of the long-standing challenges in financial statement audits or fraud investigations.
