basel-ay/Detecting-Anomalies-in-Financial-Transactions
Building and training an Autoencoder Neural Network (AENN) model for detecting anomalies in financial transactions.
This project helps financial professionals identify unusual patterns in financial transactions, which can indicate fraud or errors. It takes raw financial transaction data, such as general ledger entries and posting amounts, and pinpoints specific entries that deviate from normal behavior. This tool is designed for auditors, compliance officers, and financial analysts who need to review large volumes of transactions for anomalies.
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Use this if you need to quickly flag financial transactions that are statistically unusual, either individually or in combination, to prioritize for further investigation.
Not ideal if you need a complete fraud examination strategy or a comprehensive forensic data analysis tool, as this focuses specifically on anomaly detection as a starting point.
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Last pushed
Nov 21, 2023
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