This solution is a QIO (quantum Inspired Optimization) based deep learning approach to learn and understand the patterns in transactional data. It aims at learning the normal behavior patterns of the transactions during the training process using a generative pattern recognition algorithm. Once trained, the model can identify abnormal patterns of transactions, thereby classifying them as anomalous.

Anomaly Detection using Azure Quantum QIO can be used to identify transactions that are spurious given the transaction pattern of the customer. Identified spurious transactions can be flagged to the customer or blocked. This solution can be used by Banks, Credit Card Issuers, etc.

This solution uses a semi-supervised approach-based generative deep learning model to learn normal transaction patterns using non-fraudulent data and then builds a 1-rule threshold model using data from both classes to identify the anomalous transactions using the inclusion-exclusion principle. The solution also allows for re-training to capture information drift.

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