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"Revolutionizing Fintech: The Power of Generative AI"

Generative AI could be used in Fintech for many use cases.


Let use understand how Fraud deduction could be detected using Generative AI




Generative AI for fraud detection works by learning the underlying patterns of normal behavior from a dataset and then generating synthetic data points that deviate from these patterns. Steps for fraud detection are provided below.


1.Data Collection and Preprocessing: Involves collecting historical data containing examples of both normal and fraudulent transactions. This dataset is then preprocessed to extract relevant features and normalize the data.


2.Training the Generative Model: Generative model is trained on the preprocessed dataset. During training, the model learns the probability distribution of the normal data and tries to generate synthetic data points that closely resemble the normal data distribution.


3.Anomaly Detection: Once the generative model is trained, it can be used to generate synthetic data points. These synthetic data points are compared to the real data points, and any significant deviations from the normal data distribution are flagged as anomalies. These anomalies are then considered potential instances of fraud.


4.Evaluation and Refinement: The performance of the generative model for fraud detection is evaluated using metrics such as precision, recall, and F1-score. The model may be refined by fine-tuning its parameters or training it on additional data to improve its accuracy and reliability.


5.Deployment and Monitoring: Once the generative model demonstrates satisfactory performance, it can be deployed in a production environment for real-time fraud detection. The model continuously monitors incoming transactions and flags any suspicious activities for further investigation by fraud analysts

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