This thesis presents the development of an unsupervised learning model designed to detect anomalies in digital banking account transactions. The project was carried out in collaboration with one of the most prestigious Italian banks, which provided more than 100 million real transactions over one year of data, together with monthly account balances, registry information, and transaction-level metadata. These heterogeneous data sources were integrated, cleaned, and transformed into a structured feature set that enables robust anomaly detection. The feature engineering process produced a total of 98 features extracted on a monthly basis for each account. These features range from straightforward metrics, such as the number of credited and debited transactions or the overall transaction volume, to more sophisticated indicators.