Customer Transaction Analysis and Fraud Detection System using SQL (Python)

OUR STORY

Anomalous Transaction Patterns

I worked with large datasets from various sources (customer transactions, account activity (logs, etc.) and used SQL to clean, normalize, and join data from multiple tables.

Example of first-degree normalization: 1NF

In a previous project as a data manager, I spearheaded an initiative focused on enhancing transaction monitoring and fraud detection for a financial institution.

Utilizing SQL, the project aimed to pinpoint suspicious or anomalous transaction patterns that could suggest fraudulent activity.

A comprehensive Python data frame was created to incorporate customer transactions, enabling us to analyze trends and outliers effectively.

The initial steps involved cleaning and organizing the data, followed by crafting SQL queries to extract relevant transaction information.

By employing advanced analytics techniques, we were able to generate insights that significantly improved the institution's ability to detect and prevent fraud, ultimately safeguarding both the organization and its customers from potential financial losses. The success of this project underscored the critical role of data management in the financial sector.

Large Python dataframe including customer transactions.

First steps were the following:

Data Integration and Cleaning:

The analysis of several accounts had to be 1NF normalized to split the analysis of their possible different accounts.

Queries to remove duplicates, correct data inconsistencies, and create a unified dataset for analysis

Transaction Monitoring and Pattern Analysis:

- I developed SQL queries to analyze customer transaction histories, identifying patterns such as unusual transaction volumes, high-frequency transactions, or foreign transfers.

- Used aggregation functions (e.g., `SUM()`, `COUNT()`) to track spending behaviors by account, customer demographic, or time of day.