In the evolving landscape of data analytics and management, insurance companies can leverage graph databases like Memgraph to enhance fraud detection and other analytics tasks by transitioning from traditional relational databases. Memgraph enables the transformation of tabular data into graph form, allowing for more efficient data management and faster queries using graph algorithms. To facilitate this transition, the open-source Python library GQLAlchemy serves as a bridge between graph database objects and Python objects, supporting the import of data from various formats such as CSV, Parquet, and ORC. This process is simplified through the creation of configuration files that define how tabular data should be transformed into graph nodes and relationships, eliminating the need for complex Cypher commands. By employing GQLAlchemy, developers can more easily manage data import, allowing them to focus on leveraging graph technology and machine learning for advanced tasks like fraud detection, as illustrated in a Jupyter demo using a mock insurance dataset.