Graph-related applications, including classification, regression, and clustering, have seen significant advancements with the development of graph neural networks (GNNs). However, a gap remains in effectively using these models for heterogeneous graphs, as current methods primarily focus on homogeneous graphs, often overlooking potentially valuable semantic information. To address this issue, our work introduces a novel approach, G-HIN2VEC (Graph-Level Heterogeneous Information Network to Vector), specifically designed to generate heterogeneous graph representations. This method uniquely leverages a single graph to learn its own embeddings without relying on a graph dataset, by sharing model parameters across the dataset. Inspired by recent developments in unsupervised learning in natural language processing, G-HIN2VEC employs a negative sampling technique to learn graph-level embedding matrices from a variety of metapaths. This approach has been applied to real-world credit card data, facilitating the analysis of cardholder transactions through three downstream applications-graph-level regression and classification tasks, including age and income prediction, and gender classification. G-HIN2VEC outperforms traditional methods, demonstrating improvements in gender classification accuracy by 2. 45% and income prediction R-squared (R2) by 7. 19%. Furthermore, for age prediction, we achieved an increase of 6. 55% in the mean absolute error (MAE) compared to DiffPool, a strong baseline.