Fraud prediction, compromised account detection, and attrition signaling are vital problems in the financial domain. Generally, these tasks are temporal classification problems as labels exhibit temporal dependence. The labels of these tasks change with time. Each financial transaction contains heterogeneous data like account number, merchant, amount, decline status, etc. A financial dataset contains chronological transactions. This data possesses three distinct characteristics: heterogeneity, relational structure, and temporal nature. Previous efforts fall short of modeling all these characteristics in a unified way. Gradient-boosted decision trees (GBDTs) are used to tackle heterogeneity. Graph Neural Networks (GNNs) are employed to model relational information. Temporal GNNs account for temporal dependencies in the data. In this paper, we propose a novel unified framework, TBoost, which combines GBDTs and temporal GNNs to jointly model the heterogeneous, relational, and temporal characteristics of the data. It leverages both node and edge-level dynamics to solve temporal classification problems. To validate the effectiveness of TBoost, we conduct extensive experiments, demonstrating its superiority in handling the complexities of financial data.