Applying Deep Learning to Time Series Data for Financial Risk Modeling

Business background: expanding company accumulates massive time series data across domains such as device event logs, credit bureau records, and customer service interactions, which are valuable for risk assessment but difficult to process manually.

Problems with manual feature engineering: low efficiency, sparse features, dimensional explosion, and limited model improvement.

Solution: adopt deep learning sequence models (embedding, attention, transformer) to automatically extract rich representations from raw time‑series data, improving AUC and KS metrics in credit scoring and fraud detection.

Model performance: stacking models that combine baseline handcrafted features with deep‑learned time‑series scores achieve noticeable gains; engineering results show high reusability, integration, and automation.

System framework: a two‑stage pipeline with configurable preprocessing that converts raw tables into PyTorch tensors, followed by model training, evaluation, and deployment; the same package can be used across different business scenarios without code changes.

Algorithm details: data preprocessing includes categorical embedding, numeric normalization, and time‑interval extraction; field aggregation compresses per‑field tensors via attention, and item aggregation applies a transformer encoder to produce sequence vectors, finally fed to a fully‑connected output layer.

Experience and future work: lightweight models (e.g., emb_dim=8, nhead=2, 1 transformer layer) work well for risk control; future directions include nested and heterogeneous sequences and multimodal inputs such as audio or images.