Transfer Learning for Financial Risk Control: Theory, Methods, and Empirical Evaluation

Transfer learning leverages similarities between data and models across domains to enable knowledge transfer, and its integration with deep learning greatly expands traditional capabilities, offering new possibilities for financial risk control.

In financial risk control, obtaining labeled samples can take months, unlike fast‑moving internet domains, and traditional logistic‑regression scoring models are being replaced by more advanced machine‑learning approaches. However, limited and non‑i.i.d. samples remain a major modeling pain point.

Current mainstream financial risk‑control transfer‑learning methods still rely on traditional machine‑learning techniques such as boosting trees trained on large historical datasets and then fine‑tuned on recent, smaller samples, or using a model trained on one dataset as a feature for another, leading to multi‑stage modeling and management overhead.

Deep learning’s modular, end‑to‑end nature naturally fits risk‑control modeling, and its ability to learn embeddings and fuse multimodal features overcomes the limitations of classic feature‑distribution‑based transfer methods, making deep transfer learning highly valuable for finance.

The article first presents the basic theory of transfer learning, formalizing the problem with source and target domains (Ds and Dt) and three possible divergences: different feature spaces, different label spaces, or different probability distributions. It introduces the structural risk minimization principle, adds a regularization term R(f), and derives three transfer‑learning strategies: sample‑weight transfer (e.g., Tradaboost), feature‑transformation transfer, and pretrained‑model transfer.

Two concrete solutions are explored for financial risk control:

Multi‑task learning : jointly modeling long‑term performance, short‑term performance, and transaction intent using an MMOE/PLE framework with shared feature space and task‑specific expert layers. Dynamic weighting (uncertainty weighting and GradNorm) balances task losses, improving convergence and overall performance.

Domain adaptation : aligning pre‑loan (customer‑level) and in‑loan (event‑level) data via feature transformation T, measured by MMD or adversarial GAN discriminators, using either semi‑supervised (source‑only) or supervised (source + target) training.

Experimental results show that multi‑task learning outperforms single‑task models, reduces feature redundancy, and enables better risk‑trade‑off by adjusting approval rates based on transaction intent. Domain adaptation effectively merges disparate sample distributions, improving model generalization in cold‑start and small‑sample scenarios.

In conclusion, a deep understanding of business needs combined with appropriate transfer‑learning techniques—multi‑task learning and domain adaptation—significantly enhances financial risk‑control models, and future work may integrate reinforcement learning, Monte‑Carlo simulations, and further quantization methods.