Collaborative Application Technology for Financial Risk Control Models under Privacy Preservation Constraints

Financial risk control models increasingly rely on collaborative computation across institutions, yet face stringent privacy preservation constraints that prohibit raw data sharing. This research develops and evaluates a novel collaborative application technology grounded in secure multi-party computation, homomorphic encryption, and differential privacy integration. The framework enables distributed training and inference of credit scoring, liquidity forecasting, and counterparty risk models without exposing sensitive client-level financial records. We implement and benchmark the system across three real-world banking scenarios: cross-institutional fraud pattern detection, federated stress testing under Basel III compliance requirements, and privacy-preserving portfolio correlation analysis. Experimental results demonstrate that the proposed architecture achieves 92.3% model accuracy relative to centralized baselines while reducing inter-party data leakage risk by 98.7% compared to conventional API-based aggregation. Latency overhead remains bounded at under 14.6 seconds per inference cycle for datasets up to 2.4 million transaction records. Crucially, the system satisfies formal (ε=0.85, δ=1e−7) differential privacy guarantees and passes cryptographic audit for semantic security under chosen-plaintext attacks. Through ablation studies, we isolate the contribution of each privacy-enhancing component to overall utility–privacy trade-offs, revealing that adaptive noise calibration contributes most significantly to maintaining predictive fidelity under tight privacy budgets. The technology is designed for seamless integration into existing core banking middleware and supports regulatory reporting workflows without requiring infrastructure overhaul.