AI-Driven Loss Prevention: A Comprehensive Field-Level Risk Control System
The paper introduces an AI‑driven loss‑prevention platform that augments manual risk analysis with automated field recognition to map database and code models, generate loss‑related methods and interfaces, and deliver pre‑emptive avoidance, real‑time detection, and post‑incident response, achieving over 1,200% growth in identified loss methods and near‑full field coverage.
This paper presents a comprehensive AI-driven loss prevention system that addresses the critical challenge of financial risk management in business operations. The system focuses on three core aspects: pre-emptive risk avoidance, real-time detection, and post-incident emergency response.
The traditional manual approach to identifying loss scenarios relies heavily on individual experience and business knowledge, leading to potential gaps in coverage. To address this limitation, the proposed system enhances manual analysis with automated AI capabilities to identify and mark potential loss-related fields in database operations.
The methodology involves two primary approaches: manual analysis and AI recognition. Manual analysis examines database fields for consistency risks, billing-related risks (including calculation errors, configuration issues, and logic errors), and marketing risks (such as entitlement configuration, distribution, and redemption issues). AI recognition analyzes database write operations to identify fields with potential loss risks, creating a comprehensive mapping between database fields and code model fields.
The system generates loss-related methods and interfaces through precise testing platform capabilities, enabling automated coverage of loss scenarios. This creates a closed-loop system from field identification to method derivation, interface analysis, scenario development, deployment, and rehearsal exercises.
Implementation results demonstrate significant improvements: 1200%+ increase in identified loss methods, 400%+ increase in loss interfaces, and near 100% coverage of loss fields. The system has successfully identified multiple loss-related defects across iterations, proving its effectiveness in reducing financial risks.
Future plans include developing a dual-coverage model combining field rule coverage and automated interface coverage, implementing quality metrics for loss scenarios, and creating a company-wide loss scenario map for comprehensive risk management.
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