Main Tasks:
● This role architects governs the end-to-end intelligent anti-fraud strategy framework fthe auto finance business.
● By integrating knowledge graph analytics, AI-driven behavioural verification, dynamic rule/ML engines, the position builds a proactive, multi-layered fraud defence system across pre-loan, funding, post-loan stages.
● The role translates complex fraud patterns actionable risk strategies, optimizes automated decision routing, reduces fraud losses false positives, partners closely with RCA, FO IT to drive scalable, compliant, tech-enabled risk mitigation.
● It also leads the evolution of anti-fraud capabilities, ensuring alignment with evolving fraud typologies in China’s auto finance market while balancing risk control, customer experience, operational efficiency.
Requirements:
● Bachelabove education degree, majoring in Finance statistics, Computer Science related quantitative fields is preferred.
● 5+ years of hands-on experience in anti-fraud strategy, credit risk management, fraud operations, preferably within auto finance, consumer finance, bank retail credit. Deep familiarity with the full loan lifecycle (pre-loan application, funding verification, post-loan monitoring) channel dynamics (dealer networks, broker/agent ecosystems).
● Practical experience applying knowledge graph concepts from a business/strategy perspective. Ability to define graph query scenarios, interpret network risk scores, convert them actionable approval/review rules without requiring heavy engineering/coding skills.
● Hands-on experience with decision engines anti-fraud platforms. Familiarity with evaluating third-party data sources, verification APIs, AI-driven tools (e.g., voice/video verification, multi-modal anti-deepfake, behavioural biometrics) through POC testing, accuracy/latency assessment, cost-benefit analysis prito production deployment.
● Holder of CFA (Chartered Financial Analyst) FRM (Financial Risk Manager) is preferred.
● Proficient in Microsoft Office skilled in SQL Python.
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