Responsibilities
AI Strategy & Value Realization
- AI Transformation Roadmap: Define and execute the enterprise AI strategy, prioritizing use cases that maximize ROI across the insurance value chain.
- GenAI & LLM Integration: Pilot and scale generative AI applications, such as conversational assistants for customer service or automated medical report summarization.
- Business Case Formulations: Build financial models to justify AI investments, tracking key metrics like cost reduction and premium growth.
Operational Automation & Intelligence
- Intelligent Claims Processing: Deploy Computer Vision and NLP models to analyze damage photos, read invoices, and enable straight-through processing (STP) for claims.
- Predictive Underwriting: Integrate machine learning risk models to automate policy decisions, analyze external data, and optimize premium pricing.
- Fraud Prevention Systems: Oversee the implementation of advanced anomaly detection algorithms to flag fraudulent applications and claims in real time.
Product Delivery & MLOps Architecture
- Model Lifecycle Management: Manage the full lifecycle of AI digital solutions from data engineering and training to production deployment.
- Agile Squad Leadership: Direct cross-functional delivery teams comprising data scientists, machine learning engineers, cloud architects, and UX designers.
- Legacy System Integration: Ensure AI engines connect seamlessly with core policy administration infrastructure through robust API layers.
Governance, Risk & Ethics
- Responsible AI Frameworks: Establish guidelines to ensure AI decision transparency, explainability, and the elimination of algorithmic bias.
- Regulatory Compliance: Align all AI models and data usage with global data privacy regulations and evolving AI governance laws.
- Model Risk Management: Partner with risk teams to continuously monitor model drift, performance degradation, and data security.
Distribution & Experience Enhancement
- Hyper-Personalization: Leverage predictive analytics to empower agents with next-best-action recommendations and personalized cross-selling tools.
- KPI Performance Tracking: Monitor operational metrics including model accuracy, processing speed improvements, and customer satisfaction (NPS).
Requirements
- Bachelor's degree holder
- A minimum of 8 years of relevant experience gained from large insurance companies or financial institutions
- Solid AI project experience
- Good command of Cantonese and English
