This division accommodates actuarial researchers from diverse fields, particularly concentrating on the following areas:
Risk[quantitative risk management, emerging risks, mortality and health risks, catastrophic risks, insurance risk modelling, tail risk measuring]
Insurance[general insurance, life and non-life insurance, reinsurance; insurance pricing, capital and reserving; insurance fraud detection]
AI and InsurTech[AI/ML enhanced actuarial analytics; InsurTech& its impact on insurance]
Investment & Optimization[portfolio selection, optimal Insurance/reinsurance strategies & investment strategy; optimal consumption and investment strategies; optimal dividend and capital injection, stochastic optimal control in insurance and finance]
The division has also made substantial advancements in other related fields, including quantitative finance, probability, statistics, operations research, and data analytics.
Dr Jiajun Liu (Quantitative risk management, Emerging risks modelling, Extremes for insurance and finance, Capital allocation, Risk sharing)
Dr Ran Xu (Risk and Ruin Theory, Stochastic Optimal Control in Insurance and Finance, Applied Stochastic Processes, Machine Learning Techniques in Actuarial Science)
Dr Xin Xu (Levy processes, Optimal stopping, Risk Management)
Professor Hailiang Yang (Applications of deep learning in actuarial science, optimal insurance strategies, equity-linked insurance products, insurance risk models, mathematical finance.)
Dr Zhehao Zhang (Investment strategy, Probability and statistics in insurance and finance, Insurtech)
The division concentrates its research topics on both the fundamental aspects of AI models and optimisation algorithms, as well as their applications across diverse sectors, including financial markets, industrial control, and healthcare. The fundamental research primarily emphasises the integration of structural and data-driven models, with particular focus on embedding domain-specific knowledge into algorithms for neural networks, Gaussian processes, and other computational frameworks. This area of study naturally leads to theoretical investigations into the interpretability of data-driven models. Concurrently, the applied research theme is dedicated to developing sophisticated algorithms for predictive modelling, risk management, and fraud detection (mostly) in financial contexts. The industrial control application focus on the process optimisation, predictive maintenance, and automation. The healthcare focus involves creating advanced diagnostic tools, devising personalised medicine algorithms, and enhancing healthcare management systems. Furthermore, the division aims to explore AI applications in environmental science, education, transportation, retail, and carbon neutralisation, with the overarching goal of fostering innovation and addressing global challenges.
Financial Mathematics (FM) is the field of applied mathematics at SMP that involves providing solutions to practical problems in financial industry, using those methods from probability measures, statistics, stochastic processes, differential equations, optimization, numerical methods, machine learning and data science and etc. The primary emphasis in financial mathematics is the development of mathematical models that underpin the intuition from financial markets.
Research themes of the FM division cover a wide spectrum of topics in mathematical and quantitative finance, ranging from high-frequency modeling within micro-market structure to macro-financial modeling and systemic risk, including:
Asset Pricing Models and Theories
Asset Allocation and Portfolio Selection
Quantitative Trading and Algorithmic Finance
Financial Risk Management
Financial Engineering and Computational Finance
Statistical Modeling and Machine Learning in Finance
The division of probability and statistics brings together faculties in probability theory and statistics, becoming an active and coherent research community. The main research theme is about the theoretical study in probability and statistics. Due to their profound and wide applications, our members also applied their research results in Machine Learning, Financial Math, Actuarial Science, Quality Control and Medical Study.
Division of Actuarial Science
This division accommodates actuarial researchers from diverse fields, particularly concentrating on the following areas:
The division has also made substantial advancements in other related fields, including quantitative finance, probability, statistics, operations research, and data analytics.
Division of AI and Data Science
The division concentrates its research topics on both the fundamental aspects of AI models and optimisation algorithms, as well as their applications across diverse sectors, including financial markets, industrial control, and healthcare. The fundamental research primarily emphasises the integration of structural and data-driven models, with particular focus on embedding domain-specific knowledge into algorithms for neural networks, Gaussian processes, and other computational frameworks. This area of study naturally leads to theoretical investigations into the interpretability of data-driven models. Concurrently, the applied research theme is dedicated to developing sophisticated algorithms for predictive modelling, risk management, and fraud detection (mostly) in financial contexts. The industrial control application focus on the process optimisation, predictive maintenance, and automation. The healthcare focus involves creating advanced diagnostic tools, devising personalised medicine algorithms, and enhancing healthcare management systems. Furthermore, the division aims to explore AI applications in environmental science, education, transportation, retail, and carbon neutralisation, with the overarching goal of fostering innovation and addressing global challenges.
Division of Financial Mathematics
Financial Mathematics (FM) is the field of applied mathematics at SMP that involves providing solutions to practical problems in financial industry, using those methods from probability measures, statistics, stochastic processes, differential equations, optimization, numerical methods, machine learning and data science and etc. The primary emphasis in financial mathematics is the development of mathematical models that underpin the intuition from financial markets.
Research themes of the FM division cover a wide spectrum of topics in mathematical and quantitative finance, ranging from high-frequency modeling within micro-market structure to macro-financial modeling and systemic risk, including:
Division of Probability and Statistics
The division of probability and statistics brings together faculties in probability theory and statistics, becoming an active and coherent research community. The main research theme is about the theoretical study in probability and statistics. Due to their profound and wide applications, our members also applied their research results in Machine Learning, Financial Math, Actuarial Science, Quality Control and Medical Study.