Dr. Lu Xiong

Departments / Programs

Degree Information

PHD, Middle Tennessee State University (2014)
MS, Middle Tennessee State University (2014)
BS, Beijing University of Posts and Telecommunications (2010)

Areas of Expertise

My research focuses on Computational Actuarial Science—an interdisciplinary field that leverages advanced computational methods to solve practical issues in actuarial science and closely related areas like finance and healthcare risk management. This focus has roots in my diverse academic and professional background, which spans actuarial science, computational science, bioinformatics, and corporate financial analysis. Here’s how some of my key research areas align with this overa...

My research focuses on Computational Actuarial Science—an interdisciplinary field that leverages advanced computational methods to solve practical issues in actuarial science and closely related areas like finance and healthcare risk management. This focus has roots in my diverse academic and professional background, which spans actuarial science, computational science, bioinformatics, and corporate financial analysis. Here’s how some of my key research areas align with this overarching theme:

Enhancing Regulatory Systems

My work in improving the Insurance Regulatory Information System (IRIS) incorporates computational simulations and machine learning algorithms. Originating from my consultancy experience, I've developed probabilistic models for assessing insurer solvency, thus introducing stochastic elements into what has traditionally been a deterministic scoring system. This research is particularly relevant for regulators and policyholders who need more nuanced metrics for financial stability.

Data Science in Actuarial Science and Risk Management Applications

I have employed machine learning and predictive modeling in various sectors, including healthcare and credit risk management. For example, my work on "Predictive Analytics for 30-day Hospital Readmissions" utilized generalized linear models to not only predict but also understand the factors that influence hospital readmissions. This research provides actionable insights for healthcare institutions to improve patient outcomes.

Advanced Proxy Modeling for Financial Products

Proxy modeling techniques are widely used for pricing variable annuities—a complex but essential financial product. My research aims to accelerate the computational process involved, using distributed computing. By optimizing algorithms like the Least Squares Monte Carlo simulation, my work contributes to more accurate and faster pricing, thus benefitting the insurance and finance industries.

Health Data Systems Development

I am involved in the development and integration of health data systems based on Health Level Seven (HL7) standards. This is crucial for enhancing the seamless sharing of healthcare information and elevating the patient care.

Natural Language Processing (NLP) for Actuarial Science

I apply NLP techniques to decode and analyze vast amounts of unstructured data in actuarial science, unlocking new avenues for risk assessment and pricing models.

Corporate Financial Analysis

This work fits well with my work in actuarial risk assessment. Focusing on Chinese reverse mergers, I and co-authors explore how performance commitments influence analysts' earnings forecasts and thus affect market information. This work aims to reduce forecast risk, a vital element in corporate finance, insurance, and investment decisions, thereby providing a more holistic understanding of risk management.

Actuarial Software Development

My strong interest in software development led to the creation of AutoReserve, an open-source, web-based actuarial tool that streamlines various tasks for general insurance actuaries. This work contributes to democratizing advanced actuarial tools and can also serves as an educational resource for actuarial students.

My research aims to enhance risk management by integrating computational techniques with actuarial science. This approach not only helps academia but also benefits industries and society, while equipping future actuaries for a data-driven world.

« Read Less

Biography

Dr. Xiong is an assistant professor in the actuarial science program at Middle Tennessee State University (MTSU). He completed his ASA in 2021. He is also a faculty member in the Ph.D. program of Computational and Data Science at MTSU. Dr. Xiong has taught a broad array of actuarial courses, ranging from foundational to advanced exam preparation classes such as Exam-P, FM, LTAM, STAM, IFM, and PA. He also oversees undergraduate research projects in the field of actuarial science and acts as t...

Dr. Xiong is an assistant professor in the actuarial science program at Middle Tennessee State University (MTSU). He completed his ASA in 2021. He is also a faculty member in the Ph.D. program of Computational and Data Science at MTSU. Dr. Xiong has taught a broad array of actuarial courses, ranging from foundational to advanced exam preparation classes such as Exam-P, FM, LTAM, STAM, IFM, and PA. He also oversees undergraduate research projects in the field of actuarial science and acts as the faculty advisor for the Actuarial and Math Students Association (AMSA).

Before joining MTSU, Dr. Xiong gained valuable industry experience. He worked for two years in actuarial consulting in Nashville, focusing on general insurance. He also spent a year in quantitative investment as an entrepreneur and completed a one-year postdoctoral research stint in healthcare at Emory University School of Medicine.

When it comes to his teaching approach, Dr. Xiong integrates his industry insights to help students understand what employers are looking for and how best to gear up for a career in actuarial science. In the realm of research, he specializes in applying advanced computational methods and machine learning algorithms to resolve complex issues in actuarial science.

« Read Less

Publications

Xiong, L., Luo, J.; Vise, H.; White, M. (2023). Distributed Least-Squares Monte Carlo for American Option Pricing. Risks, 11(8), 145.

Xiong, L., Luo, J.; Vise, H.; White, M. (2023). Distributed Least-Squares Monte Carlo for American Option Pricing. Risks, 11(8), 145. https://doi.org/10.3390/risks11080145
Xiong, L., Manathunga, V., Luo, J., Dennison, N., Zhang, R., Xiang, Z. (2023). AutoReserve: a web-based tool for personal auto insurance loss reserving with classical and machine learning methods. Risks, 11(7), 131. https://doi.org/10.3390/risks11070131
Liu, Y., Yang, L., Xiong, L., Performance Commitments and the Properties of Analyst Earnings Forecasts: Evidence from Chinese Reverse Merger Firms. International Review of Financial Analysis. https://authors.elsevier.com/c/1hOl-3mS~2a18U
Xiong, L., Tian, K., Qian, Y., Musyoka, W., Chen, X. (2023). Determine the Undervalued US Major League Baseball Players with Machine Learning. International Journal of Innovative Technology and Exploring Engineering. https://www.ijitee.org/portfolio-item/B94060112223/
Xiong, L., Hong, D. (2022). CapSolve: A Solvency Assessment and Prediction Framework for Workers’ Compensation Captive Insurance Companies. Journal of Insurance Issues, 45(2), 82-113. https://www.jstor.org/stable/48703228
Xiong, L., Williams, S. D. (2022). Generalized Linear Model for Predicting the Credit Card Default Payment Risk. Advances in Science, Technology and Engineering Systems Journal (Special Issue on Innovation in Computing, Engineering Science & Technology). https://doi.org/10.25046/aj070306
Xiong, L. (2022). Predictive Modeling for Transportation Security Administration Claims Data. ANWESH: International Journal of Management & Information Technology, 7(2), 10-20. http://www.publishingindia.com/anwesh/106/predictive-modelling-for-transportation-security-administration-claims-data/32006/76746/
Xiong, L., Sun, T., Green, R. (2021). Predictive Analytics for 30-day Hospital Readmissions. Mathematical Foundations of Computing, Online First. https://www.aimsciences.org/article/doi/10.3934/mfc.2021035
Xiong, L., Hong, D. (2014). Multi-resolution Analysis Method for IMS Data Biomarker Selection and Classification. British Journal of Mathematics and Computer Science, 5(1), 65-81. https://doi.org/10.9734/BJMCS/2015/9870
Xiong, L. (2020). Comparative Study of Predictive Analytics Algorithms and Tools on Property and Casualty Insurance Solvency Prediction (pp. 81-88). Association for Computing Machinery (ACM). https://doi.org/10.1145/3418653.3418663
Xiong, L., Hong, D. (2020). Using Monte Carlo Simulation to Predict Captive Insurance Solvency (pp. 84- 88). Association for Computing Machinery (ACM). https://doi.org/10.1145/3388142.3388171
Xiong, L., Hong, D. (2017). An MCMC-MRF Algorithm for Incorporating Spatial Information in IMS Proteomic Data Processing. Statistical Analysis of Proteomics, Metabolomics, and Lipidomics Data Using Mass Spectrometry. Murfreesboro: Springer International Publishing. https://www.springerprofessional.de/en/an-mcmc-mrf-algorithm-for-incorporating-spatial-information-in-i/11933362
Xiong, L., Liang, J., Chen, X., Cao, X., Zhu, P., Zhao, M. (2023). Tree-based Machine Learning Methods for Analytics of Online Shoppers’ Purchasing Intentions. International Journal of Data Science.
Xiong, L. (2014). Statistical Computing Schemes for Proteomics Data Processing and Insurance Solvency Modeling. http://jewlscholar.mtsu.edu/handle/mtsu/4331