Cyber risk is a rising concern for organizations in both the public and private sectors. Researchers have recognized cyber insurance as an effective way to improve resilience against cyber incidents because it speeds up the process of recovery from financial losses and helps insured entities swiftly resume daily operations. It emboldens the insured to pursue business activities and innovations while offloading the potentially prohibitive cost of cyber risks to the insurer. It also offers insured financial incentives by means of premium discounts to improve cybersecurity.

However, after many years of development, the cyber insurance market is still in its infancy. Cyber insurance products are less than satisfactory and often criticized for high premiums, low capacity, and obscure policy language. This research project attempts to investigate issues impeding development in cyber insurance and seeks to provide potential solutions in several aspects, including:

• Better data and modeling for cyber risk assessment
• Developing a mechanism to incentivize stakeholders to participate in the market
• Utilizing capital markets to share risks and spur the growth in the insurance market
• Raising awareness of cyber insurance as risk management tools

Despite its critical importance, there has been surprisingly scarce literature on the actuarial methodology for assessing and mitigating cyber risk. This project seeks to fill the gap in the current actuarial literature on cyber risk assessment, data analytics, and market solutions. This project will benefit the broader actuarial and academic communities by developing a rich resource on current practice and innovations on cyber risk.

As our research team possesses expertise in actuarial science, law, and engineering, the coalescence of research interests could lead to boundary-breaking solutions. The goal of this research project is to help actuaries and insurers better understand cyber risk and develop more effective cyber insurance products and risk management strategies.

Climate change failure, extreme weather, biodiversity losses, infectious diseases, human environment damage, geopolitical confrontation are among many of the most severe risks on a global scale identified almost every year on the Global Risks Reports by the World Economic Forum. With the evolution of human societies, natural and anthropogenic disasters are inevitable and increasingly frequent.

Actuarial science plays a critical role in the study of the financial impact of natural disasters and human hazards and risk management solutions. Our research team has done research work in the design, pricing, valuation and management of catastrophe-related financial derivatives and insurance solutions. For example, we have recently worked on mathematical models to analyze the transmission dynamics of infectious diseases and to measure the cost of health care. Taking advantage of epidemic models, we project the dynamics of demand and supply for medical resources at different phases of a pandemic. Such predictions provide quantitative bases for decision makers of healthcare system to understand the potential imbalance of supply and demand, and to address disparities of access to critical medical supply across different subsidiaries and in the course of the pandemic.

The InsurTech industry is on the rise worldwide. A variety of innovative business models have emerged with the potential to disrupt the traditional insurance industry using technologies including blockchain, artificial intelligence (AI), Internet of Things (IoT) and big data. These new tools could resolve major challenges in the current insurance industry and improve efficiency in underwriting, risk pooling and claims management.

Along with this changing landscape of technology, the sharing economy, a socio-economic system built around the sharing of resources, is evolving and expanding in a wide range of industries. In contrast with the traditional server-client model in which a central authority provides services to all, the peer-to-peer nature of the sharing economy enables the exchange of goods and services among users without the heavy cost of intermediaries. The rise of the sharing economy brings a new channel for individuals to share their underutilized assets and receive rewards.

Predictive analytics is a branch of data science that applies various techniques, including statistical inference, machine learning, data mining, and information visualization, toward the ultimate goal of forecasting, modeling, and understanding the future behavior of a system based on historical and/or real-time data. Predictive analytics has only recently seen interest or adoption in risk management, which has the potential to be widely applied to determine business events that are likely to occur and be actionable.

Our research team focuses on data-driven modeling techniques, and topics of particular interest include but are not limited to:

• Statistical learning algorithms for loss modeling, fraud detection, etc.
• AI-Powered lifecycle financial planning
• Cyber risk data analytics
• Automated Machine Learning (AutoML) for imbalanced datasets
• Tree-based models with modified loss functions
• Large-scale parallel computing

The expansion of the digital universe has been one of the most fundamental changes to how we communicate with each other in the modern society.

Social media is now as one of the top sources of business insight used by financial firms to stay competitive. Natural Language Processing (NLP) has the potential to be widely applied in the finance and insurance industry in areas, such as fraud detection, claim management, and legal case analysis. NLP can turn unstructured text data into useful features that can be analyzed by actuaries, quants, and data scientists.

Foot traffic data is captured by various sources, such as smartphone APP, telematics devices in the vehicle, which can help insurance companies monitor policy holders’ behavior. It is beneficial for insurance companies to price the risk accurately and accelerate the underwriting process. On the other hand, policyholders are given incentives for good driving behavior. There are various state-of-art techniques to extract useful information from the foot traffic data, including spatial and temporal analysis, and geospatial analysis.

These state-of-art techniques can unleash Fin/InsurTech innovations and transform the finance and insurance industry. Our research team collaborates with Fin/InsurTech firms and the National Center for Supercomputing Applications to push boundaries and deliver breakthrough solutions for Fin/InsurTech innovations.

Decision-making is an everyday activity. When it comes to insurance and finance, complication raises due to the involvement of risk and uncertainty. To add more complexity, different decision makers have different goals. Specifically, individual insurance buyers look for insurance products to best protect their positions; insurance providers focus on designing products so as to make profit and/or increasing market shares subject to their risk management needs and regulatory requirements. On the other hand, the goal of regulators/policymakers is at the macro level: make policies and implement regulations so as to protect the interest of insurance buyers as a whole and meanwhile maintain the sustainability of the market.

In this research theme, we employ the tool of risk and decision analysis to uncover the science behind decision-making processes from multiple perspectives. With a better understanding of decision-making, we anticipate to establish mechanisms to encourage insurance providers’ design of socially responsible products, to incentivize insurance buyers’ choice on these products, and ultimately to enhance the overall social welfare in various insurance markets.

Insurance markets currently under our investigations include annuity market and health insurance market. In annuity market, we study how policyholders and insurers are incentivized in participating insurance contracts with economic and financial models, and find empirical evidence to support our hypotheses. On the product level, we investigate optimal behavior and consumer surplus from the policyholder’s perspective; we explore product design and pricing strategies from the insurer’s perspective; and we study the effectiveness of current regulations in the market from the policymaker’s perspective. In health insurance market, a long existing issue is moral hazard and overtreatment/ overutilization. While practitioners and researchers are actively exploring strategies to mitigate moral hazard, most studies are qualitative. Through decision analysis, we aim to develop models to quantify moral hazard and recategorize policyholders according to moral hazard index. Based on the recategorization, we further develop moral hazard mitigation strategies with better precision and efficiency.

As insurance industry evolves, dependence starts to emerge. Risks are likely to be dependent particularly in the emerging insurance markets, such as cyber insurance and autonomous vehicle insurance. The existence of dependence brings challenges in insurance risk modeling and risk management.  

A conventional approach to model dependence is through copula. It requires high-dimensional data, which is not always available. We aim to develop and explore general dependence notions beyond copula. Compared to the copula approach, these notions should be more flexible and requires lower resolution data for statistical modeling. Meanwhile, these notions should still provide adequate decision for the purpose of decision-making.

Risk management in traditional insurance business crucially replies on the assumption of independence. When dependence comes into play, new methodologies are needed for pricing and risk management. We aim to design novel premium principles that can accommodate both insurer and insured’s interest in the presence of dependence, and develop risk management strategies accordingly. We shall also explore the potential in combing these pricing and risk management techniques with other approaches, such as peer-to-peer insurance and catastrophe bond design.

Ruin theory is devoted to the quantification and assessment of the likelihood of insolvency for insurance business. A typical approach for ruin analysis is to investigate a stochastic model representing an insurer’s asset and liability structure and to calculate various measures of ruin, which occurs when the insurer’s assets fail to keep up with its liabilities.

Risk aggregation and capital allocation are of paramount importance in the business world, as they play critical roles in pricing, risk management, project financing, performance management, regulatory supervision, etc. We study and propose innovative risk aggregation and capital allocation methods for a variety of areas of practice, including property and casualty, life and annuities, etc.