PROJECTS
Project | 01
Project | 01 Title: Discovering dynamic adverse behavior of users
Adverse selection (AS) is one of the significant causes of market failure worldwide. Analysis and deep insights into the Australian life insurance market show the existence of adverse activities to gain financial benefits, resulting in loss to insurance companies. Understanding the behavior of policyholders is essential to improve business strategies and overcome fraudulent claims. However, policyholders' behavior analysis is a complex process, usually involving several factors depending on their preferences and the nature of data such as data which is missing useful private information, the presence of asymmetric information of policyholders, the existence of anomalous information at the cell level rather than the data instance level and a lack of quantitative research. This project aims to analyze the life insurance policyholder's behavior to identify adverse behavior (AB).
Source: Islam, Md Rafiqul, Shaowu Liu, Rhys Biddle, Imran Razzak, Xianzhi Wang, Peter Tilocca, and Guandong Xu. "Discovering dynamic adverse behavior of policyholders in the life insurance industry." Technological Forecasting and Social Change 163 (2020): 120486.
Project | 02
Project | 02 Title: Natural Language Interaction Aided with Data Visualization for Exploring Claim and Risk Management
Analysis of claims and risk management is the key task to avoid frauds and to provide risk management in the life insurance industry. Though visualization plays a fundamental role in supporting analysis tasks of the business domain, analyzing user behaviour remains a challenging task. The prevalence of natural language interactions aided with data visualization has become quite the norm. With the increasing demand of visualization tools and varying level of user expertise, it comes as no surprise the use of natural languages interface. However, the design of visual analytics tools aided with natural language interfaces (NLIs) for risk management and claim analysis requires thorough task analysis and domain expertise. In this project, our aims to design an alternative approach through a natural language interaction based interactive representation such as chart, pie, and histogram, which can be applied for investigating insurance claims and risk management.
Project | 03
Project | 02 Title: Adaptive Deep Learning Underwriting Quality Assurance Modelling
The goal of this project is to provide a deep learning-based underwriting quality assurance model with long and short-term memory states. The model is expected to adapt to various external factors that influence the way underwriting evolves over time as well as should be able to depict and respond to the macro factors influence any changes in the underwriting policies and procedure. The deep learning-based model is expected to give higher weightage to recent events compared to lower weightage to older events, thus reducing underwriting workload and claim risk. This model may also be trained through reinforcement learning, for better results of solving exclusions, loading & policy outcome decisions based on large and complex disclosures.