Spring 2022  Projects

Fall 2021 Projects

 
 
 
Computer Programming

Automated Machine Learning (AutoML) Project

Description:

Machine Learning (ML) techniques have been applied broadly in the field of actuarial science and achieve fruitful results. However, to solve a practical problem with ML, the agent needs to preprocess the dataset, choose the proper ML tools, and tweak the hyperparameters of the model. All these steps are task-specific and require expert knowledge, which may render less effective ML model performance when operated by a non-ML-expert agent. 

 

To this end, AutoML is developed to facilitate the usage of ML by non-expert agents and aims to integrate the automated data processing, model selection, and model tuning into one system. This project continues on the development of an existing AutoML pipeline that adapts and modifies the AutoML packages (for example, AUTO-SKLEARN) to fit better for the actuarial ML problems. 

We will perform the following tasks:

 

  • Literature review on the existing AutoML system developed from last semester

  • Understand and modify the existing AutoML codebase

  • Wrap up the results in a scientific report

Supervisors: Zhiyu (Frank) Quan, Xiaochen Jing 

Graduate Supervisor: Yuxuan Li 

Market Analysis

AXIS Systemic Cyber Threats Project

Description:

AXIS Capital is one of the major insurance carriers that provide cyber policies. As the world is becoming more connected than ever, systemic cyber threats pose a great risk to businesses that rely on information technology, as well as to insurers like AXIS, who provide financial protections to those businesses. As an example of systemic cyber threats, the outage of cloud services may simultaneously impact many policyholders, thus leading to a large number of claims that AXIS shall be responsible for. To improve insurers’ resilience against this risk, this project aims to explore and identify a suite of internal systemic cyber threat scenarios that together are broad enough to cover the entire threat landscape, but individually are specific enough to be used in practice on a stand-alone basis to support business decision making and risk understanding. 

Furthermore, in this project, we will try to characterize each scenario for the inference of its occurrence likelihood and severity, and the results will be used to support setting capital and pricing loads, to challenge probabilistic risk modeling approaches, to monitor and manage exposure and risk trends, and may be used to set strategy.

Supervisors: Runhuan Feng, Zhiyu (Frank) Quan, Project Manager from AXIS Capital 

Graduate Supervisor: Linfeng Zhang 

Image by Glenn Carstens-Peters

Building an NLP-Powered Repository and Search Tool for Cyber Risk Literature Project

Description:

Since the time when cyber insurance was first introduced to the market, there has been a rapidly expanding volume of literature that focuses on other aspects of cyber risk, such as the legal and financial consequences of cyber incidents, and they are closely related to the development of the cyber insurance industry. With the large and growing body of cyber risk literature, we see three major challenges faced by the actuarial research community,

  • No context-aware tool for finding cyber risk resources

  • No central repository of cyber risk resources

  • Lack of accounting for trends in cyber risk research

To address the abovementioned challenges, we propose to build a repository of cyber risk literature, equipped with an NLP-powered search tool that can be easily used by researchers to find relevant materials. The first stage of this project involves identifying sources of literature, creating a program that gathers documents from those sources, and labeling the gathered documents. A databased will be built based on the collected information. On top of that, a web-based user-interface will be built to make it easier for researchers to query the database and see the results in a clear manner. In the second stage, as the database gets sizeable and becomes suitable for training and testing purposes, the labeling of new articles can be automated by natural language processing and machine learning techniques.

Supervisors: Runhuan Feng, Zhiyu (Frank) Quan

Graduate Supervisor: Linfeng Zhang

Market Analysis

COUNTRY Financial Business Owner's Policy (BOP) Loss Predictive Model

Description:

For the insurance industry, the potential to understand customers and businesses using new
dimensions represented by social and other online data can unleash significant new insights
from both customer behavior and risk perspective. These insights can drive insurance
automation, underwriting efficiency, and enhanced customer experience. The objective of this
project is to develop and optimize the loss prediction models empowered by those insurtech
innovations. This project is a real-life actuarial data science project provided by Carpe Data and COUNTRY Financial.
Carpe Data is an Insurtech company that provides insurance companies with next-generation data solutions to gain a more in-depth insight into risks. COUNTRY Financial is a US insurance and financial services company that offers a range of insurance and financial products and services, including auto, home, life, commercial insurance, etc.  We will perform tasks including, but not limited to: 

  • Actuarial loss modeling 

  • Feature engineering using NLP 

  • Unsupervised/Supervised Learning 

Supervisors: Zhiyu (Frank) Quan, Project Manager from Carpe Data, Actuarial manager from COUNTRY Financial
Graduate Supervisor: Changyue Hu

 

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Insurtech Innovation via Natural Language Processing (NLP) Project

Description:

For the insurance industry, the potential to understand customers and businesses using new dimensions represented by social and other online data can unleash significant new insights from both customer behavior and risk perspective. These insights can drive insurance automation, underwriting efficiency, and enhanced customer experience. The objective of this project is to develop NLP models empowered by those insurtech innovations. 

This project is a real-life actuarial data science project provided by Carpe Data. Carpe Data is an Insurtech company that provides insurance companies with next-generation data solutions to gain a more in-depth insight into risks. COUNTRY Financial is a US insurance and financial services company that offers a range of insurance and financial products and services, including auto, home, life, commercial insurance, etc. 

We will perform tasks including, but not limited to: 

  • Data cleaning and imputation

  • Feature engineering using NLP 

  • NLP: Summarization, Topic Modeling, Named Entity Recognization, Question Answering, etc.

Supervisors: Zhiyu (Frank) Quan, Project Manager from Carpe Data

 

Image by Tyler Franta

Luyan Sales Data Analysis Project

Description:

Sales data provides businesses with valuable information on the profiling of their customers and thus has important implications on business problems, such as the improvement of customer satisfaction, retention rate, and sale efficiency. In this project, a dataset of sales records will be provided by Luyan Pharma, which has not been analyzed and utilized to its full potential for the purpose of gaining insights into the company’s operations and customers and guiding business decision-making. Luyan is a pharmaceutical company headquartered in Xiamen, Fujian, China, with operations in health product R&D, manufacturing, and distribution. As an industry leader, it is exploring new ways of improving its services provided to its customers. This is an exploratory project, and the tasks will be performed including but not limited to

  • Data wrangling

  • Feature engineering using NLP

  • Supervised/unsupervised learning

Supervisors:  Zhiyu (Frank) Quan, Project Manager from Luyan Pharma

Graduate Supervisor: Linfeng Zhang

Image by Jess Bailey

Smart Contract for Distributed Insurance Project

Description:

A smart contract is a set of codes that execute business logics for contractual agreement in financial transactions. Our University of Illinois team has designed various business models for catastrophe risk sharing.  In this project, students are expected to learn smart contract programming and build the first of its kind smart contract for distributed insurance.

Supervisors: Runhuan Feng

Graduate Supervisor: Yulong Wu