Curriculum and Program Requirements

General Requirements

This Master’s degree program in Predictive Analytics & Risk Management (PARM) will be jointly administered by the Departments of Mathematics and Statistics in the College of Liberal Arts and Sciences, and the Department of Finance in the Gies College of Business.

The coursework is intended for students who have the prerequisite quantitative background to train for careers in predictive analytics for insurance and other financial settings by providing a multidisciplinary and integrated program. Core requirements include courses from three disciplines, a course in financial risk management, courses in risk management and predictive analytics from an actuarial science perspective, and training in statistical machine learning, big data techniques, and Bayesian statistical methods. Related courses from the three disciplines may then be chosen as electives for students to reach their individualized educational goals. Courses will be scheduled so that students may complete the 32-hour program in one academic year.

Each concentration requires 12 hours of common core courses, organized around three broad areas of expertise, including a case study course. Each concentration also requires 12 hours of related area coursework specific to the concentration, plus an additional 8 hours of electives from a prescribed list. At least 12 hours must be taken at the 500 level. Students must maintain a minimum GPA of 2.75.

Core Courses:12 hours
Concentration Required Courses:12 hours
Core Electives:8 hours
Total:32 hours

Core Courses

PARM students are from a variety of backgrounds, not necessary in computer science or finance. Some students may need to work on their fundamental knowledge in important areas during their first semester. We offer four core classes:

  • FIN 530 Foundation in Risk Management (2 credit hours)
    This course introduces risk management including basic concepts and techniques of pure risk and financial risk management. Corporate hazard risk management including insurance and securitization of pure risks will be covered in detail. Insurer risk management will be examined including reinsurance, loss reserving, underwriting of risks, and catastrophic risk management. Students will also be introduced to Enterprise Risk Management (ERM).
  • ASRM 410 Investment and Financial Markets (4 credit hours)
    Theoretical foundation in financial models and their applications to insurance and other financial risks. Topics include derivative markets, no arbitrage pricing of financial derivatives, interest rate models, dynamic hedging and other risk management techniques.
  • ASRM 455 Predictive Analytics (4 credit hours)
    This course focuses on financial and insurance applications of statistical learning techniques to build predictive models, with integrated case studies and training on computational software packages and effective communication of statistical results. Topics include the model building process, data preparation, model selection, refinement and validation.
  • ASRM 539 Risk Analytics and Decision Making OR FIN 539 Cases in Risk Management (2 credit hours)
    The course will give students the opportunity to practice their existing data analytics skills to solve diverse real-world cases. Students will also deepen their ability to select the appropriate method to solve each problem, clearly and concisely present results, and clearly articulate the strengths and limitations of their analyses.
Concentrations

A concentration is an extension of a graduate major comprised of a coherent set of courses some or all of which count toward the major. Students must take a minimum of 12 credits of the required courses in order to earn a concentration.

The PARM program offers two concentrations:

  • Financial and Insurance Analytics
  • Enterprise Risk Management
Required Courses – Financial and Insurance Analytics Concentration

The program is focused on providing a flexible set of classes for students to tailor to their own specific career aims. There are only three required courses in the program:

  • STAT 431 Applied Bayesian Analysis (4 credit hours)
    Introduction to the concepts and methodology of Bayesian statistics, for students with fundamental knowledge of mathematical statistics. Topics include Bayes’ rule, prior and posterior distributions, conjugacy, Bayesian point estimates and intervals, Bayesian hypothesis testing, noninformative priors, practical Markov chain Monte Carlo, hierarchical models and model graphs, and more advanced topics as time permits. Implementations in R and specialized simulation software.
  • STAT 432 Basics of Statistical Learning (4 credit hours)
    Topics in supervised and unsupervised learning are covered, including logistic regression, support vector machines, classification trees and nonparametric regression. Model building and feature selection are discussed for these techniques, with a focus on regularization methods, such as lasso and ridge regression, as well as methods for model selection and assessment using cross validation. Cluster analysis and principal components analysis are introduced as examples of unsupervised learning.
  • STAT 480 Data Science Foundations (4 credit hours)
    Examines the methods of data management and analysis for “big data”, characterized by high volume, variety, velocity, and veracity. Attention will be focused on advanced statistical analysis and visualization in data science applications employing parallel processing, storage and distribution techniques necessary for analysis of massive data sets. Data mining techniques, machine learning methods, and streaming technologies will be utilized for real-time analysis. Students must have access to a computer on which they can install software.

Required Courses – Enterprise Risk Management Concentration

The program is focused on providing a flexible set of classes for students to tailor to their own specific career aims. There are only three required courses in the program:

  • FIN 538 Enterprise Risk Management (4 credit hours)
    The application of basic risk management principles to all risks facing the organization. Integrates hazard, financial, strategic and operational risks under a single framework. Provides a conceptual framework for making risk management decisions to increase business value. The course will includes a review of the legal and regulatory environment that sets the stage for Enterprise Risk Management, cover the tools used for risk analysis, examine data integration processes and show how risk measurement relates to strategic and tactical business decisions.
  • FIN 537 Financial Risk Management (4 credit hours)
    This course covers selected topics in financial risk management. The focus is on statistical techniques used in financial risk management rather than risk management practice, cases, or valuation issues. The course will cover the value-at-risk (VaR) measure and expected shortfall, statistical techniques useful to model financial market returns, and techniques used to model the joint distribution of defaults on fixed income instruments. The course will also cover additional topics such as retail credit risk, risk budgeting, and economic capital modelling.
  • ASRM 533 Risk Management Practices and Regulation (4 credit hours)
    Offers a comprehensive coverage of different aspects of risks and regulation of financial institutions. Topics include financial institutions and their trading, risk management frameworks, market risk, interest rate risk, liquidity risk, credit risk, operational risk, latest industry practices and regulation, including Basel and Solvency, fundamental review of trading books, scenario analysis and stress testing, etc..
Electives Available for Students

All of our extensive elective offerings are included in at least one of the PARM Specializations listed above. Here is a list of the electives that are offered in the MS Predictive Analytics & Risk Management program.

  • ASRM 409: Stochastic Processes for Finance and Insurance
  • ASRM 499: Topics in Actuarial Science
  • ASRM 510: Financial Mathematics
  • ASRM 533: Risk Management Practices Regulation*
  • ASRM 555: Advanced Predictive Analytics
  • ASRM 561: Loss Data Analytics and Credibility
  • ASRM 569: Extreme Value Theory and Catastrophe Modeling
  • ASRM 575: Life Insurance and Pension Mathematics
  • ASRM 595: Advanced Topics in Actuarial Science and Risk Analytics
  • FIN 431: Property and Casualty Insurance
  • FIN 511: Investment
  • FIN 512: Financial Derivatives
  • FIN 513: Financial Engineering I
  • FIN 514: Financial Engineering II
  • FIN 515: Fixed Income Portfolios
  • FIN 519: Behavioral Finance
  • FIN 537: Financial Risk Management*
  • FIN 538: Enterprise Risk Management*
  • FIN 550: Special Topics in Finance (Big Data Analytics)
  • FIN 551: International Finance
  • MATH 563: Risk Modeling and Analysis
  • STAT 431: Applied Bayesian Analysis*
  • STAT 432: Basics of Statistical Learning*
  • STAT 480: Big Data Analytics*
  • STAT 542: Statistical Learning
  • STAT 590: Individual Study and research

* Available as an elective if not taken as a concentration requirement

Alma in full graduation regalia greets commencement graduates on the campus of the University of Illinois Urbana-Champaign.