Welcome: In this course, each voice in the classroom has something of value to contribute. Please take care to respect the different experiences, beliefs and values expressed by students and staff involved in this course. My colleagues and I support UMass’s commitment to diversity, and welcome individuals regardless of age, background, citizenship, disability, sex, education, ethnicity, family status, gender, gender identity, geographical origin, language, military experience, political views, race, religion, sexual orientation, socioeconomic status, and work experience.
Course description: This course provides an introduction to the principles and practice of fraud detection across a variety of problem domains such as money laundering, credit card fraud, telecommunications fraud, and computer and network intrusion. The key topics of this course include defining fraud in various domains; the interactions between fraud prevention and fraud detection; data collection and management; statistical tests and statistical power; methods for statistical fraud detection, including numerical validation, outlier detection, supervised and unsupervised classification methods, and application of Benford’s Law. The course includes homework assignments and exams. The assignments will include practical exercises involving fraud detection on nontrivial datasets using software such as Excel, R, and Python.
Who is this course for? This course is intended for students pursuing a Graduate-level Certificate in Information Security. It is also approved as an outside elective for the MS program in Computer Science.
Prerequisites: A background in computer security, and familiarity with mathematics through precalculus. If you are unsure if your preparation is sufficient, please feel free to contact the instructor.