IS 687
Applications of Stream Data Mining
Course Syllabus
Fall 2011
Instructor: Michael Recce
Office: GITC 4103
E-Mail: mlrecce@gmail.com
Office Hours: Thursday 3:00pm -5:00pm
Description of Course:. Increasingly all of our transactions are electronic. We use the internet
to find information and to shop. When we go to a bricks and mortar shop we increasingly use
debit and credit cards (electronic transactions) instead of checks and cash. The store tracks
individual items that are purchased electronically. Banks, telephone service companies, stores
and many other businesses have high volume transaction streams. This course presents
methods for finding opportunity and risk in this transaction data. Opportunity might correspond
to a customer who through his transactions looks like he may be interested in another of the
company’s products. Evaluation of transactions to find risk includes detection of terrorists,
fraudsters and money launderers.
The course will review case studies of transaction
monitoring and will discuss the range of methods that are applied.
Course Grade Components:
Midterm Exam (closed notes) 30%
Final Project (open notes) 30%
Weekly Summaries (a half to one page summary of each weeks reading ) and Assignments 30%
Class participation 10%
Note: this is a tentative schedule, and we reserve the privilege to modify and edit these topics
for the benefit of the course.
Week 1: What is transaction monitoring? Description of basic principles and applications.
Week 2: The nature of transactions. Structured verses unstructured transaction data. Building
temporal and spatial profiles of transaction data. Use of peer group models.
Week 3: Transaction monitoring at banks. Detecting money laundering at a top tier bank. System
organization and requirements for the transaction monitoring system. Sample detected cases.
The bank transactions of the 9/11 and 7/7 terrorists.
Week 4: Detecting fraud in bank transaction data. Definition and examples of bank fraud. Case
study of monitoring for employee fraud and identity theft at a major European bank.
IS 687
Applications of Stream Data Mining
Week 5: Sarbanes-Oxley and the Patriot Act. How legislation has advanced requirements for
transaction monitoring at financial institutions. Case study of general ledger monitoring at a
large US bank.
Week 6: Application of transaction monitoring to phone conversions and phone records. Case
study of customer calls at a Continental Airlines to stem loss of customers. Case study of
monitoring phone calls for watch list terms. Linkage analysis, and other methods to detect
groups that communicate often.
Week 7: Unstructured data on the internet. Using the click stream to determine who is lost and
who is looking. Profiling user click activity. Case study evaluation of cross selling at online
retailers.
Week 8: Midterm exam
Week 9: Constructing demographic profiles from web traffic. Constructing appropriate profiles
and peer groups based on web traffic. The new trend in targeted advertising through analysis of
transaction data. Case study of Google adwords and Quantcast web demographics.
Week 10: System requirements for transaction monitoring. Hardware and software requirements.
Data extracts and data issues. Who needs to be involved. How much does it cost.
Week 11: New product packaging and tracking what you buy. Radio frequency and alternative
technologies to monitor purchases. Monitoring traffic on roads or knowing where you are.
Week 12: Transaction monitoring of remote online education. Profiling the learning process.
Using transactions to direct education and discover when to provide what material for more
efficient learning. Case study with ipods.
Week 13: Future trends in transaction monitoring. Where are the limits.
Week 14: Review and student final presentations