Real-Time Anomaly Detection Within Credit Card Transactions

[MUSIC PLAYING] In this digital age,
credit card fraud has become a common
occurrence that can mean huge losses
for businesses and put consumers’ personal
information at risk. Students at the
University of Chicago set out to develop a real-time
anomaly detection process to detect abnormal behavior
within credit card transaction data. So how did they do it? First they needed to find
realistic customers to study. So they developed a methodology
utilizing US census attributes of age, sex, and
population density, which allowed them to create
a complete, accurate, and synthetically
generated data set. Then they established
customized spending profiles for each consumer segment. This allowed them to
represent and study people with diverse spending habits. From there, the
students identified, through a literature
review, a set of significant
variables that are indicative of
fraudulent transactions. These fraud indicators were
varied beyond normal limits to generate fraudulent
transactions. Their approach was
based on finding the anomalies in
transactional behavior by defining normal behavior and
declaring any data occurrences that lie outside of that
region as an anomaly. They developed a
statistical model to predict these anomalies. Specifically, they
used Markov chains, consisting of state transition
matrices for each user, as well as aggregate
state transition matrices for each consumer segment. These state transition
matrices allow them to define the
probability of a user to transition between
successive states. Categorized credit
card transactions. These probabilities, when
examined over a rolling window of a user’s five
most recent credit card transactions, allow
them to identify fraudulent
transactional behavior through state
transitions that had a low probability of occurring. Ultimately, they were
able to accurately detect approximately 83.5% of
fraudulent transactions, with a false positive
rate of roughly 0.5%. A very promising result.

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