Question

1. Read the article entitled "Following Benford's Law , or Looking Out for No. 1" found at www.rexswain.com/benford.html. The article mentions that a statistic professor can easily discern if students flipped a coin 200 times or if they merely faked it. Of this exercise, the professor stated, "Most people do not know the real odds of such an exercise, so they cannot fake data convincingly." How does that exercise relate to Benfords Law and detecting Fraud?

2. Search an Internet search engine for neural network fraud" Answer the following questions:

a. What is a neural network?

b. How can neural networks be used to detect fraud?

c. Which industries will benefit the most from neural network technology?

d. Name a couple of firms that are developing this technology.

e. Credit card companies are very concerned with the growing problem of credit card fraud. They spend enormous amounts of money each year on detection. Go to the Web site of a large credit card company such as visa, Mastercard, or American Express. What are some of the proactive measures of these institutions are taking to control fraud and to persuade the public that it is safe to use credit cards?

Answer 1

Answer:

1.As discussed in the article, performing statistical analysis using Benford’s Law is a good proactive method to detect fraud. The probability of a number beginning with 1 as the first digit is not 1 in 9 as most people think. The law states that the probability of a number beginning with 1 as the first digit is about 30 percent, and the probability of the number beginning with 9 as the first digit is actually 4.6 percent. Armed with this knowledge, a fraud examiner can detect fraud by looking for exceptions to Benford’s Law. Perpetrators of fraud may not understand Benford’s Law and incorrectly assume that the probability is 1 in 9. Benford’s Law helps identify those people that “fake” transactions.

2. a) A neural network is a computer-based model that patterns after the human brain. It is used to model data,  similar to regression in statistics.

b)   Neural networks can model the normal patterns in the data and find transactional trends that do not match the norm.

c)   Many industries can benefit from this technology, but in particular, the credit card industry has used it significantly to model consumer behavior and guess when a credit card is being used by a different person.

d) Students will name firms that they have found on the Internet.

e) Students will find different procedures and policies that help identify fraud.One example is a representative calling the card owner to verify large transactions, overseas transactions, or other risky transactions that seem different from that card owner’s (consumer) normal behavior.