This document discusses fraud detection techniques using data mining. It describes how data mining and statistics can help anticipate and quickly detect fraud. Three main techniques are discussed: Bayesian networks, decision trees, and backpropagation. Bayesian networks construct models to describe fraudulent versus legitimate behavior. Decision trees induce a tree to generate classification rules. Examples of rules generated from a decision tree for fraud detection are provided. The document also briefly mentions existing fraud detection systems such as a fuzzy logic system and self-organizing feature map.

1. FRAUD DETECTION
BY:
PATEL SHRUTI J.
B.E-SEM 7,COMPUTER ENGINEERING

2. INTRODUCTION
 Fraud detection is a topic applicable to many industries including banking
and financial sectors, insurance, government agencies and law
enforcement, and more.
 Fraud attempts have seen a drastic increase in recent years, making fraud
detection more important than ever. Despite efforts on the part of the
affected institutions, hundreds of millions of dollars are lost to fraud every
year.
 The term fraud refers to abuse of a profit of the organization.

3. Where FRAUD happens:
 Banking sector
 Insurance fraud
 Telecommunication fraud,etc…

4. Data mining in use:
 Data mining and statistics help to anticipate and quickly detect fraud and
take immediate action to minimize costs.
 Through the use of sophisticated data mining tools, millions of
transactions can be searched to spot patterns and detect fraudulent
transactions.

5. Data mining techniques:
 Three data mining techniques used for fraud analysis are:
i)Bayesian network
ii) Decision tree and
iii) backpropagation

6. Bayesian network:
 For the purpose of fraud detection, we construct two Bayesian networks to
describe the behavior of auto insurance. First, a Bayesian network is
constructed to model behavior under the assumption that the driver is
fraudulent (F) and another model under the assumption the driver is a
legitimate user (NF).
 We can get the probability of the measurement x under two above
mentioned hypotheses. This means, we obtain judgments to what degree
an observed user behavior meets typical fraudulent or non-fraudulent
behavior. These quantities ,we call p(x|NF) and p(x|F).

7. Bayesian network:

8. Bayesian network:
 The classifier has to predict the class of instance to be fraud or legal.
 P(fraud) = si / s = 3/20 = 0.15
 P(legal) = si/ s =17/20 = 0.85

9. Bayesian network:

10. Bayesian network:
 Suppose, we wish to classify X = (Crystal Smith, F, 31). By using these
values and the associated probabilities of gender and driver age, we obtain
the following estimates:
 P(X |legal) = 4/17 * 3/18 = 0.039
 P(X |fraud) = 3/3 * 1/2 = 0.500
 So,she is more likely to be fraud.

11. Decision tree:
 A decision tree (DT) is a tree associated with a database that has each
internal node labeled with an attribute, each arc labeled with a predicate
that can be applied to the attribute, and each leaf node labeled with a
class.
 Solving the classification problem is a two-step process:
i) decision tree induction- construct a DT and
ii) apply the DT to determine its class. Rules can be generated that are easy
to interpret.

12. Decision tree:
 The following rules are generated for the Decision Tree (DT).
 If driver age =25, then class = legal
 If (driver_age =40) ∧(vehicle_age =7), then class = legal.
 If (driver_age =32) )∧(driver_rating =1), then class = fraud.
 If (driver_age ≤40) )∧(driver_rating =1) ) ∧(vehicle_age =2), then class =
fraud.
 If (driver_age > 40) ) ∧(driver_age ≤ 50) ) ∧(driver_rating = 0.33), then class
= legal.

13. Existing Fraud detecting systems:
 A fuzzy logic system.
 The hot spots methodology
 The credit fraud model
 Self-Organizing Feature Map

14. THANK YOU