18-09-2014, 10:42 AM
PREDICTIVE DATA MINING AND FRAUD MANAGEMENT
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ABSTRACT
Predictive models and Data Mining have been used for various business applications and
predictions from quite a long time. The applications of Predictive Data Mining include Sales
and Marketing, Buyer Behaviour Prediction, Customer Retention, Cost/Utilization, Inventory
Management, Quality Control and Fraud Management. Fraud Management has been the least
addressed issue among all the above applications[3]. This paper basically deals with the
application of predictive analytics and data mining techniques for Fraud Management. Frauds
in various businesses are covered such as, insurance, health care and telecommunication. The
case studies of various frauds tackled are also presented in this paper.
INTRODUCTION
In a broadest sense, fraud is ”a criminal deception intended to gain money or personal
advantage” (Compact Oxford Dictionary Thesaurus and Wordpower Guide). Fraud is a big
problem for many businesses and can be of various types. Inaccurate credit applications,
fraudulent transactions, identity thefts and false insurance claims are some examples of this
problem. These problems plague firms all across the spectrum and some examples of likely
victims are credit card issuers, insurance companies, retail merchants, manufacturers,
business to business suppliers and even services providers. This is an area where a predictive
model is often used to help weed out the “bads” and reduce a business's exposure to fraud.
Data Mining is an analytic process designed to explore data (usually large amounts of data -
typically business or market related) in search of consistent patterns and/or systematic
relationships between variables, and then to validate the findings by applying the detected
patterns to new subsets of data. The ultimate goal of data mining is prediction - and
predictive data mining is the most common type of data mining and one that has the most
direct business applications.
DATA MINING
Introducing Data Mining:
Databases today can range in size into the terabytes — more than 1,000,000,000,000 bytes of
data. Within these masses of data lies hidden information of strategic importance. How do we
summarize and extract the necessary information from such a huge warehouse of data?
The newest answer is Data Mining, which is being used both to increase revenues and to
reduce costs. The potential returns are enormous. Innovative organizations worldwide are
already using data mining to locate and appeal to higher-value customers, to reconfigure their
product offerings to increase sales, and to minimize losses due to error or fraud.
Data Mining: What it can’t do.
Data mining is a tool. It won’t sit in your database watching what happens and send you e-mail to get
your attention when it sees an interesting pattern. It doesn’t eliminate the need to know our
business, to understand our data, or to understand analytical methods. Data mining assists business
analysts with finding patterns and relationships in the data — it does not tell us the value of the
patterns to the organization. Furthermore, the patterns uncovered by data mining must be verified in
the real world.
Data mining will not automatically discover solutions without guidance. Rather than
setting the vague goal, “Help improve the response to my direct mail solicitation,” we might
use data mining to find the characteristics of people who (1) respond to our solicitation, or (2)
respond AND make a large purchase. The patterns data mining finds for those two goals may
be very different.
Data mining does not replace skilled business analysts or managers, but rather gives them
a powerful new tool to improve the job they are doing. Any company that knows its business
and its customers is already aware of many important, high-payoff patterns that its employees
have observed over the years. What data mining can do is confirm such empirical
observations and find new, subtle patterns that yield steady incremental improvement (plus
the occasional breakthrough insight).
PREDICTIVE DATA MINING(PDM).
Introducing Predictive Data Mining.
Predictive Data Mining combines database analysis with multivariate statistics and artificial
intelligence to derive conclusion from huge databases. In recent years, predictive data mining
has become an essential tool for strategic decision making among mid-size to large
corporations. It has been proven effective in predicting future customer behavior, classifying
customer segments, forecasting events and fraud management.
Predictive Data Mining uses predictive analytics and builds predictive models which actually
enable PDM to predict the desired information. Predictive analytics encompasses a variety
of techniques from statistics, data mining and game theory that analyze current and historical
facts to make predictions about future events. A Predictive Model is made up of a number of
predictors, which are variable factors that are likely to influence future behaviour or results.
In marketing, for example, a customer's gender, age, and purchase history might predict the
likelihood of a future sale.
A Predictive model can be defined as, “A black box that makes predictions about the future
based on information from the past and present.”[14] Here is an example of a predictive
model.
1Direct Marketing.
Product marketing is constantly faced with the challenge of coping with the increasing
number of competing products, different consumer preferences and the variety of methods
(channels) available to interact with each consumer. Efficient marketing is a process of
understanding the amount of variability and tailoring the marketing strategy for greater
profitability. Predictive analytics can help identify consumers with a higher likelihood of
responding to a particular marketing offer. Models can be built using data from consumers’
past purchasing history and past response rates for each channel. Additional information
about the consumers demographic, geographic and other characteristics can be used to make
more accurate predictions. Targeting only these consumers can lead to substantial increase in
response rate which can lead to a significant reduction in cost per acquisition. Apart from
identifying prospects, predictive analytics and data mining can also help to identify the most
effective combination of products and marketing channels that should be used to target a
given consumer.
CASE STUDY—DATA MINING POWERS TELECOMMUNICATION FRAUD DETECTION SOLUTION.
Introduction.
Telecommunications fraud has been identified as the single biggest cause of revenue loss for
telecommunications providers, with figures averaging between 3 and 5 percent of an
operator's annual revenue. Current statistics point to a global loss of USD 55 billion per year,
making telecommunications fraud a bigger business than international drug trafficking.
International Data Corporation (IDC) estimates that more than 200 variants of telecom fraud
exist and that this number is growing with the advent of new services such as 3G and VoIP.
Telecom fraud attacks are becoming increasingly sophisticated and are tapping into the
arrival of these new telecommunication technologies
What is Telecommunication Fraud?
There are as many definitions of telecom fraud as there are fraud managers employed in the
industry. However, there does seem to be a general consensus that telecom fraud, as the
term is generally applied, involves the theft of services or deliberate abuse of voice and data
networks. Furthermore, it is accepted that in these cases the perpetrator’s intention is to
completely avoid or at least reduce the charges that would legitimately have been charged for
the services used. On occasion, this avoidance of call charges will be achieved through the
use of deception in order to fool billing and customer care systems into invoicing the wrong
party.
Telecommunication fraud is the theft of telecommunication service (telephones, cell phones,
computers etc.) or the use of telecommunication service to commit other forms of fraud.
Victims include consumers, businesses and communication service providers.[11]
CONCLUSION.
In this work, the successful application of Data Mining Techniques and Predictive Modelling
in various fields such as marketing, customer behaviour prediction, risk management and
fraud management is discussed. A case study of Telecommunication Fraud management
using Data Mining is also taken up. The problem of fraud plagues firms all across the
spectrum and some examples of likely victims are credit card issuers, insurance companies,
retail merchants, manufacturers, business to business suppliers and even services providers.
From the study made, it can be concluded that Data Mining along with Predictive Modelling
can be successfully used for detecting and investigating frauds.