data mining technique

1. PRESTIGE INSTITUTE OF MANAGEMENT, GWALIOR
Presented by-
Parinita shrivastava
Abhishek sharma
Arpit bhadoriya
Bhupendra sony
BCA 2nd sem

2. What is DATA
WAREHOUSE..?
 A DATA WAREHOUSE is a subject oriented,
integrated, time-varying, non-voletile collection of
data in support of the management’s decision-making
process.

3. KDD (Knowledge Discovery In
Database)
 Steps:-
 Selection
 Preprocessing
 Transformation
 Data Mining
 Interpretation And Evaluation

4. What is DATA MINING..?
 DATA MINING refers to extracting knowledge from
large amount of data.
 It is a powerful new technology with great potential
to analyze important information in the data
warehouse .

5. Why use DATA MINING?
 Two main reasons to use data mining:
 Too much data and too little information.
 There is a need to extract useful information
from the data and to interpret the data.

6. DM Application Areas
 Business Transaction
 Electronic Commerce
 Health Care Data
 Web Data
 Multimedia Documents

7. DM Techniques
 Verification Model
 Discovery Model
 Clustering

8. APPLICATIONS IN BANKING
SECTOR
 Marketing.
 Risk Management.
 Customer Relation Management.
 Customer Acquisition And Retention.

9. APPLICATION IN
MARKETING
Objective:
Improve marketing techniques and target customers
Traditional applications:
 Customer segmentation
Identify most likely respondents based on previous campaigns
 Cross selling
Develop profile of profitable customers for a product
 Attrition analysis:
Alert in case of deviation from normal behaviour

10. RISK MANAGEMENT
Objective:
Reduce risk in credit portfolio
Traditional applications:
 Default prediction
Reduce loan loses by predicting bad loans
 High risk detection
Tune loan parameters ( e. g. interest rates, fees) in order to
maximize profits
 Profile of highly profitable loans
Understand characteristics of most profitable mortgage loans
 Credit card fraud detection
Identify patterns of fraudulent behaviour

11. CUSTOMER ACQUISITION AND
RETENTION
Objective:
Increasing value of the Customer and Customer Retention.
Traditional Application:
 Needs of the customer by providing products and services
which they prefer.
 Help us to find the loyal customer.
 Need to accomplish relation between bank and customer.

12. CONCLUSION
 Data mining is a tool enable better decision-making
throughout the banking and retail industries..
 Data Mining techniques can be very helpful to the banks for
better targeting and acquiring new customers.
 Fraud detection in real time.
 Analysis of the customers.
 Purchase patterns over time for better retention and
relationship.