This document discusses data warehousing, knowledge discovery in databases (KDD), and data mining. It defines a data warehouse as a subject-oriented collection of integrated and non-volatile data used to support management decision making. Data mining is extracting knowledge from large amounts of data and has two main uses: making sense of too much data with too little information and extracting useful information from data to interpret it. The document also discusses applications of data mining in areas like banking, marketing, risk management, and customer acquisition/retention.

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.