PRESTIGE INSTITUTE OF MANAGEMENT, GWALIOR 
Presented by- 
Parinita shrivastava 
Arpit bhadoriya
What is DATA 
WAREHOUSE..? 
 A DATA WAREHOUSE is a subject oriented, 
integrated, time-varying, non-voletile collection o...
KDD (Knowledge Discovery In 
Database) 
 Steps:- 
 Selection 
 Preprocessing 
 Transformation 
 Data Mining 
 Interp...
What is DATA MINING..? 
 DATA MINING refers to extracting knowledge from 
large amount of data. 
 It is a powerful new t...
Why use DATA MINING? 
 Two main reasons to use data mining: 
 Too much data and too little information. 
 There is a ne...
DM Application Areas 
 Business Transaction 
 Electronic Commerce 
 Health Care Data 
 Web Data 
 Multimedia Document...
DM Techniques 
 Verification Model 
 Discovery Model 
 Clustering
APPLICATIONS IN BANKING 
SECTOR 
 Marketing. 
 Risk Management. 
 Customer Relation Management. 
 Customer Acquisition...
APPLICATION IN 
MARKETING 
Objective: 
Improve marketing techniques and target customers 
Traditional applications: 
 Cus...
RISK MANAGEMENT 
Objective: 
Reduce risk in credit portfolio 
Traditional applications: 
 Default prediction 
Reduce loan...
CUSTOMER ACQUISITION AND 
RETENTION 
Objective: 
Increasing value of the Customer and Customer Retention. 
Traditional App...
CONCLUSION 
 Data mining is a tool enable better decision-making 
throughout the banking and retail industries.. 
 Data ...
USE OF DATA MINING IN BANKING SECTOR
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USE OF DATA MINING IN BANKING SECTOR

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WHAT IS DATA MINING???
WHY TO USE?????
USES OF DATA MINING IN BANKING SECTOR ???

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USE OF DATA MINING IN BANKING SECTOR

  1. 1. PRESTIGE INSTITUTE OF MANAGEMENT, GWALIOR Presented by- Parinita shrivastava Arpit bhadoriya
  2. 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. 3. KDD (Knowledge Discovery In Database)  Steps:-  Selection  Preprocessing  Transformation  Data Mining  Interpretation And Evaluation
  4. 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. 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. 6. DM Application Areas  Business Transaction  Electronic Commerce  Health Care Data  Web Data  Multimedia Documents
  7. 7. DM Techniques  Verification Model  Discovery Model  Clustering
  8. 8. APPLICATIONS IN BANKING SECTOR  Marketing.  Risk Management.  Customer Relation Management.  Customer Acquisition And Retention.
  9. 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. 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. 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. 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.
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