USE OF DATA MINING IN BANKING SECTOR
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The 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 applications in business transactions, ecommerce, healthcare, and more. Specifically for banking, data mining can be used for marketing, risk management, and customer acquisition/retention by identifying patterns in large customer data sets.
USE OF DATA MINING IN BANKING SECTOR
- 1. PRESTIGE INSTITUTE OF MANAGEMENT, GWALIOR Presented by- Parinita shrivastava Arpit bhadoriya
- 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.