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Data Quality, Data Mining & Applications of Data Mining in Banking Sector

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Data Quality, Data Mining & Applications of Data Mining in Banking Sector

  1. 1. Data Quality & Applications of Data Mining in Banking Sector Submitted To: Prof. Vivek Bhambri Group Name: Information Management Group Members: Nardeep Singh, Manpreet Singh, Jaspreet Singh, Jasvir Singh, ( Gurjeet Singh, Sarbjeet Singh)DBU
  2. 2. Data Quality • Data Quality is Perception or an assessment of data’s fitness to serve it’s Purpose in a given context. • Data Quality is affected by the way data is entered, stored and managed.
  3. 3. Aspects of Data Quality • Accuracy • Completeness • Update Status • Accessibility
  4. 4. Data Mining • Data Mining is the process of collecting large amounts of raw data and transforming that data into useful information. • 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 reason to use data mining:  Too much data and too little information.  There is need to extract useful information from the data and to interpret the data.
  6. 6. Application of Data Mining in Banking • Marketing • Risk Management • Customer Relation Management • Customer Acquisition and Retention.
  7. 7. Application in Marketing • Objective: Improve Marketing Techniques and target Customers. • Traditional Application: Customer Segmentation Cross Selling Attrition Analysis
  8. 8. Customer Segmentation: • Segmentation is made more complex because customer may belong to multiple segments. • Identify the characteristics of the customers who buy the same product from your company. • Predict which customer are likely to leave your company and go to competitor.
  9. 9. Cross Selling • Cross selling is one of the easiest and most effective method of marketing. • Cross selling is when the customer comes up to buy something and we sell completely a different product to offer customers. • Cross selling generally occurs when the sales representatives has more than one type of products.
  10. 10. Attrition Analysis • Attrition analysis basically include the reason for leaving the job like salary, boss problem and other issue. • Attrition is the reduction in the number of employs through resignation, retirement and death.
  11. 11. Risk Management • Risk management is the study of how to control the risk. • Object: Reduce risk in Credit Portfolio. • Types of Risk in Banking Sector: Credit Risk Market Risk Operational Risk
  12. 12. Credit Risk • Credit Risk involves Borrower risk, industry risk, Portfolio Risk. • It also known as default Risk which checks the ability of an industry or a customer. • Credit Risk is one of the most fundamental type of the risk. After all it represent the chance the investor loss his investment.
  13. 13. Market Risk • Market Risk is the risk of losses in positions arising from movements in markets place. • Types of Market Risk: • Interest rate Risk • Currency Risk • Commodity Risk.
  14. 14. Operational Risk • Operational Risk is the risk of change in value caused by the fact that actually losses or failed internal process and system. • It can also include other classes of risk such as fraud, security, privacy, protection, legal risk, physical or environmental risk. • It is different from the expected losses.
  15. 15. Customer Relation Management • Customer Relation management is a widely implemented strategy for managing a Bank/Company’s interaction within the customer and clients. • The Overall goal are to find, attract and win new clients. • Customer being asset to any industry, one planning to work as a part of customer service.
  16. 16. The Basic needs of a Customer • We all have various needs which we do give importance. • Since customers are our assets we need to take care of their every need. Most basic needs are: • Friendliness: Customer require Friendliness, they want us to treat them equal just like a friend. • Empathy: it follows up with understanding, on the latter, both work relatively together. customer require attention from us because they are here in our organization to get a product and no to waste time. They need us to feel their need. Thus empathy play a vital role in customer relation management. • Information: Yes, customers are here in our organization with quite much knowledge about the product that we offer. If we are not up to their expectations regarding the information part of the service , we are in some trouble because there is very less chance for the customer to stay and invest.
  17. 17. How to managed the angry Customers?
  18. 18. • One of the worst feelings which gets ignited out is anger as it makes the person as well as the surrounding uncomfortable. Stay Calm Listen Ask Question Take full control
  19. 19. Customer Acquisition and Retention • Objective: Increasing value of Customer and Customer Retention. • Traditional Applications: Needs of Customer by Providing Product and Service. Help us to find loyal customer. Need to accomplish relation between bank and customer.
  20. 20. Building Blocks: Acquisition Conversion Retention
  21. 21. Acquisition • How do you attract your customers? • How they are going to use it first time? • There are three main channels through which someone can find your site. They find it themselves. They find out through the media. They find it from friends.
  22. 22. Customer Conversion • Customer acquisition and conversion are equally important, even thought each have different focus. • In this Converting means How effective are you in converting users to customers? • Conversion action plans concentrate on turning “lookers” into paying customers.
  23. 23. Customer Retention • An assessment of the product or service quality provided by a business that measure how loyal it’s customers are. • Customer Retention is a cost-effective and profitable business strategy. • Successful customer retention starts with the first contact and continue through the entire lifetime of the relationship.
  24. 24. Conclusion • Data Mining is a tool enable better decision making throughout the banking. • Data Mining Technique can be very helpful to the bank for better targeting and acquiring new Customers. • Analysis of the Customer.

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