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

Date post: 05-Dec-2014
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Student at Desh Bhagat University, Mandi Gobindgarh.
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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
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Page 1: Data Quality, Data Mining & Applications of Data Mining in Banking Sector

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

Page 2: Data Quality, Data Mining & Applications of Data Mining in Banking Sector

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.

Page 3: Data Quality, Data Mining & Applications of Data Mining in Banking Sector

Aspects of Data Quality

• Accuracy• Completeness• Update Status• Accessibility

Page 4: Data Quality, Data Mining & Applications of Data Mining in Banking Sector

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.

Page 5: Data Quality, Data Mining & Applications of Data Mining in Banking Sector

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.

Page 6: Data Quality, Data Mining & Applications of Data Mining in Banking Sector

Application of Data Mining in Banking

• Marketing• Risk Management• Customer Relation Management• Customer Acquisition and Retention.

Page 7: Data Quality, Data Mining & Applications of Data Mining in Banking Sector

Application in Marketing

• Objective:Improve Marketing Techniques and target

Customers.• Traditional Application:

Customer SegmentationCross SellingAttrition Analysis

Page 8: Data Quality, Data Mining & Applications of Data Mining in Banking Sector

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.

Page 9: Data Quality, Data Mining & Applications of Data Mining in Banking Sector

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.

Page 10: Data Quality, Data Mining & Applications of Data Mining in Banking Sector

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.

Page 11: Data Quality, Data Mining & Applications of Data Mining in Banking Sector

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 RiskMarket RiskOperational Risk

Page 12: Data Quality, Data Mining & Applications of Data Mining in Banking Sector

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.

Page 13: Data Quality, Data Mining & Applications of Data Mining in Banking Sector

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.

Page 14: Data Quality, Data Mining & Applications of Data Mining in Banking Sector

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.

Page 15: Data Quality, Data Mining & Applications of Data Mining in Banking Sector

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.

Page 16: Data Quality, Data Mining & Applications of Data Mining in Banking Sector

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.

Page 17: Data Quality, Data Mining & Applications of Data Mining in Banking Sector

How to managed the angry Customers?

Page 18: Data Quality, Data Mining & Applications of Data Mining in Banking Sector

• One of the worst feelings which gets ignited out is anger as it makes the person as well as the surrounding uncomfortable.

Stay CalmListenAsk QuestionTake full control

Page 19: Data Quality, Data Mining & Applications of Data Mining in Banking Sector

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.

Page 20: Data Quality, Data Mining & Applications of Data Mining in Banking Sector

Building Blocks:Acquisition

Conversion

Retention

Page 21: Data Quality, Data Mining & Applications of Data Mining in Banking Sector

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.

Page 22: Data Quality, Data Mining & Applications of Data Mining in Banking Sector

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.

Page 23: Data Quality, Data Mining & Applications of Data Mining in Banking Sector

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.

Page 24: Data Quality, Data Mining & Applications of Data Mining in Banking Sector

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.

Page 25: Data Quality, Data Mining & Applications of Data Mining in Banking Sector

THANK YOU


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