Final year B.E proposal in "Dataware house based intelligent banking analysis system"

The data warehousing & data mining have changed the decision making process in modern day business environment, which basically equip the business companies to reach their customers with the right product and right offer at the right time. This project is mainly concentrated to analyze the customer churn behavior, fraud detection and customer relationship management (CRM) in a banking system. The project will be implemented with a completely warehouse based business intelligence tools with some of the data mining algorithms implemented during reporting phase for churn prediction and anomaly detection.Since customers usually churn from one company to another quite often and this too is happening at an alarming rate and is becoming the most important issue in customer relationship management, so customer retention is the need of the hour to ponder upon. Our project will implement different visualization methods & techniques through Oracle Business Intelligence tool to analyze churn behavior. For this we will implement classification & regression tree (CART) analysis. The pattern of fraud detection will be implemented as location and time-wise. Rule-based methods such as BAYES, FOIL or RIPPER or Support Vector Machines (SVM) or unsupervised neural network (NN) algorithms such as Kohonen’s self-organizing map (SOM) NN will beused using meta-learning algorithms to improve prediction in fraud detection.

Introduction:

In Nepal, the number of banking customers are increasing day by day. As the customers’ number increases, the number  of transactions will also increase and more transactions  as well as customer's data will be added into the bank's database. This results into difficulty in managing the transaction and keeping the sound relationship with each customer. The customer dissatisfaction leads into the continuous loss and even collapse of the organization. So managers and executives of organization must be able to predict the churn behavior of his customer and must maintain a family relationship with all the customers. It costs very high if the managers use traditional approach without using the new tools and technology. Our system, that we are going to develop, will visualize and report the churn behavior, fraud detection and custom er relationship management (CRM) in a banking system.Customer is the heart and soul of any organization. The era of globalization and cut throat competition has changed the basic concept of marketing, now marketing is not confined to selling the services  to the customers, but the objective is to reach to the hearts of the customers so that they feel belonging towards the organizations and hence should remain the loyal customers. But, the ever growing databases make it difficult to analyze the data and to forecast the future trends. The solution liesin the use of Data Mining tools for predicting the churn
behavior of the customers.Churn in banking refers to a customer ceases his or her relationship with a bank. Reducing customer churn is a key business goal of every online business. The ability to predict that a particular customer is at a high risk of churning, while there is still time to do something about it, represents a huge additional potential revenue source for a bank. Besides the direct loss of revenue that results from a customer abandoning the business, the costs of initially acquiring that customer may not have already beencovered by the customer's spending to date. Furthermore, it
is always more difficult and expensive to acquire a new customer than it is to retain a current
paying customer. In order to succeed at retaining customers who would otherwise abandon the
business, our system will make the managers and executives of bank to be able to
 (a) predict in advance which customers are going to churn and
 (b) know which marketing actions will have the greatest retention impact on each particular customer.
Our project throws light on the underlying technology and the perspective applications of data mining in predicting the churn behavior of the  customers and hence paving path for better customer relationship management in today’s competitive banking environment.Another aspect of the project is fraud detection. Fraud means obtaining services/goods and/or money by unethical means, and is a growing  problems all over the world nowadays.  As a recent example  -  Himalayan Bank in Nepal has suffered from a kind of fraud in debit cards and it has stopped all the transactions related to debit card all over the branches in Nepal. Fraud deals with cases involving criminal purposes that, mostly, are difficult to identify. Credit cards are one  of
the most famous targets of fraud. Other targets are personal loan, home loan and retail. Our
system will identify the different types of fraud and notify the concerned  authority about the fraud. We will implement different types of data mining algorithm in order to catch the anomaly in credit and debit cards.


Scope of data warehouse in banking system in context of Nepal

1.   Helps in carving the future direction of the bank and what actionable point to note for the 
2. future.
3.  Used in historical analysis, performance analytics, performance budgeting, product 
4. innovation, employee performance, customer relationship management and many others. 
5.  Detection of banking fraud such as identity theft and money laundering.
6.   Improvement in risk management for investment, loans and bankruptcies.
7.   Increase the efficiency of ATM services.

REFERENCES

1. 1.      Bhambri, Vivek, (2012). Data Mining as a Tool to Predict Churn Behavior of Customers. International Journal of Computer & Organization Trends, Vol. 2, Issue 3.
2. 2.      Bolton, R. J. and Hand, D. J., (2002). Statistical Fraud Detection: A Review. Statistical Science, Vol. 17, No. 3, 235–255.
3. 3.      Ogwueleka, F.N., (2011). Data Mining Application in Credit Card Fraud Detection. Journal of Engineering Science & Technology, Vol. 6, No. 3, 311-322.
4. 4.      Lane, P.; Schupmann, V. and Stuar, I. (2007). Oracle Database Data Warehousing Guide, 11g. Oracle Inc.
5. 5.      Tsiptsis, K. and Chorianopoulos, A. (2009). Data Mining Techniques in CRM: Inside Customer Segmentation. A John Wiley and Sons, Ltd., United Kingdom.
6. 6.      Yakuel, P. (2012). Optimove Learning Center. http://www.optimove.com/churn-prediction-prevention.aspx [accessed on 24/01/2013].
7. 7.      Bhattarai, D., Sharma D.R. (2012). Banking and Financial Statistics. Nepal Rastra Bank, Kathmandu, Nepal.  

Download the full copy of the proposal here