Business Analytics and Data Mining Techniques for Banking Industry

This presentation explores the applications of business intelligence analytics and data mining in the banking industry for decision making. It discusses the techniques used in the banking industry, such as association, clustering, forecasting, and classification. It also highlights the challenges faced in implementing data mining and business analytics in the banking industry.

TECH7406- Business Intelligence
and Data Warehousing
Research Report
Individual Report – 1 Banking Industry

Introduction
Main objective of this project is to prepare the report to analysis the
business analytics and data mining techniques to the selected domains, here we are
chosen the banking industry and, also this report is used to review the applications
of business intelligence analytics and data mining in banking industry for decision
making contents. This project is used to enable user to understand the how business
intelligence analytics and data mining technique revolutionize business today.

Business Analytics and Data mining
Techniques for Banking Industry
 The business analytics and data mining techniques are playing an imperative role in banking
industry.
 The banking industry is a highly competitive and it is sensitive to economic and political conditions
in over the world because it has numerous risks and key strategy for many banks is to improve their
performance by increasing the revenues and reducing the costs (Biswas and Bishnu, 2015).
 Basically banking industry, the data acts a main asset of the organization since valuable interesting
and knowledge patterns are hidden in the data.
 So, the data mining is used to discover the process of analyzing the data from various perspectives
and summarizing it into the useful information which is used to increase the cuts, costs and
revenues.

Business Analytics and Data mining
Techniques for Banking Industry
The data mining techniques are listed in below.
 Association - The correlation and association is used to determine the
frequently used data items in the large data set. It is also finding the patterns
where one event is connected to another event.
 Clustering - It can be said as identification of similar classes of objects. It is
used to combining the transactions with similar behavior into customer with
same set of the transaction into one group. It is used to provide the effective
mean of unique the groups.

Business Analytics and Data mining
Techniques for Banking Industry
 Fore casting - It is regression technique which can be modified for prediction. It
is sued to model the relationship between the more than one dependent and
independent variable. It is used to discovering the patterns form which one can
create the sensible predictions.
 Classification - It is most commonly applied data mining technique which is
used to employs the set of the pre - classified to develop a model that can
classify the population of the records at large. It is used to estimate the accuracy
of the classification rules (Jayasree, 2013).

Applications of the Banking Industry
The application of Data mining techniques in banking industry is represented as below.
Customer Relationship Management
The system of customer relationship management for banking is very helpful to
create the brand value and understand and identify the customer needs by providing the
timely targeted and relevant information which is used to add the value to their customers. It
is used to create the customer profiling to group the likeminded customers in to the group. It
is also used to discover the new customers by using the clustering technique and it retain the
customer to perform the market basket analysis which is used to discovering the co-
occurring items.

Applications of the Banking Industry
Fraud Detection
It used to detect the actions of fraud in baking because the fraudulent is
an increasing concern for various businesses. The fraud detection is used to detect
and report the fraudulent actions by using the two approaches. The first approach is
to detect the bank taps the data warehouse of the third party and use the data
mining to identify the fraud detections. The second approach is used to identify the
fraud pattern in banking industry (Jisha and Kumar, 2018).

Business Opportunities
The data mining techniques are used to add the business value and generated the
new business opportunities within the banking industry. The most widely used areas of the
data mining for the industry is marketing.
In marketing, the banking department is uses the data mining and business
analytics techniques which are used to analyze customer databases and also it analyzed the
collected data to decide the consumer behavior with reference to price, distribution and
product channel.
It can analyze the past trends, determine the present demand and forecast the
customer behavior of various products and services in order to take more business
opportunities and anticipate behavior patterns.

Challenges
The challenges that associated with the application of the data mining
and business analytics for banking industry is illustrated as below.
 Lack of transparency
 Small size of revenues do not justify the additional costs
 Traditional banking habits
 Security and Fraud instances
 Consumer Expectation
 Regulatory Pressure

Conclusion
This project was successfully prepared the report which is effectively
analyses the business analytics and data mining techniques to the Banking industry
and, also this report is also effectively review the applications of business
intelligence analytics and data mining in banking industry for decision making
contents. This project is used to enable user to understand the how business
intelligence analytics and data mining technique revolutionize business today.

References
 Biswas, P. and Bishnu, P. (2015). Application of Data Mining and CRM in
Banking Sector Medical Insurance. International Journal of Innovative
Research in Computer and Communication Engineering, 03(01), pp.38-46.
 Jayasree (2013). A REVIEW ON DATA MINING IN BANKING
SECTOR. American Journal of Applied Sciences, 10(10), pp.1160-1165.
 Jisha, M. and Kumar, D. (2018). A CASE STUDY ON DATA MINING
APPLICATIONS ON BANKING SECTOR. International Journal of Computer
Sciences and Engineering, 06(08), pp.67-70.