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#0183;#32;Data mining is becoming strategically important area for many business organizations including banking sector. It is a process of analyzing the data from various perspectives and summarizing it into valuable information. Data mining assists the banks to look for hidden pattern in a group and discover unknown relationship in the data.
Using data science in the banking industry is more than a trend, it has become a necessity to keep up with the competition. Banks have to realize that big data technologies can help them focus their resources efficiently, make smarter decisions, and improve performance.
Banking industries adopt the data mining technologies in various areas especially in customer segmentation and profitability, Predictions on Prices/Values of different investment products, money market business, fraudulent transaction detections, risk predictions, default prediction on pricing.
#0183;#32;Data mining is becoming strategically important area for many business organizations including banking sector. It is a process of analyzing the data from various perspectives and summarizing it into valuable information. Data mining assists the banks to look for hidden pattern in a group and discover unknown relationship in the data.
#0183;#32;Classification, as one of the most popular data mining techniques, has been used in the banking sector for different purposes, for example, for bank customer churn prediction, credit approval, fraud detection, bank failure estimation, and bank telemarketing prediction.
This paper explores and reviews various data mining techniques that are applied in the banking sector but also provides insight into how these techniques are used in different areas to make
Application of Data Mining in Banking Sector
Data Mining plays important roles in many organizations especially the customer service oriented establishments like banks. It is helpful in scrutinizing the collected data and delivering it into...
#0183;#32;CONCLUSION Data mining is a tool enable better decisionmaking 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.
Role of Data Mining in Banking Sector
USE OF DATA MINING IN BANKING SECTOR SlideShare
Data mining in banking industry Describes how data mining can be used. Data mining is the process of analyzing data from multitude different perspectives and concluding it to worthwhile information. Information can be used to increase revenue and cut costs. Nowadays we live in a modern era.
#0183;#32;Credit scoring systems and fraud protection techniques are wellknown applications of data mining analytics in banking industry. A modern trend in this group is extending the volumes of information that is used as predictors in data dining models.
Data mining is becoming strategically important for banking sector as well. It analyzes the data from various perspectives and summarizes it into valuable information. Data mining assists the banks to look for hidden pattern in a group and discover unknown relationships in the data.
set of data, including historical base, could be interpreted and analyzed. Application of data mining in the banking sector In the banking sector, there are several applications of data mining credit analysis, cross selling, customer profitability and segmentation, fraudulent transactions, ranking investments, most profitable customers on cross selling and credit card, and the like.
Another popular area where data mining can be used in the banking industry is in fraud detection. Being able to detect fraudulent actions is an increasing concern for many businesses; and with the help of data mining more fraudulent actions are being detected and reported.
Data mining techniques have a wide applicability in retail banking and are used in customer relationship and marketing management, risk management, fraud detection, money laundering prevention. Marketing and Customer Relationship Management (CRM)
#0183;#32;To process these data in an effective manner, data mining techniques and methods are pertinent. By using these techniques several interesting patterns and knowledge base can be retrieved. This article analyzes the various data mining techniques and concepts that can be applied to banking sector to enhance its performance.
Data mining techniques have a wide applicability in retail banking and are used in customer relationship and marketing management, risk management, fraud detection, money laundering prevention. Marketing and Customer Relationship Management (CRM)
Data Mining encompasses many different techniques and algorithms. These differ in the kinds of data that can be analyzed and the kinds of knowledge representation used to convey the discovered knowledge (Mabroukeh and Ezeife, 2010). Application of Data Mining in Banking Sector There are various areas in which data mining can
Simply, data mining is the process of finding patterns, trends, and anomalies within large data sets to take adequate decisions and to predict outcomes. Now, there is an enormous amount of data available anywhere, anytime. But this data is worthless for the management
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