E-commerce Models For Banks’ Profitability
Author(s)
V. Aggelis
Abstract
The use of data mining methods in the area of e-business can already be
considered of great assistance in prediction, knowledge management, and
decision support. In e-commerce in particular there are a significant number of
metrics which have been tested and used for measuring interesting parameters. In
most cases these parameters are in relation with customer habits and customer
profitability. Nowadays, many merchants cooperate with banks for authorizing
credit card transactions in order to purchase products. Banks are also interested,
in measuring the profit and the strength of this cooperation. In this paper we
introduce two models with this scope, (a) a merchant clustering model and (b) a
bank revenue predictive model. According to the first model, a bank scores and
classifies its cooperating merchants using a number of parameters, while in the
second model, a bank predicts its revenue from e-commerce transactions.
Keywords: data mining, clustering, k-means algorithm, predictive model, linear
regression.
1 Introduction
Banking or financial data treatment is generally conducted using several data
mining methods such as Linear Regression, Clustering, Classification and others
aiming at the development of patterns, rules, predictive models and finally
forecasting. These methods produce interesting as well as useful results.
However, not all kinds of results lead to rigid conclusions.
From this point of view the data miner and the judgment of the user are
essential in evaluating the results and especially the predictive models efficiency.
Therefore the co-operation between people expert in data mining and others with
good knowledge of the data sets is important leading to proper evaluation of the
predictive model. In the banking area this combination is definitely necessary
due to the singularity of bank data as well as bank market rules.
Keywords
data mining, clustering, k-means algorithm, predictive model, linear
regression.
Related Book
Other papers in this volume
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