Journal of Intelligent Learning Systems and Applications, 2011, 3, 55-56
doi:10.4236/jilsa.2011.32007 Published Online May 2011 (http://www.SciRP.org/journal/jilsa)
55
Editorial: Intelligent Learning Systems in Banking
and Finance
Vincenzo Pacelli*
Faculty of Economics, University of Foggia, Foggia, Italy.
Received July 10th, 2010; revised October 1st, 2010; accepted December 1st, 2010.
The non-linear and often obscure relations that govern
the economic and financial variables, the presence of sig-
nificant amounts of data and the failures of the conven-
tional mathematical and statistical models are only some
reasons which should encouraged a growing development
of the studies on the application of the intelligent learn-
ing systems in banking and finance.
The literature on the applica tion of artificial in tellig e nc e
systems (such as neural networks, expert systems, fuzzy
models and genetic algorithms) to the fields of banking
and finance has explored various aspects, which can be
classified according to the following taxonomy in four
macro-categories:
1) studies on the application of these models on time
series forecasting;
2) studies on the application of these systems within
the classification and discrimination of economic phe-
nomena, with particular attention to the management of
credit risk;
3) studies on the application of these systems within
the approximation of economics function, with particular
attention to the phenomenon of pricing of financial pro-
ducts;
4) studies on the application of these models on the
portfolio management, with a particular focus on the
portfolio optimization.
The purpose of this special issue of the JILSA is to
analyze the role of the intelligent learning systems and
algorithms in banking and finance, both in a theoretical
and empirical point of view.
All papers went through a rigorous peer review pro-
cedure and each paper received at least three review re-
ports. After this rigorous reviewing process, five papers
were accepted for publication in this special section. All
the five papers analyze the application of the intelligent
learning systems and algorithms in banking and finance
by empirical analysis.
The first paper by V. Pacelli, V. Bevilacqua and M.
Azzollini develops and tests empirically an artificial
neural network model to forecast the trend of the ex-
change rate Euro/USD up to three days ahead of last data
available. The variable of output of the ANN designed is
then the daily exchange rate Euro/Dollar and the fre-
quency of data collection of variables of input and the
output is daily. By the analysis of the empirical data, the
authors conclude that the ANN model developed can
largely predict the trend to three days of exchange rate
Euro/USD.
The second paper by B. Alexandrova-Kabadjova, E.
Tsang and A. Krause analyses the dynamics of competi-
tion in the payment card market through a multi-agent
based model, which captures explicitly the commercial
transactions at the point of sale between consumers and
merchants. Through simulation, the authors attempt to
model the demand for payment instruments on both sides
of the market. Constrained by this complex demand, a
Generalised Population Based Incremental Learning
(GPBIL) algorithm is applied to find a profit-maximizing
strategy, which in addition has to achieve an average
number of card transactions. In this study the authors
compare the performance of a profit-maximizing strate-
gies obtained by (GPBIL) algorithm versus the perform-
ance of randomly selected strategies. They found that
under the search criteria used, GPBIL was capable of
improving the price structure and price level over ran-
domly selected strategies.
The third paper by Khaled Assaleh, H. El-Baz and S.
Al-Salkhadi presents two prediction models for predict-
ing securities’ prices. The first model is developed using
back propagation feed forward neural networks. The
second model is developed using polynomial classifiers
(PC), as a first time application for PC to be used in stock
prices prediction. The inputs to bo th models are identical,
and both models are trained and tested on the same data.
The study is conducted on Dubai Financial Market as an
emerging market and applied to two of the market’s lea-
*Guest Editor of the Special Issue of the JILSA “Intelligent Learning
Systems in Banking and Finance”.

Editorial: Inte l ligent Learning Systems in Banking and Finance 56
ding stocks. In general, both models achieved very good
results in terms of mean absolute error percentage. Both
models show an average error around 1.5% predicting
the next day price, an average error of 2.5% when pre-
dicting second day price, and an average error of 4%
when predicted the third day price.
The fourth paper by M. Gorgoglione and U. Panniello
proposes a number of models which can be used to gen-
erate marketing actions, and shows how to integrate them
into a model embracing both the analytical prediction of
customer churn and the generation of retention actions.
The benefits and risks associated with each approach are
discussed. The paper also describes a case of application
of a predictive model of customer churn in a retail bank
where the analysts have also generated a set of personal-
ized actions to retain customers by using one of the ap-
proaches presented in the paper, namely by adapting a
recommender system approach to the retention problem.
The fifth and last paper by V. Pacelli and M. Azzollini
develops and tests empirically an artificial neural net-
work model to forecast the credit risk of a panel of Italian
manufacturing companies. In a theoretical point of view,
this paper introduces a detail literature review on the ap-
plication of artificial intelligence systems for credit risk
management. In an empirical point of view, this research
compares the architecture of the artificial neural network
model developed in this research to ano ther one, built for
a previous research with a similar panel of companies,
showing the differences between the two neural network
models.
I would like to thank Professor Stefano Dell’Atti for
his guidance in my research activity and for his precious
suggestions.
At last, I would like to thank the editor in chief of the
JILSA (Xin Xu), my co-editor of this special issue (V.
Bevilacqua) and all of the authors (M. Azzollini, V.
Bevilacqua, M. Gorgoglione, Hazim El-Baz, B. A. Ka-
padjova, A. Khaled, E. Krause, V. Pacelli, U. Pan-
niello, E. Tsang, Saeed Al-Salkhadi) and reviewers (E.
Angelini, V. Bevilacqua, S. Dell’Atti, A. Malinconico, F.
Miglietta, V. Pacelli, S. Sylos Labini) who have made
contributions to this special issue.
Vincenzo Pacelli
Guest Editor
JILSA