a novel approach for analyzing bank features by using neural clusters

7/29/2019 A Novel Approach for Analyzing Bank Features by using Neural Clusters

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International Journal of Computational Intelligence and Information Security, December 2012 Vol. 3, No. 10

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A Novel Approach for Analyzing Bank Features by using Neural Clusters

Bibhuti Bhusan Dash and Chetan Vashistth

Abstract

Banks plays an important role in every ordinary and extraordinary person. Generally there is a great doubt

in mind while selecting a bank. There are a number of good banks in market. It is common factor that every bank

cant fulfill every type of need.

Here we are proposing an artificial network which can guide the leman user. We are working on a

number of features/ needs which we came to know while our survey. We interviewed a lot of customers and bank

personnel too. Our network takes those parameters of bank as input and results the clusters of good banks on the

basis of various features. By carefully reading those clusters we can conclude the good banks for any specific

type user.

Keywords: Large Bank Database, Raw Query, Intelligent Query, Expert System, Fuzzy Clusters, Neural

Clusters

1. Introduction

This document is written as a sequel of our previous paper Intelligent Query by Using Fuzzy Set over

Banking and Finance Databases. We have proposed an idea of using fuzzy queries for selection of right bank in

a large database. Here we are taking the result of that application as input for our current neural network.

Present time is a race against time. People generally dont have time for survey or advices from experts. In

this type of scenario it is quite difficult to find out the right bank. The condition goes worse when customer is

leman about internet or computer. Internet is the largest hub of information available today. Hence it is very hard

to dig out the right information from internet, the promotion websites and advertises makes the condition worseever.

This approach providing a platform to customer for giving his query in a normal language and our expert

system will parse that query and give the desired output without any technical labor.

Fig.1 is giving the overview of system. The system will look like this from outside. End user is giving his

inputs in the form of raw query. Our system is taking that raw query as a input and transferring that to query

parser. Query parser parses query on the basis of keywords in query. Parser gives input to query group selector,

this system also has a neural cluster network working. This module assigns a group after matching the keywords.

If query mismatches then this module is also trained for making new group of queries. After assignment of group

to any query, Neural Cluster unit works on our selected features and gives output at Display Unit.


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This complete paper is further divided into a number of sections namely, previous work done, proposed

methodology, implementation, experimental results, discussion and analysis and we have conclude with the

future scope of this research.

2. Previous Work Done

We have reviewed a number of research papers written by various researchers in the field of expert systems,

fuzzy clusters and neural clusters. Every researcher concludes with some new theory, but only statistical methods

are used for banks and finance sector so far. Statistical methods are giving remarkably good results for a well

defined structured query. When we move to raw query these systems are unable to work with. Time series is the

tool which has been used most so far. Regression and correlation have been applied by a number of researchers.

We have reviewed a number of techniques used by management students, these techniques helped in

understanding the concept of banking and need of customer.

In our previous paper we have find out a number of features in banks on the basis of questionnaire. We used

fuzzy means of query for solving the problem. There was also the same problem that was the scrutiny of queries.

We have worked on attributes by taking the limits manually for our previous system that increases the human

intervention and also the probability of error.

We have reviewed a large literature on clusters for selecting the right one in our application [1-7]. We havestudied both type clusters Fuzzy clusters and Neural clusters as well. After analysis we got neural clusters are

more useful for automating the system.

3. Proposed Methodology

Our proposed methodology is using Neural Clusters for selecting the query type and further for classifying

the features of banks. We are working by interviewing the customer from questionnaire. After collecting the data

from surveys we find out a number a number of features of banks. Our proposed work contains a number of

sections.

III(a): Preparation of questionnaire: after surfing a lot on internet and with the help of management

institutions we prepared a questionnaire for interviewing customers. We took help from many social networking

sites like facebook, orkut, linkedin, yahoo etc for completion of our survey. Our questionnaire consists of

multiple questions like:

What is the expectation from a bank?

What quality should be there for a satisfactory investment?

What is the importance of net banking in simple life?

and many more.

III(b): Collection of data: there are two main information which we have to collect for our application.

The survey data and data about banks according to needs. The survey part is done by our students and

by us also. Social networking sites helped a lot in collection of survey data. As far as when we concern about

bank database, the banks have their own websites from which we can collect data. We also visited a number of

banks for fulfillment of our database needs.

III(c): Making the queries: on the basis of survey data we have made a large number of queries, which canoccur in real time. Our system is working on keywords in any realtime query. After breaking the query after

parsing that we match that one to our database. In case of database mismatch or any new query our expert system

find out the nearest cluster and fit that to other clusters. Expert system completely works on relative membership

in various clusters.

III(d): Neural Clusters used in application: Here we are explaining the basics of clustering approach used in

various applications.

Fuzzy C-Means(FCM) : Fuzzy C-Means clustering model can be defined as follows:

min (1)


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ST: (2)

where, is the degree of belonging of the data to the cluster, is the distance between thedata and the cluster center, m is the degree of fuzziness, c is the number of clusters, and N is the number of

data.

Fuzzy clustering algorithm: Clustering, as a basic approach, on some unbalanced data set X =

is partitioning x into c subsets such that 1 < c < n. Each point in x is a vector in n-

dimensional space. In most of the clustering methods, each data point belongs to at most one cluster. We define

c-partition of x as a cn matrix representing memberships of each data point to all clusters. We show the matrix

as = , i=1c, j=1.n. in k-means algorithm, U is defined by the equation (13).

(3)

Possibilistic clustering approach: Possibilistic clustering approach named PCM was proposed to overcome

the limitations of FCM model. Instead of probabilistic memberships , the resulting partition of data can be

interpreted as a possibilistic partition and each membership value may be interpreted as possibility or degree ofcompatibility. Equation (14) represents definition of matrix U based on possibilities.

(4)

By minimizing the objective function, update formulas for ui, j, i(center for cluster i) are indicated in

equations (6,7,8).

(5)

(6)

(7)

(8)

Cluster prototype leads us to an iterative computing which is shown in the equation below.

(9)

Where, is new center for cluster i. Iteration continues while < . For i on the right

side of the above equation, values of previous iteration are used. A cluster is attracted by data assigned to it and

repelled by the other clusters.


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4. Implementation

Implementation part consists of a number of steps:

1. making queries from survey data2. making bank database on basis of features3. implementing neural cluster algorithm

IV(a): Making queries from survey data: the very first task is making appropriate queries from survey data.

The main motive of this application is to design a platform on which any leman user can find answer of his

queries. Hence in other words this application is providing an interface between technical and non-technical

world. From our questionnaire and interview we have collected all raw data so far. Our main motive here is to

make some logical queries from that raw material.

We are making a number of dummy queries on the basis of questionnaire. This query database is an

incremental database, and with the experience of any new problem we can add some new query to it.

IV(b): Making bank database on the basis of queries: on the basis of survey data and query database we

have selected a number of features in banks, we also have worked on those selected features in our previous

paper.

These selected features are:a). Age of bank: the very first data about any bank is the age of that particular bank.

b). Number of customers: the number of customers also play an important role in reliability factor of any

bank.

c). Government Share: the share of government also plays an important part in reliability and policies of

any bank.

d). Number of branches: number of branches of any bank in total increases the reach ability of customers to

it. Bank branches in market or in any crowded area increases customer in its own way.

e). Interest rates: interest rates play its own role in selection of banks.

f). Customer satisfaction policy: the policies applied by the banks for various customers also play

significant roles in attracting the customers. Less processing fees and time in loans and less interest rates for

existing customers and easy account opening and various gift schemes attract customers.

g). Modern facilities: the modern facilities also giving reliability and ease to customers. Easy debit cards

and credit cards and net banking are the part of these facilities.

h).Customer satisfaction and delivery at home services: this is a latest trend of banks of opening accounts at

your location. A new scheme is also came in consideration that banks also used to send their representative at

your home for making drafts and also gives delivery at home. These types of services are making market more

competitive.

i). Value of unit: now a days every bank has public shares in stock market. The value of unit share of any

bank also increases reliability. The track of some past years also make significant changes.

j). Upgradation support: some banks also provides upgradation support to existing customers. By using any

customer can use other services too without changing banks.

k). Feedback by customers: there are a lots of websites available with works on feedbacks given by various

customers. These feedbacks also play a part in customers mind.

l). Budget of customer: instead of above discussion budget of customer plays most significant role in

selection of bank. As according to survey we came to know some international banks maintains minimumbalance INR 10000 or more, while some Indian banks are still opening accounts with 0 balance.

m). Number of ATMs : after our previous survey we just come to know that number of ATMs also play a

part in selection of banks.

n). Customer care support: with the increased use of ATMs, the problem related to with ATMs and with

online transactions is also increasing. Hence the customer care support to customers also playing a second fiddle.

We have seen all the parameters which are responsible for a bank to get selected. On the basis of above

features we can make a database which can contain the data for all banks.

IV(c): Implementing Neural Cluster Algorithm: the work in this implementation has a number of phases in

it.

The very first task is to maintain query database in our network. From our survey data we already have a

large number of exact queries. We are maintaining a those queries as exact query and also taking them for

substring match for matching of nearby queries viz. most reliable bank for investment and best bank for

investment are substrings of same query in our theory. For better results we are breaking our queries into query


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keywords.

We are preparing queries in form of vectors containing keywords. As a query banks in Rajnagar or banks

nearby rajnagar these both queries belongs very much nearly. Actually here the concept of intellisense,

implemented in google search, is used so far. It is more clear by following example.

The very first check is at query parser. Query parser breaks query into keywords, then these keywords are

mapped with unique ids. These unique ids used as vector component for our neural network. By using thatneural cluster we implement intellisense to our application.

This can be understand in following manners, as any query encounter with system. good banks for lower

budget it will break into: good, banks, lower, budget. Then in second step we will assign unique ids to them

namely a, b, c, d..etc.

Hence this query makes a complete vector abcd hence we can apply neural cluster to get relative distance of

any new query from existing query. This technique decreases the chances of miss.

This is for guiding our network to handle with queries. After handing with queries the next big task is to

maintain banks information with our network.

We are using the fuzzy technique used in our previous paper for memberships of various banks in the fuzzy

set of different features. As example we are taking a set for feature age of banks. We are considering a dummy

example of 12 banks here. Namely b1, b2, b3.. the data of these twelve banks is as follows. b1-12, b2-5, b3-3,

b4-19, b5-8, b6-29, b7-1, b8-9, b9-11, b10-7, b11-3, b12-2.

Fig.3 is showing fuzzy set for above bank detail. This fuzzy membership function ranges 0-30 years. All

of our banks lie under this range. Now each bank has a relative membership in this membership set. Viz. b1-has

40% new and 60% avg membership in this set. Hence if we consider new as a keyword then it has .4 as

membership and if we have avg as a keyword then it has .6 as membership.

In similar passion we can find the memberships of all the banks. This is for one feature only; similarly

all features can be traced through fuzzy set.

These membership values will make vector for each bank. In our input matrix each row will represent adifferent bank and each column will show any specific feature.

This matrix will work as input to neural cluster network. The structure of vector is

Xa = {x1,x2,} 0


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single vector entity.

Distance chart between neighbors

Fig 6 is representing the relative distance chart between various vectors i.e. between various banks on the

basis of their cumulative characteristics. On the basis of distance we can decide the similarity of bank with any

other bank. At that time when our query is coming we take a perfect bank for that feature and match all banks to

that. By calculating the relative distance we can select or reject any specific bank.


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Density of input for various vectors

Fig 7 is showing one of the most important results here. This diagram is showing the density chart for each

vector in input. By this diagram it is clearly shown how similar is any input with any other input. The density can

also be calculated in statistical view with the help of some higher simulators. By that statistical data we cancompare any bank with respect to various features.


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Sample hit chart

Fig 8 is all about the number of hits at various locations in plane. By this chart we can take an idea about

how many banks are showing same features.

SOM input positions

Fig 9 is showing the chart for various weight positions in plane.


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Here is some matlab code (reusable script), which is generated by this experiment on dummy database of

bank.

Script for loading datasheet into workspace:

function [newData1] = importfile(fileToRead1)

%IMPORTFILE(FILETOREAD1)

% Imports data from the specified file

% FILETOREAD1: file to read

% Auto-generated by MATLAB on 31-Oct-2011 01:32:48

% Import the file

sheetName='Sheet1';

[numbers, strings] = xlsread(fileToRead1, sheetName);

if ~isempty(numbers)newData1.data = numbers;

end

if ~isempty(strings)

newData1.textdata = strings;

end

Script for training Neural Network and for generating output:

% Solve a Clustering Problem with a Self-Organizing Map

% Script generated by NCTOOL

% Created Mon Oct 31 01:34:12 PDT 2011

%

% This script assumes these variables are defined:

%


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% data - input data.

inputs = data;

% Create a Self-Organizing Map

dimension1 = 10;dimension2 = 10;

net = selforgmap([dimension1 dimension2]);

% Train the Network

[net,tr] = train(net,inputs);

% Test the Network

outputs = net(inputs);

% View the Network

view(net)

% Plots

% Uncomment these lines to enable various plots.

%figure, plotsomtop(net)

%figure, plotsomnc(net)

%figure, plotsomnd(net)

%figure, plotsomplanes(net)

%figure, plotsomhits(net,inputs)

%figure, plotsompos(net,inputs)

Fig 10 and fig 11 are showing the simulation diagram generated from experiments. Fig 10 is showing neural

network diagram and fig 11 is showing simulink diagram for the application.


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6. Discussion and Analysis

The previous work done by us is totally based on fuzzy logic applications. That work was not giving somuch satisfactory results. The final output with fuzzy logics was bit confusing. This time we have used fuzzy

results in making the feature vectors. We have used neural clusters in two fold hence expert system has worked

twice. The lower and upper limits are set automatically each time when application runs. The results are more

nearer to exact in each fold as this is a self learning network. New queries are prepared itself if any new problem

arises.

We have also reviewed a number of other methods for the same purpose. Statistical regression and time

series are two main in them. These methods are quite controlled in result and result is also very near to exact but

these methods are only for large database. If we dont have sufficient data for any new bank then these methods

screen outs that bank. This is a big problem in implementation as a number of banks are also giving remarkably

good services to customers. Hence false negative rate is quite high for statistical methods. Our method is

providing quite good opportunity for even new banks too as it is working on features.

7. Conclusion and Future Scope

After studying a lot of literature in this field and seeing the results from neural cluster we can conclude that

expert system are quite useful in this field.

Results are quite demanding for new banks too. The main feature of this method is diminishing the number

of false negatives. By minimizing the false negatives and false positive the results are justified. The user

intervention from application is just like an end user hence any leman can also use this application easily.

Various banks can also justify their position on the basis of weight chart provided by this application.

Various banks can make focus on the features in which they have less dense chart position. Banks can also

improve their performance.

In future we can also implement this application in share market and in commodity too.

Even after giving the best efforts nothing can be best in this ever changing world. Hence there are always

some places where any application lacks. There is ever a scope of modification in everything.

There is chance of using genetic algorithm for matching the pattern between various bank vectors. There is

a chance it might work well in preparation of new crosses of query strings.

8. Acknowledgements

There is a mighty contribution of a number of people. Giving their names here will be an honor to me. We

are heartily thankful to MBA department of our college for making the market research. We also want to mention

the name of social networking sites like linkedin and facebook here for the success of surveys.

The guidance and contribution of Mr. Ajay Agarwal is unforgettable. We are also thankful to our families

for their valuable support and their sacrifice.

REFERENCES

[1] T.K. Ho. The random subspace method for constructing decision forests. IEEE trans Pattern Anal. Mach. Intel. 20 (8) (1998) 832-844.[2] C Guerra Salcedo. D. Whitley, Genetic Approach to Feature Selection for Ensemble Creation. GECCO-99, 1999,

http://www.cs.colostate.edu/genitor/pubs.html

[3] B.D. Ripley, Pattern Recognition and Neural Networks . Cambridge University Press, New York, 1996.[4]J. Hertz. A. Krogh, R. Palmer, Introduction to the Theory of Neural Computation. Addision Wesley Publishing Company, 1991.[5]Mehdi Salkhordeh Haghighi, Hadi Sadoghi, Abedin Vahedian, A Hierarchical Possibilistic Clustering, International Journal of

Computer Theory and Engineering, Vol. 1, No. 4, October 2009 1793-8201.

[6]M.H. Fazel Zarandi, M. Zarinbal, I.B. Turksen, Type-I Fuzzy Possibilistic C-Mean Clustering, IFSA-EUSFLAT 2009.


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[7] M.H. Fazel Zarandi, M. Zarinbal, I.B. Turksen, Type-II Fuzzy Possibilistic C-Mean Clustering, IFSA-EUSFLAT 2009.

a novel approach for analyzing bank features by using neural clusters

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