Intelligent Information Management, 2012, 4, 251-260 Published Online October 2012 (
Fuzzy-Neuro Model for Intelligent Credit
Risk Management
Elmer P. Dadios1, James Solis2
1Department of Manufacturing Engineering and Management, De La Salle University, Manila, Philippines
2College of Computer Studies, De La Salle University, Manila, Philippines
Received August 30, 2012; revised September 30, 2012; accepted October 8, 2012
This paper presents hybrid fuzzy logic and neural network algorithm to solve credit risk management problem. Credit
risk is the risk of loss due to a debtor’s non-payment of a loan or other line of credit. A method of evaluating the credit
worthiness of a customer is complex and non-linear due to the diverse combinations of risk involve. To address this
problem a credit scoring method is proposed in this paper using hybrid fuzzy logic-neural network (HFNN) model. The
model will be implemented, tested, and validated for individual auto loans using real life bank data. The neural network
is used as the learner and the fuzzy logic is used as the implementer. The neural network will fine tune the fuzzy sets,
remove redundant input variables, and extract fuzzy rules. The extracted fuzzy rules are evaluated to retain the best k
number of rules that will give final and intelligent decisions. The experiment results show that the performance of the
proposed HFNN model is very accurate, robust, and reliable. Comparison of these results to other previous published
works is also presented in this paper.
Keywords: Fuzzy Logic; Neural Networks; Fuzzy-Neuro Model; Credit Risk Management
1. Introduction
Credit scoring is a method of evaluating the credit wor-
thiness of a customer. A credit scoring model is built to
assist credit analysts to decide whether a new loan should
or should not be granted [1,2]. The model is used as a
gauge for every applicant’s profile. If a profile is equal or
better than the model, the account is predicted to be
“good”. Otherwise, the account is predicted to be “bad”.
There have been several automated approaches presented
before to solve credit scoring problem. Among them are:
1) Rule-based, 2) Statistical-based, 3) Genetic Algorithm
(GA), 4) Neural Networks (NN). The rule-based (or
knowledge based) approach is believed to be the easiest
for a credit professional to formalize and the least expen-
sive to implement [3]. It uses a set of rules derived from
past credit experiences. It provides consistency to the
account review process since it is an automation of the
traditional risk assessment process [2]. But one major
problem with rule-based scoring is the difficulty of de-
termining the source of error if the system makes a series
of bad decisions [3], hence, also difficult to improve. The
statistical-based credit scoring method uses linear dis-
criminant analysis and logistic regression. Thus, it re-
quires specialized education, training and experience.
Also, the traditional regression techniques cannot be fully
automated. It is labor-intensive and time consuming to
design and updates the model [4]. Limited in its effect-
tiveness as a long-term decision tool, the credit scoring
models have to be updated and improved as trends in
customer behavior changes by which the performance of
the system falls below the acceptable level of prediction
One of the successful techniques used to solve the
credit scoring problem is the neural networks (NN) [5,6].
It is believed that NN provide an essential technology for
a faster and more effective tool for credit scoring [7]. NN
are capable of modeling very complex mathematical and
logical relationships that are unknown to the credit ana-
lyst and NN are able to model linear and non-linear rela-
tionships. In terms of accuracy, in most cases, the rule-
based and statistical-based credit scoring systems cor-
rectly classifies at 74% [8], commercially available NN-
based at 75% - 80% [8,9] and Genetic Algrithm between
72% to 74% [9].
2. The Fuzzy Logic and Neural Networks
Fuzzy Logic is a system that imitates the way a human
being thinks [10-13]. Unlike conventional logical sys-
tems, fuzzy logic does not need actual theoretical data
opyright © 2012 SciRes. IIM
and input-output relations to solve a problem. Rather, it
defines complex systems with linguistic descriptions.
The fuzzy logic system (FLS) contains four components:
the fuzzifier, rules, inference engine and defuzzifier. The
fuzzifier maps crisp input numbers into fuzzy sets. Data
obtained from the outside world is converted into data
understandable by the system. Rules are linguistic vari-
ables expressed in IF-THEN statements. The inference
engine maps fuzzy sets into fuzzy sets. It also handles
situations wherein two or more rules are combined. The
defuzzifier maps the fuzzy output sets into crisp outputs.
The crisp output is an output that is easily understood by
the outside environment.
Neural Network (NN) is an information processing
paradigm that is inspired by the way biological nervous
systems, such as the brain, process information [14-16].
The key element of this paradigm is the novel structure
of the information processing system. It is composed of a
large number of highly interconnected processing ele-
ments (neurons) working in unison to solve specific
problems. NN, like people, learn by example and solve
problems for a specific application, such as pattern rec-
ognition or data classification [16].
It has been suggested that, since all the other methods
are known to possess definite advantages and disadvan-
tages [2,17], the best approach is to combine the methods,
taking advantage of the strengths and, thus, creating a
hybrid approach to the overall credit scoring process.
3. Hybrid Fuzzy Logic-Neural Network
(HFNN) Model for Intelligent Credit
Risk Management
The hybrid fuzzy logic neural network (HFNN) model
combines the desirable properties of both neural network
and fuzzy logic to form a system that supercedes the
limitations of neural network and fuzzy logic algorithms.
The HFNN model system learns inductively from the
data using the neural network. Fuzzy rules are extracted
from the trained neural network and with the extracted
rules, previously unseen data can be discriminated whe-
ther “good” or “bad” accounts.
The HFNN model developed in this paper undergoes
two stages, namely: learning (or training) and implemen-
tation (or testing). Figure 1 shows the learning stage of
the HFNN model. The different phases that took placed
in the learning stage are: Fuzzification of the Data, Neu-
ral Network Learning, Fuzzy Sets Tuning, Pruning Input
Variables, and Fuzzy Rule Extraction & Evaluation.
3.1. Fuzzification of Data
The fuzzification of data is conducted and configured
manually. All fuzzy sets are initialized before the training
data are inputted into the neural net. The input variables
Figure 1. Architecture of the learning stage of the HFNN model.
Copyright © 2012 SciRes. IIM
are partitioned by overlapping fuzzy sets and the mem-
bership functions are initialized based on the recommen-
dation of a loan evaluation expert [18]. Note that the
specification of membership function is subjective and
may vary from different experts. However, membership
functions cannot be assigned arbitrarily. The number of
membership functions is chosen such that the resulting
fuzzy rules will be easily readable and accurate enough
to classify data. Figure 2 shows sample typical fuzzy
sets for an input variable Total Income.
The fuzzification and distribution of data are auto-
mated processes. Through the fuzzy sets, the crisp input
from 1000 samples is fuzzified one at a time and serves
as a fuzzy input to the NN [19]. To fuzzify a crisp input,
the degree of membership of the crisp input in each of
the affected fuzzy members is computed. The fuzzy
member that gets the bigger degree of member gets the
value 1, the rest of members get the value 0. In case, the
degree of member is the same for 2 adjacent fuzzy
members, the leftmost member gets the value 1. The
rightmost member gets the value 0. For example, as
shown in Figure 2, when Income = P45,000 and based in
the membership functions shown, the degree of mem-
bership of P45,000 is 7/8 in Low and 1/8 in Medium.
Hence, μ(45,000) = max(7/8, 1/8) = 7/8
So, μ(45,000) is Low, which means that the Total In-
come is Low. The data then are distributed after fuzzify-
ing the entire 1000 samples. The first 630 samples be-
comes the training set, the next 70 samples becomes the
evaluating set, and the last 300 samples becomes the
testing set.
3.2. Neural Network Learning for HFNN Model
In this research, the neural network (NN) learns purely
Figure 2. Sample fuzzy sets for an input variable total in-
from the training data presented to the model. The NN is
initialized with input neurons equal to the number of
fuzzy input members. This means that for every fuzzy
input member there should be a corresponding neuron in
the NN. Hence, for the input variable Total Income
shown in Figure 2, there must be also 4 input neurons in
the NN shown in Figure 3. The output layer has two
neurons one for good and the other one for bad [19,20].
The number of hidden neurons is 2/3 of the sum of the
total number of input neurons plus the total number of
the output neurons [21].
The neural network is trained using backpropagation
method to map the fuzzified inputs to the desired output
[14]. The optimum accuracy is achieved when the classi-
fication error is minimized for the training data and at the
same time giving the best accuracy performance for the
validation data. When the neural network attained opti-
mum classification accuracy, fuzzy rules are extracted
from the NN. The extracted rules are used by the fuzzy
system during the implementation for the classification
of the previously unseen samples.
3.3. Fuzzy Input Set Tuning
The tuning process of fuzzy input sets is conducted
automatically [19]. Only inputs that are continuous vari-
ables are tuned. Adjustment on the boundaries of the
fuzzy members of a given fuzzy set follows specific re-
straints. In this paper, the following restrictions are ada-
1) The fuzzy sets are kept overlapped with the adjacent
fuzzy sets, as shown in Figure 2.
2) When updating the parameters, the parameters a, b,
and c should remain valid; that is l a b c u must
always hold, where [l, u] is the domain of the corre-
sponding variable.
3) When updating the position parameters of the cur-
rent fuzzy member, the parameters of current member
should not become smaller than the corresponding pa-
rameters of the left neighbor or larger than the corre-
sponding parameters of the right neighbor.
4) The parameter a of the leftmost member and the
parameter c of the rightmost member remain fixed.
5) The parameter b of the current fuzzy member being
updated should not become greater than the parameter c
of the right member or smaller than the parameter c of
the left member.
For every error backpropagated from the output to in-
put fuzzy sets, the input fuzzy sets that are continuous
variables are adjusted so as to reduce the error. After
satisfactory numbers of adjustment were made with the
input fuzzy sets and yet the error does not go down, the
tuning had reached its optimum. Once the fuzzy input
sets are tuned, the NN is re-trained to an optimum accu-
Copyright © 2012 SciRes. IIM
Figure 3. Architectur e of fuzzy input to the NN during lear ning stage.
racy for the newly tuned fuzzy input sets [18,19].
3.4. Pruning the Input Variables or Fuzzy Sets
Pruning or removal of redundant input variables or fuzzy
sets will improve the readability of a fuzzy rule base ex-
tracted during the learning stage. Removal of the redun-
dant input fuzzy sets will simplify the model. Pruning
techniques are adapted from NN wherein, tests are made
for its parameters. i.e. either weights or neurons to de-
termine how the error would change if the parameter is
removed [20]. In this paper, fuzzy sets that represent va-
rious input variables in all possible combinations are re-
moved and the network classification performance is
The process in determining redundant variables is con-
ducted by systematically enabling or disabling the inputs.
When an input is enabled, its contribution is accepted
into the NN. But when an input is disabled, its value is
blocked in the NN. It is as if the input does not exist.
With all the possible combination of enabled and dis-
abled inputs, the prediction accuracy of each combina-
tion is recorded.
In this study, the 16 input variables shown in Table 1
is presented in binary and equivalent to 216 1 or 65,535
combinations. For a given sample, the HFNN model may
take in the values of 2 input variables, e.g., Age and To-
tal Income, and ignoring the values of the remaining 14
input variables. This is one unique combination of the
inputs. The ignored values of input variables are changed
to zero before being inputted to the hybrid network. This
is to neutralize the effect of the input variables that are
not included in that particular combination. The 65,535
unique combinations will be ranked according to their
accuracy performance. Among the combinations with the
same accuracy performance, say 100%, the combination
with the least number of fuzzy inputs and maximum num-
ber of rules is the most desirable combination.
3.5. Fuzzy Rule Extraction and Evaluation
From the pruned system, all possible fuzzy rules are ex-
tracted. Figure 4 shows how to extract fuzzy rules in the
HFNN model. The combination pattern 0 3 0 2 is the
exact representation of the fuzzy inputs combination both
enabled and disabled. The zero value represents disabled
input. With the output of G for Good, 0 3 0 2 G is one
fuzzy rule. Every possible combination of the fuzzy in-
puts is considered a rule. This rule is being evaluated by
NN whether the combination results is “good” or “bad”.
Note that rules are derived from unique combinations of
the enabled fuzzy inputs. There are no two rules that
have exactly the same combination of the enabled fuzzy
inputs. Hence, there are no conflicting rules that results
from these combinations.
The final rules for the rule base are selected from the
extracted rules by computing the performance of each
rule. Rules that are only responsible for a fixed numbers
of classifications may be deleted. Only a number of k
best rules are kept [19]. In here, k is a number determined
by the number of rules derived from specific combina-
tion of input variables. k is directly proportional with the
number of input variable and fuzzy member per input
variable. Given these factors, k is typically determined by
the number of rules that have “hits” at least twice in the
training set. Each extracted fuzzy rule is rated by count-
ing how many times it is used or “hit” by the training
samples. All rules that were not used were eliminated
from the list of best k rules. Rules that were used only
once are also eliminated from the list. The best k rules
Copyright © 2012 SciRes. IIM
Table 1. Snapshot of a single record in the sample data.
Input/Variable Name Variable Type *Original Record Converted RecordNo. of Fuzzy members Fuzzified Record
1) Age *Cont. 39 39 4 0100
2) No. of Dependents Cont. 2 2 4 1000
3) Length of Service Cont. 10 10 4 1000
4) Total Income Cont. 225,000 225,000 4 0001
5) Term of Loan Cont. 18 18 5 01000
6) GMI Ratio Cont. 18.04 18.04 4 0100
7) Equity Ratio Cont. 55.56 55.56 4 0001
8) Employment Type *Categ Self-employed 2 2 01
9) Civil Status Categ Married 1 6 100000
10) Gender Categ. Male 1 2 10
11) Type of Residence Categ. Owned 1 4 1000
12) Type of Neighborhood Categ. Average 2 5 01000
13) Credit Experience Categ. Up-to-date 1 14 10000000000000
14) Nature of Business Categ. Comm’ty/social service6 11 00000100000
15) Court Case Categ. w/o court case 1 2 10
16) Landline Availability Categ. w/home phone 1 4 1000
*This is the first record in the sample data. Cont.—Continuous. Categ.—Categorical.
Figure 4. Extracting rules from the HFNN model.
define the credit scoring model. Figure 5 shows the sam-
ple diagram of the process for getting the best k rules.
3.6. Fuzzy Logic Implementation Stage of HFNN
The optimized fuzzy sets achieved during the learning
stage are used in the implementation stage. The test sam-
ples are first fuzzified through these optimized fuzzy sets
before these input are inferred with the fuzzy rule base.
Once the rules are evaluated and compiled, each fuzzi-
fied input is compared with the compiled rules and are
classified, as shown in Figure 6. If corresponding rule is
not found, the input is classified as unclassified. Other-
wise, input is classified either as correctly classified or
A sample miniature compilation of the fuzzy rules
learned and evaluated from the learning data are as enu-
merated below:
1) IF Total Income is Low AND Civil Status is Single
AND Age is Young THEN Account is BAD.
Copyright © 2012 SciRes. IIM
Co mbination Pa ttern OutputxTim e u
0 1 0 1 B0
0 2 0 1 B2
0 3 0 1 B1
0 4 0 1 G4
0 1 0 2 B1
0 2 0 2 G8
0 3 0 2 G2
0 4 0 2 G0
8 Rules
Training Samples
0 2 0 1 B
0 2 0 2 G
0 3 0 1 G
0 4 0 1 G
0 2 0 2 G
0 3 0 2 G
0 1 0 2 B
0 3 0 1 B
6 R ules
C om bination P attern OutputxTim e
0 2 0 2 G8
0 4 0 1 G4
0 2 0 1 B2
0 3 0 2 G2
0 3 0 1 B1
0 1 0 2 B1
Comb ination Pattern OutputxTime us e d
0 2 0 2 G8
0 4 0 1 G4
0 2 0 1 B2
0 3 0 2 G2
4 Best Rules
Figure 5. Illustration of retaining the best k rules.
Figure 6. Architecture of the fuzzy logic implementation
stage of the HFNN model.
2) IF Total Income is High AND Civil Status is Mar-
ried AND Age is Middle THEN Account is GOOD.
3) IF Total Income is High AND Civil Status is Single
AND Age is Old THEN Account is BAD.
The Classification of the data is the end result of the
process. A sample classification of the systems is shown
1) IF Total Income is High AND Civil Status is Mar-
ried AND Age is Middle.
A fuzzified account that is actually BAD but is in-
ferred by the fuzzy rule base as GOOD is a misclassifica-
2) IF Total Income is Low AND Civil Status is Single
AND Age is Young.
This account is actually GOOD and classified as
GOOD is correctly classified.
3) IF Total Income is Low AND Civil Status is Mar-
ried AND Age is Young
If this account cannot be found in the fuzzy rule base
then it is unclassified.
Table 1 presents sample data record, from its original
form to its fuzzified form. Just refer to section 4.1 for
more details.
4. Experiment Results
4.1. Description of Experiment Data
In this research a total of one thousand records from the
bank were selected uniformly to be used for experiments.
There are 630 “good” and 370 “bad” of the thousand
accounts selected. The data contains 16 variables, shown
in Table 1. These 16 variables were used by statistical
tool of the bank in assisting the human credit evaluators
in reviewing the loan applications. Also, the same input
variables were considered in earlier credit scoring sys-
tems developed and published as described in section 1.
Some variables of the data have to be transformed so
as to reduce the complexity of the computation of the
traditional NN for the benchmark. The variables Total
Income, Equity Ratio and Gross Monthly Income (GMI)
Ratio, and Court Case were transformed. Total Income
values were transformed to the nearest million, e.g. from
100,000 to 0.10. Equity Ratio and GMI Ratio values
were transformed from percentage to their decimal val-
ues equivalent, e.g. from 78.45% to 0.7845. Out of 12
possible values for the Court Case variable, these values
were grouped into 2 simple values, each account have
either “With” court case or “Without” court case. This is
Copyright © 2012 SciRes. IIM
because only 14 out of the 1000 accounts selected have
“With” court case value and 986 accounts have “With-
out” court case value.
70060050040030 02001000-100
Classif ica t io n , %
4.2. Traditional Neural Network (MLP)
Experiments Results
In this research, experiments using traditional NN (Multi
Layer Perceptron) is conducted and compared to the pro-
posed HFNN model developed. Figures 7 and 8 shows
the results of the NN training and samples classification
performance. Figure 7 presented a typical behavior of a
neural network that is being trained. It just continuously
learned and cross-validation was used to determine when
the training stopped. When compared to the performance
of the proposed HFNN model shown in Figure 9, the
traditional NN performance does not indicate any spike
because there was no fuzzy input tuning process that hap-
EV s amples
TE samples
TR samples
It can be observe from Figure 8 that the patterns of
training (TR), evaluation (EV), and testing (TE) samples
are similar. When one group of samples, say TR, are de-
creasing in classification accuracy, the other groups are
also decreasing. Moreover, Figure 8 shows that as clas-
sification rate for the TR samples is still increasing, the
Ave. Sq rd . E r ro r (in E -02 )
Figure 7. Neural network training perf or mance.
Classification, %
TE samples
EV samples
TR samples
Figure 8. Neural network classification accuracy compari-
Figure 9. HFNN model classification performances.
classification rates for EV and TE samples are already
declining. This is an indication that the network is getting
over-fitted for TR samples. Hence, the training is stopped.
The final classification accuracy on this experiment is
94.67%. However, it is important to note that the training
of the traditional MLP to get optimum accuracy took
longer time compared to the proposed HFNN Model. It
recorded and average of 48 hours to train the MLP for
each set using Pentium 4 - 2.8 GHz Single Core Proces-
sor with 512 Mb RAM.
4.3. The Proposed HFNN Model Experiments
Figure 9 shows the performance of the proposed HFNN
model developed in this research. It can be seen from the
graph that each group of the samples behave similarly as
the system is being trained. The classification accuracies
of Training (TR), Evaluation (EV), and Testing (TE)
samples were already dropping after the 25th epoch.
However, when the tuning process for input fuzzy set is
activated all classification accuracies started to improve
particularly the EV samples, which reached 100% accu-
racy from 77th epoch to 197th epoch. The classification
accuracy of the system for the training samples (TR)
started to slow down at 117th epoch. However, at 347th
epoch the system achieved 100% classification accuracy.
The evaluation samples (EV) had reached 100% classify-
cation accuracy at 77th epoch. However it was over-fit-
ted after the 197th epoch. Hence, a drop in the classifica-
tion accuracy for EV begun until it settled at 98% classi-
fication accuracy at 247th epoch.
The reason why EV had reached 100% prediction ac-
curacy ahead of TR is because the system looks EV pat-
terns as just part of the TR patterns. But as the system
was further trained for TR, the system started to see the
minor differences in the patterns between EV and TR.
The continuous drop in EV classification accuracy con-
tinued until it reached a plateau staring at the 217th ep-
Copyright © 2012 SciRes. IIM
Copyright © 2012 SciRes. IIM
The test samples, TE, on the other hand, were classi-
fied by the system as having patterns much similar with
EV. But the graph in Figure 9 shows that the TE samples
have more patterns that are diverse than that of EV sam-
ples. That is why, TE classification rate dropped earlier
and lower than EV. TE classification rate reached 96%.
The tuning of the fuzzy sets served its purpose—the
classification performance of the HFNN Model had im-
proved so much. As can be seen in Figure 9, all three
graphs were falling continuously until at their lowest at
47th epoch.
4.4. Performance Comparison of the Proposed
HFNN Model against Traditional Neural
Network (MLP)
Figure 10 shows the behavior of the proposed HFNN
model against the traditional NN developed in this re-
search. Both of these two models showed superior per-
formance against the previous published works men-
tioned in Section 1. It can be seen from this figure that the
highest TE classification accuracy equal to 95.33% ob-
tained by the proposed HFNN model using 2560 rules.
The neural network classification accuracy is lower than
this at 94.67%.
Furthermore, it can be observed that using HFNN mo-
del with 95 rules resulted to 86% classification accuracy.
It even dropped to 83.67% when using 60 rules. This is a
trade-off of the system developed which the human ex-
perts have to decide. It should be noted that minimizing
the number of rules and input variables will make easy
and simple for the human credit evaluators to read and
decide. The simplification of the rules may result the ex-
pense of the classification accuracy. However, in this
proposed HFNN model, the 83.67% classification accu-
racy using 60 rules is still way above acceptable than that
of classical method reported in Section 1 mentioned ear-
lier in this paper.
The training time of the proposed HFNN model to get
optimum accuracy performance took only 3 hours com-
pared to 48 hours of the traditional Multilayer Perceptron
(MLP) Neural Network. The fuzzification increased the
granularity of the continuous input values. The complex-
ity of the various patterns found in the samples was re-
duced, and so with the training time as a consequence.
5. Discussion and Analysis of Results
In this research, the developed HFNN model tuning
process improved the classification accuracy signifi-
cantly. Without tuning process, the classification accura-
cies of the 3 sets; TR, EV, and TE could have been peg
to 94%. But employing the tuning process, TR samples
had reached and set at 100%, EV samples at 98.57% and
TE samples at 96%. This can be seen in Figure 9.
The original HFNN model with complete 16 input
variables got a classification accuracy of 98.57% only
when tested with the evaluating samples (EV). When the
redundant input variables were removed, the perform-
ance improved to 100%. The improvement signified that
some input variables or their particular combinations
contributed to noise. Hence, the removal of the redundant
input variables not only improved the readability of the
rules but it also improved the accuracy performance of
the HFNN model developed.
The improvement of the rule base can be defined in
terms of performance (i.e. reduction of error) and in terms
of complexity or simplicity (i.e. number of variables or
NN-16 HFNN (57599)-2560 HFNN (63991)-896 HFNN (63991)-95 HFNN (63991)-60
Classification Accuracy, %
Figure 10. Traditional NN and proposed HFNN model classification accuracy comparison for TE samples.
parameters). There is a trade-off between performance
and simplicity. To obtain high accuracy, a large number
of free parameters are needed, which again resulted in a
very complex and thus less comprehensible or readable
model. However, often the performance of a model can
actually increase with the reduction of the number of
parameters because the generalization capabilities of the
model may increase. If the model has too many parame-
ters, it tends to over-fit the training samples, TR, and
displays poor generalization on test samples, TE.
The removal of 12 input variables found to be redun-
dant resulted to 896 extracted rules only. Eliminating the
836 noise rules, the ones with single or no “hits” in the
training set, further simplified the readability of the set of
rules down to 60 rules. These 60 rules define the credit
scoring model with a classification performance of
83.67% when tested with the test set, way above the in-
dustry standard of 74% classification performance. Fi-
nally, The HFNN model developed in this research trains
faster than the traditional NN by 16 times and has better
classification accuracy of 95.33% compared to 94.67%
of traditional NN.
6. Conclusions and Recommendations
The HFNN model developed in this research to solve
credit risk management problem is capable of self-
learning similar to the traditional neural network. Subse-
quently, once trained, it is capable of discriminating the
“good” and the “bad” accounts with better accuracy
compared to the traditional NN. Unlike the neural net-
work’s “black box” configuration, which is an undesir-
able feature for credit evaluation, the HFNN model is
capable of generating the rules behind the discrimination
of each account subjected to it. The system behaves
much like a traditional fuzzy logic system in this aspect.
However, the HFNN model is better than the traditional
fuzzy logic system because of its learning capability. The
fuzzy logic system does not have this capability.
Although, this research was done for auto loan, the
Hybrid Fuzzy-Neuro Network is easily transferable to
similar loan products like mortgage loan, salary loan, and
even for credit card grants. These types of loans are the
same because they have similar input and output vari-
ables required.
In this research, the extracted rules were just listed in
the order of their importance, i.e. the most relevant rules
were listed first in the list. For future works, it is worth to
investigate some of these rules that can be fussed to-
gether to further simplify the list of rules. The output of
the developed HFNN model is limited to 2 possible val-
ues; either good or bad. By providing the data with more
than 2 outputs, say 2 additional outputs, namely: margin-
ally good and marginally bad. Marginal accounts can be
taken for a closer look before a decision is granted. This
can be considered for future study.
[1] M. Bonilla, I. Olmeda and R. Puertas-An, “Application of
Genetic Algorithms in Credit Scoring,” WASL Journal,
[2] L. P. Wallis, “Credit Scoring,” Business Credit Magazine,
March, 2001.
[3] A. Fensterstock, “Credit Scoring Basics,” Business Credit,
March, 2003.
[4] A. Jost, “Neural Networks,” Credit World, Vol. 81, No. 4,
1993, p. 26.
[5] A. Fensterstock, “The Application of Neural Networks to
Credit Scoring,” Business Credit, March, 2001.
[6] G. Vasconcelos, P. Adeodato and D. S. M. P. Monterio,
“A Neural Network Based Solution for the Credit Risk
Assessment Problem,” Proceedings of the IV Brazilian
Conference on Neural Networks, Washington, 20-22 July
1999, pp. 269-274.
[7] S. A. DeLurgio and F. Hays, “Understanding the Finan-
cial Interests in Neural Networks,” WASL Journal, 2001.
[8] BrainMaker, “Credit Scoring with Brainmaker Neural
Network Software,” 2003.
[9] J. Whitehouse, “Human Capabilities + Computer Auto-
mation = Knowledge,” IT Briefing, Pacific Lutheran Uni-
versity, Parkland, 2000.
[10] E. P. Dadios and D. J. Willaims, “A Fuzzy-Genetic Con-
troller for the Flexible Pole-Cart Balancing Problem,”
Proceedings of 1996 IEEE International Conference on
Evolutionary Computation, Nagoya, 20-22 May 1996, pp.
[11] L. A. Zadeh, “A Theory of Approximate Reasoning,” In:
Machine Intelligence, John Wiley & Sons, New York,
1979, pp. 149-194.
[12] I. Iancu, “A Mamdani Type Fuzzy Logic Controller,” In:
E. P. Dadios, Ed., Fuzzy Logic: Controls, Concepts,
Theories and Applications, InTech Croatia, Rijeka, 2012,
pp. 55-54.
[13] A. Achs, “From Fuzzy Datalog to Multivalued Knowl-
edge-Base,” In: E. P. Dadios, Ed., Fuzzy Logic: Algo-
rithms, Techniques and Implementations, InTech Croatia,
Rijeka, 2012, pp. 25-54.
[14] E. P. Dadios, K. Hirota, M. Catigum, A. Gutierrez, D.
Rodrigo, C, San Juan and J. Tan, “Neural Network Vision
Guided Mobile Robot for Driving Range Golf Ball Re-
triever,” Journal of Advanced Computational Intelligence
and Intelligent Informatics, Vol. 10, No. 4, 2006, pp. 181-
[15] R. Gustilo and E. P. Dadios, “Optimal Control of Aqua-
culture Prawn Water Quality Index Using Artificial Neu-
ral Networks,” Proceedings of the 5th IEEE International
Conference on Cybernetics and Intelligent Systems and
the 5th IEEE International Conference on Robotics,
Automation and Mechatronics, Qingdao, 17-19 Septem-
Copyright © 2012 SciRes. IIM
ber 2011, pp. 266-271.
[16] S. Hilado and E. P. Dadios, “Face Detection Using Neural
Networks with Skin Segmentation,” Proceedings of the
5th IEEE International Conference on Cybernetics and
Intelligent Systems and the 5th IEEE International Con-
ference on Robotics, Automation and Mechatronics,
Qingdao, 17-19 September 2011, pp. 261-265.
[17] N. Marcos, “Belief-Evidence Fusion through Successive
Rule Refinement in a Hybrid Intelligent System”, Ph.D.
Thesis, De La Salle University, Manila, 2002.
[18] D. Nauck, “Combining Neural Networks and Fuzzy Con-
trollers,” FLAI, Linz, 28 June-2 July 1993.
[19] D. Nauck, “Data Analysis with Neuro-Fuzzy Methods,”
Habilitation Thesis, University of Magdeburg, Magde-
burg, 2000.
[20] S. Haykin, “Neural Networks: A Comprehensive Founda-
tion,” Prentice Hall, Upper Saddle River, 1999.
[21] S. Piramuthu, “Financial Credit-Risk Evalution with Neu-
ral and Neurofuzzy Systems,” European Journal of Op-
eration Research, Vol. 112, No. 1, 1999, pp. 310-321.
Copyright © 2012 SciRes. IIM