







































 

 

 
1 

© 2022 Conscientia Beam. All Rights Reserved. 

PREDICTIVE CREDITWORTHINESS MODELING IN ENERGY-SAVING FINANCE: 
MACHINE LEARNING LOGIT AND NEURAL NETWORK   

 

 

 Herlan1 

 Eka Sudarmaji2+ 

 M. Rubiul Yatim3 

 

1,2,3Faculty of Economics and Business, University of Pancasila, Jalan 
Srengseng Sawah, Pasar Minggu Jakarta, Indonesia. 
1Email: herlan@univpancasila.ac.id Tel: 0816946278 
2Email: esudarmaji@univpancasila.ac.id Tel: 087884964643 
3Email: mrubiulyatim@univpancasila.ac.id Tel: 081384467762 

 

 
(+ Corresponding author) 

 ABSTRACT 
 
Article History 
Received: 14 December 2021 
Revised: 17 January 2022 
Accepted: 31 January 2022 
Published: 8 February 2022 
 

Keywords 
Creditworthiness 
ESCO 
Machine learning 
Logit regression 
LCCA 
Retrofit finance. 

 
JEL Classification: 
C25, C53, Q48. 

 
Customer's creditworthiness was becoming more crucial for ESCO. Machine learning 
was used to predict the creditworthiness of clients in retrofit financing processes. 
Machine learning was used to predict the creditworthiness of clients in ESCO financing 
processes. This research aimed to develop a retrofitting scoring model to leverage a 
machine learning and life cycle cost analysis (LCCA) to evaluate alternative financing for 
Energy Efficiency Saving in Indonesia. The model was built on the Logistic Regression 
model and Artificial Neural Networks model of machine learning. The model was 
developed and tested using the Python algorithm, and the proposed model's efficiency 
was demonstrated. The logistic regression calculations showed that the accuracy value 
of prediction data with test data was 88.3562 % and 87.67% for Artificial Neural 
Networks and Logistic Regression models.  The prediction rate result that refers to the 
correct predictions among all test data for Artificial Neural Networks and Logistic 
Regression model was 92.20% and 91.98%, respectively. Meanwhile, the percentage of 
customers who were correct to all customers predicted to default was 94.41% for 
Artificial Neural Networks and 93.81% for the Logistic Regression model. Credit models 
were helpful to evaluate the risk of consumer loans. Finally, the quality and performance 
of these models were evaluated and compared to identify the best one. The logistic 
regression and neural network models obtained were good and very similar, although 
the neural network was slightly better.  
 

Contribution/Originality: This study gained a deeper understanding of the obstacles in promoting energy 

efficiency practices in Indonesia's Building Energy Efficiency and ESCO. Therefore, the results of this study have 

implications for management science, management practices in the company and commercial building industry in 

Indonesia, and the government as a regulator. 

 

1. INTRODUCTION 

This research looked at the influence of an energy-efficiency program on one method of lowering energy 

consumption: switching to energy-saving lighting. The figure shows the example comparative measurement between 

LED energy-saving and conventional lightings shown in Table 1. Authors defined retrofits as the replacement of 

conventional lightings equipment with new LED energy-saving, or the construction of new infrastructure to increase 

energy efficiency and lower utility costs before the old equipment was damaged or reached the end of its economic 

life (Dobbs et al., 2013; Frankel, Heck, & Tai, 2013; Husin, Ahmad, Ab Wahid, & Kamaruzzaman, 2017; McWilliams 

& Walker, 2005). Under this model, retrofit projects would need investment, and the worth of the retrofits would be 

determined by the investment payback time (Heesen & Madlener, 2016; Kumbaroğlu & Madlener, 2012). The value 

Financial Risk and Management Reviews 
2022 Vol. 8, No. 1, pp. 1-11. 
ISSN(e): 2411-6408 
ISSN(p): 2412-3404 
DOI: 10.18488/89.v8i1.2919 
© 2022 Conscientia Beam. All Rights Reserved. 

 
 
 

 
 
 

 

 
 
 
 

https://orcid.org/0000-0002-7240-1589
https://orcid.org/0000-0002-6074-5114
mailto:herlan@univpancasila.ac.id
mailto:esudarmaji@univpancasila.ac.id
mailto:mrubiulyatim@univpancasila.ac.id
https://orcid.org/0000-0002-9465-9601
https://www.doi.org/10.18488/89.v8i1.2919


Financial Risk and Management Reviews, 2022, 8(1): 1-11 

 

 
2 

© 2022 Conscientia Beam. All Rights Reserved. 

of the investment determined the payback time, energy expenses, and operational hours of the equipment (Angelis & 

Nussbaum, 2015; Fuller & Petersen, 1996; Ruparathna, Hewage, & Sadiq, 2017). The operational hours of LED were 

used to determine the performance of the company's financial plans. 

 

Table 1. Comparative measurement between LED and TLD. 

No Description TLD LED TUBE 

1 Energy Consumption (Watt)  72 16 
2 Operational Hours 8 8 
3 Electricity Consumption/Kwh Year 210.24 46.72 
4 Electricity Bill/Kwh 1.125 1.125 
5 Electricity Cost in IDR 236.520 52.560 
6 Life Cycle in Hours 10.000 15.000 
7 Life Cycle in Years 3 5 
8 Lamp Price 12.450 81.000 
9 Differences in life cycle TLD vs. LED 6.225 - 
10 Energy cost/Year 242.745 52.560 
11 Difference Energy Saving TLD vs. LED - 190.185 
12 Energy Saving in % 78.35% 

 

Two independent finance agreements were used for retrofitting: the Energy Saving Performance Contract 

(ESPC) and the Energy Saving Agreement (ESA). The key idea was that the ESPC and ESA stipulated that the client 

pays the ESCO from the savings in energy costs. The flow of duty, privileges, and the entity participating in the 

retrofitting finance plan is shown in Figure 1.  

One of the reasons the authors investigated energy efficiency was technical improvements. In many regions, the 

employment of cutting-edge technology has created hurdles to embracing energy-saving energy technologies, 

resulting in inefficiencies in energy usage. Researchers speculated that sophisticated technology users could struggle 

to manage it Fornara, Pattitoni, Mura, and Strazzera (2016); Heesen and Madlener (2016); Stern (2011). All of these 

variables were converted into transaction costs. As a result, the authors understood how energy conservation works 

and the repercussions and how its creative business model, investment value, and new innovative financing choices 

would benefit everyone. Our research intends to develop a credit risk modeling methodology to lower the risk of 

credit granted to retrofitting projects while increasing ESCO benefits. 

 

 
Figure 1. Energy efficiency financing (WSGR, 2015). 

 



Financial Risk and Management Reviews, 2022, 8(1): 1-11 

 

 
3 

© 2022 Conscientia Beam. All Rights Reserved. 

Consequently, the goal of this research was to determine the optimum modeling with the highest performance 

and accuracy. The research employed the same methodologies and principles as EPA-Energy Start (The United States 

Environmental Protection, 2008) to assess the project's feasibility. The most important aspects to examine were the 

initial upfront investment, revenue factors, extra revenue factors, payback duration, investment returns, and client 

attributes. As a result, the firm's financial state was assessed first, followed by the return rate (payback). The hurdle 

rate was the agreed-upon criterion for passing the investment profitability test. If the IRR meets or exceeds predicted 

profits, the investment is profitable. A stream of future cash flows discounted with the project's risk provided the 

correct rate of return. As the cost of capital and risk grows, the amount of profit required would need more investment. 

Last but not least, the investor qualities must be taken into account. The approach was almost identical to banks' 

determining a customer's creditworthiness. 

In addition to the cost of credit default risk, the cost of retrofitting aspects is an essential factor. The retrofitting 

process is the same as every other loan process. Financial selection criteria were generally measured through credit 

reports. Some of the problems that can be identified were 1) What the ML model can use variable determinants to 

predict the occurrence of retrofitting credit risk?, and 2) What is the right strategy to mitigate credit risk in the 

retrofitting service process at ESCO?. The paper seeks to demonstrate how ML can help ESCO cope with energy 

efficiency projects with real case studies. The paper investigates case studies conducted between June 2020 and 

January 2021. As a result, interview procedures were updated to obtain information directly from company directors, 

chief executives, and general managers. In this paper, the authors use a case study, where the ESCO provider was 

MWS, and the customer was SHJ' The authors refer to pseudonyms for the research. Until recently, MWS decided 

that credit for retrofitting projects was traditionally done, i.e., ensuring that customers met the criteria and covered 

all risks. Ultimately, ESCO management decided on a retrofitting project based on this individual analysis, where the 

final decision was made by acclamation based on this analysis. Here we designed a Retrofitting Risk Modelling System 

for ESCO using the ANN and LR model of ML. As a result, a sound credit risk modeling system could assist MWS 

in predicting credit risk.  

Predictive analytics aims to build analytic models that predict the target size of interest. The target was used to 

direct the learning process during the optimization process. There are two types of predictive analytics depending on 

the size of the target, regression and classification. An example of a variable target in the regression was the 

creditworthiness of SHJ. As for the target, classification was a category, the binary classification between default and 

non-default. According to Halladay (2013), predictive analytics includes a variety of statistical techniques used for data 

analysis today, historically, economically, and even unstructured data such as sentence text and images. This 

information was used to identify some risks and opportunities and capture relationships between these factors. These 

analyses were used to make more accurate predictions about future events and explore patterns in data studies that 

lead to better decision-making. 

Furthermore Halladay (2013) also presents the evolution of predictive analysis development. The evolution began 

with project feasibility, which focused on project analysis and internal corporate data, continuing to be business 

Intelligence that already uses corporate external data integrated with internal company data and can answer why and 

evaluate the project from a strategic perspective. Finally, the authors decided to develop the data into Predictive 

Analytics, focusing on predictive modeling and forecasting tools. 

Sarosa (2012); Lee and Lee (2015) predictive analytics was a process that includes a series of methodologies that 

can manage various large-scale data-driven problems faced by many entities. It was a repetitive process that connected 

several statistical methods of sampling, model estimation, model prediction, and evaluation to form a credit scoring 

system. According to Halladay (2013), an essential stage in developing information about predictive analytics was 

setting the goals of the prediction itself to determine the business problems that must be solved. While according to 

Lee and Lee (2015), some essential things that cannot be ignored in organizing predictive analytics work are the 

collection and sampling of data, model estimation, model prediction and evaluation, and model adjustments.  



Financial Risk and Management Reviews, 2022, 8(1): 1-11 

 

 
4 

© 2022 Conscientia Beam. All Rights Reserved. 

Observing the credit default events in the retrofitting process in the energy efficiency industry must be 

understood as a flow business process. When starting the marketing process, officers look for prospective  

customers, the data verification/validation process, the credit analysis process, the verdict process, and the 

disbursement process. With the above explanation, this research was expected to provide some of the following 

benefits: 1) The use of machine learning in its particular retrofitting practice on granting energy efficiency 

project credit is a new practice and is starting to grow in Indonesia. Therefore, this research is expected to 

contribute to science, especially accepting technological innovation to produce efficiency in the energy 

efficiency industry. 2) The results of this research are expected to be one of the sources of information about 

retrofitting practices with Machine Learning to detect credit risk. 3)  This research can be helpful to be one of 

the references for decision-makers in the energy efficiency industry, namely problematic credits carried out by 

either outsiders or parties in the provision of retrofitting credit. 4) Providing information about alternative 

detection models, and 5) Creating a culture in the energy efficiency industry using machine learning-based 

technology innovations. 

 

2. LITERATURE REVIEW 

Neural networks and logistics have a long history, and many individuals have worked on their development 

throughout the years. In recent years, many fields have created outcome prediction models based on artificial neural 

networks ("AN") and logistic regression analysis ("LR"). According to empirical investigations, logistic regression 

(LR) was the most remarkable statistical analytic technique for creditworthiness (Lessmann, Baesens, Seow, & 

Thomas, 2015). The authors of this research look at ESCO's procedures for determining client creditworthiness. The 

authors looked at which elements influenced ESCO's final retrofitting project. To begin, the authors evaluate 

applications using traditional methods such as upfront investment, income factors, extra income variables, return 

durations, investment returns, and criteria related to client characteristics. The authors then employ Machine 

Learning (ML) neural networks and Logistic regression to forecast the likelihood of future behaviors (Lessmann, 

Baesens, Seow, & Thomas, 2015). 

In most cases, logistic regression is utilized in the credit score model. The creditworthiness model (Wiginton, 

1980) used a logistic model. Cramer (2004) later created some other types of logistic regression. According to the 

experimental data, the border logistic regression approach has a greater classification accuracy. Overall, while the 

creditworthiness model performs well in machine learning, it needs big raw data. Logistic regression is one of the 

techniques borrowed by machine learning from statistics. It was a method for solving binary classification problems 

(two class values). The authors write a logistic regression algorithm for machine learning in this paper. The logistics 

function was also called the sigmoid function. An S-shaped curve can take an absolute value number and map it into 

values between 0 and 1, but never precisely at that limit. Logistic regression hypotheses tend to limit functions between 

0 and 1 . In machine learning, the authors use sigmoids to map probability predictions.   

The authors expect the classifier to provide a set of outputs or classes based on probability when passing input 

through the prediction function and returning a probability score between 0 and 1. Logistic regression becomes a 

probability model. In this paper, the prediction models creditworthiness stated as 'credit default' or 'default note.' The 

first-class can 'default,' and the logistic regression model can be written as a possible 'default' or P 

(Creditworthiness=default|credit-score). It can be written in another way; the authors model the probability that the 

input (X) belongs to the default class (Y =1); hence the authors can write this formally as P(X) = P(Y=1| X). Logistic 

regression was a binary classification approach that may predict when the item has only two potential values. For 

example, depending on age, yearly income, height, weight, and other factors, the researchers could determine whether 

a person was a male (0) or female (1). Logical regression was more straightforward than a neural network. 

Artificial Neural networks have been widely used for the credit-scoring problem (Lee & Chen, 2005; West, 2000). 

Different ANNs have been suggested to tackle the credit scoring problem more recently. In the empirical result, the 



Financial Risk and Management Reviews, 2022, 8(1): 1-11 

 

 
5 

© 2022 Conscientia Beam. All Rights Reserved. 

neural networks achieve the highest average correct classification rate compared with the LR model, considering that 

results were very close Abdou and Pointon (2011).  When a complicated nonlinear connection between credit scoring's 

features was shown, the ANN model offered a considerable benefit; according to West (2000) and Hájek (2011) ANN 

and LR are techniques for categorization problems with a finite number of options (e.g., not a continuous value). 

Many people utilize the sigmoid and softmax functions to solve multiclass-classification issues in logistic regression. 

The structure of neural networks was relatively specific, with one input layer, at least one hidden layer, and eventually 

one output layer. Both ANN and LR have supervised Machine Learning algorithms with two primary goals: 1) 

training the model and 2) predicting it. Both algorithms face the same challenge: determining the optimum parameter 

value. 

 

3. METHOD 

In creditworthiness research, result prediction models based on artificial neural networks and logistic regression 

analysis have been created. Both of these strategies have benefits and drawbacks. Both of these strategies have benefits 

and drawbacks. This study investigated the efficacy of artificial neural networks and logistic regression models in 

predicting credit default occurrences in the energy efficiency industry's retrofitting. A test with a ROC value of one 

perfectly differentiates two outcomes, but a test with a ROC value of 0.5 was entirely worthless and performed no 

better than chance. This study takes the form of a case study at the ESCO firm  MWS, and the authors employ a 

'pseudo name' for the company's name to safeguard an institution's security. An empirical research approach was 

applied in this study. Empirical evidence can show the truth of a particular event using this technique. As a result, 

the core data for this study was based on incident data from MWS's business database.  

The research focuses on MWS adopting retrofitting practices with their own ESCO model on energy 

management or conservative energy projects or activities. MWS was intended to introduce the latest PSS model in 

their own ESCO model with their capital in Indonesia. MWS conducts simple financial analysis to finance retrofitting 

projects to determine the appropriate project's value, risk, and liquidity effects. Energy efficiency projects need to be 

considered on par with others to succeed. MWS uses financial analysis to assess whether investments generate 

reasonable returns while not damaging the consumer base. This research was conducted intensively from July 2020 

to June 2021. Activities include research preparation, data collection, data analysis process, and reporting of research 

results in research conducted in Jakarta and Bekasi. The implementation of the research begins with the collection of 

data until the final report of the research takes ± ten weeks.    

Customer's creditworthiness and retrofitting default risks were becoming more crucial for ESCO. Therefore, 

upon LCC analysis, MWS was conducted to assess the cooperative's causal relationship by using the characteristics 

of problems in causal relationships between creditworthiness variables and other variables. Dependent variables were 

retrofitting feasibility, i.e., Rejected Project or Accepted Project. The independent variables, namely Initial upfront 

investment, revenue factors, additional revenue factors, payback period, investment returns, and customer 

characteristics, were the most significant factors that need to consider; see Table 2.  

The process of developing the predictive model includes data access, data preparation, development of a training 

set, selection of an algorithm, training the model, the model testing, valuation of the model, and implementation. The 

simulation runs were repeated several times with different configurations to improve results. The visualization of the 

findings can be achieved in a variety of ways. A Logistic Regression and Artificial Neural Networks using ML have 

been done.  

The data analysis approach was a credit score prediction model using machine learning to choose the appropriate 

model in this research. In this research, a definitive inference test, in addition to hypothesis testing, was also 

conducted. The research looked at the relationship between independent and dependent variables. The frame of mind 

of this research can be explained in four parts, namely identifying research and research-gap problems in the 1st stage; 



Financial Risk and Management Reviews, 2022, 8(1): 1-11 

 

 
6 

© 2022 Conscientia Beam. All Rights Reserved. 

analysis of the causative factors/impact in stage 2; and problem mapping with theoretical models and discussion of 

results analysis in the third and fourth stages.  

 

Table 2. Variable, membership function dan range. 

No Aspect Input Variable 
Membership 
Function 

Range 

1 Project Agreement Agreement Type ESA & ESPC 
ESA = 0.8 - 1.0 
ESPC = 0.5 - 0.7 

2 
Upfront 
Investment 

Tolerance 
Factors 

High Risk, 
Medium & Low 
Risk 

High Risk: Tolerance > 1.5Bio 
Medium Risk: Tolerance >750Mio & 
<=1.5Bio 
Low Risk: Tolerance <=750Mio 

3 Revenue Factors Project Margin 
High, Medium & 
Low 

High: Margin > 30% 
Medium: Margin 10 - 30% 
Low: Margin > 10% 

4 
Additional 
Revenue Factors 

Interest Loan 
High, Medium & 
Low 

High: Interest Loan > Time Deposit 
Rate 
Medium: Interest Loan = Time 
Deposit Rate 
Low: Interest Loan < Time Deposit 
Rate 

5 Payback Period Payback Years 
High Risk, 
Medium & Low 
Risk 

High Risk: Years > 3Years 
Medium Risk: 1 - 3 years 
Low Risk: Years <= 1 

6 Investment Return NPV 
High, Medium & 
Low 

High: NPV >30% of Upfront 
Investment 
Medium: NPV ~ 10%-30% of 
Upfront Investment 
Low: NPV < 10% of Upfront 
Investment 

7 Customer 
Type of 
Customer 

High Risk, 
Medium & Low 
Risk 

High Risk = 1 
Medium= 0.5 - 1 
Low Risk = 0.2 - 0.5 

 

4. RESULTS  

4.1. Performance Evaluation Criteria 

The confusion matrix offered data for comparing the system's (model) classification results to the actual 

classification results. The error matrix was another name for the confusion matrix. The confusion matrix was a matrix 

table that described the classification model's performance on a set of test data whose actual values were known. In 

the confusion matrix, four terms indicate the categorization process' findings were True Positive (TP), True Negative 

(TN), False Positive (FP), and False Negative (FN). The following is a basic case example of predicting whether or 

not a customer defaults on the payment. True Positive (TP) - Data that was expected to be right was found to be 

true. For instance, a customer who fails on a retrofitting payment (class 1) forecasts that the customer will also default 

on payment (class 1). True Negative (TN) - It was projected that negative data would be actual. Customers that do 

not default on retrofitting payments (class 2), for example, are predicted to not default on payments by the model 

(class 2). Type I Error — False Positive (FP) — Negative data was forecasted as positive data. Customers who, for 

example, do not default on retrofitting payments (class 2) but are predicted to default on payments by a model (class 

1). Type II Error: False Negative (FN) – Positive data was supposed to be negative. A customer, for example, defaults 

on a retrofitting payment (class 1), but the model predicts that the customer would not default on the payment (class 

2). 

In some cases, the Type II Error was more dangerous. We can relate that statement to the example of the credit 

default prediction above. If the customer does not default but was predicted to default (FP), then in the following 



Financial Risk and Management Reviews, 2022, 8(1): 1-11 

 

 
7 

© 2022 Conscientia Beam. All Rights Reserved. 

prediction, ESCO can find out the actual situation that the customer did not default. However, if customers default 

but are predicted not to default (FN), the company will find the actual situation very late, and ESCO will suffer 

financial losses. So, it can be said that Type II Error was dangerous. The confusion matrix will tell how well the 

model we make. In particular, the confusion matrix also provides information about TP, FP, TN, and FN. It was 

beneficial because the classification results generally cannot be adequately expressed in one number only. The 

confusion matrix allows us to see from whom to predict the credit default quickly, how much to default and not. The 

confusion matrix in the image below shows that companies were said to not default in the ANN – 473 companies, but 

470 companies were declared not to default when using the LR model (TP). According to the LR (TN) model, the 

ANN model predicts 43 companies default correctly and 42 companies according to the LR (TN) model. The correct 

prediction (TP) was in the diagonal table in the upper left corner, so visually, it was straightforward to see the 

prediction error because it was in the table outside the left corner diagonal; the confusion matrix is depicted in Figure 

2. 

 

  

Figure 2. Confusion matrix – artificial neural network & logit model. 

 

The result showed that the model's accuracy was generally measured based on correctly classified instances. Both 

ANN and LR Models were chosen to optimize predictive accuracy.  The term accuracy refers to the model's ability 

to categorize data correctly. As a result, accuracy was defined as the proportion of accurate predictions (both positive 

and negative) to the total data. To put it another way, accuracy was the degree to which the projected value was near 

to the actual (actual) value. The accuracy value can be obtained by equation TP = (TP+TN) / (TP + TN + FP + 

FN). From the binary classification confusion matrix example above, calculating the accuracy value can answer the 

question, what percentage of customers were correctly predicted to default or who do not default from all customers? 

Based on Figure 2, the accuracy of ANN was (473+43) / (473+43+28+40) = 88.36%, on the LR model was shown 

(470+42) / (473+42+31+41) = 87.67%. The prediction rate result on the ANN model that refers to the percentage 

of correct predictions among all test data was as follows: True Positive Rate (TPR) = 473 /(473+40) = 92.20%, TPR 

or sensitivity or recall was the percentage of correctly classified cases of creditworthiness. On the LR model it shown 

470 /(470+41) =  91.98%. True Negative Rate (TNR) = 43 /(43+28) = 60.56%, TNR or specificity was the percentage 

of correctly classified cases. On the LR model, it shown 42 /(42+31) = 57.53%. TPR was the number of positive 

examples classified as positive, and TNR was the number of negative examples classified as negative. 

On the other hand, precision refers to the degree of agreement between the desired data and the model's predicted 

outputs. Consequently, precision was defined as the ratio of accurate positive predictions to correct positive expected 

outcomes. Of all the positive classes that have been correctly predicted, how many data were truly positive. The TP 

/ (TP+FP) equation can obtain the precision value. From the binary classification confusion matrix example above, 



Financial Risk and Management Reviews, 2022, 8(1): 1-11 

 

 
8 

© 2022 Conscientia Beam. All Rights Reserved. 

calculating the precision value can answer the question, what percentage of customers were correct to default out of 

all customers predicted to default? The answer for the precision rate of ANN were 473 / (473 + 28) = 94.41% and on 

LR model 470 / (470 + 31) = 93.81%.  

Based on Lessmann et al. (2015) the authors need to build evaluation criteria to quantify the misclassification of 

default assessment models. This article utilized this relative ratio to create a cost indicator that indicates the credit 

risk assessment model's misclassification cost. We also constructed the cost of credit risk as False Negative Rate 

(FNR)  = FN / (FN + TP) = 40/(40+743) = 7.80% and  False Positive Rate (FPR) = FP / (FP + TN) = 28 / (28+43) 

= 5.59%. On the other hand, FNR and FPR on the LR model were 8.02% and 6.19%, respectively. FNR was the 

number of positive examples classified as negative (i.e., type I error), and FPR was the number of negative examples 

classified as positive (i.e., type II error). Lessmann reported that the ratio of misclassification costs associated with 

type II and type I errors was 5:1, which (Abdou, 2009) used. Hence the cost of credit risk become = 5 x (7.80% + 

5.59%) or 66.93% based on ANN model and 71.06% based on LR model.   

A ROC curve (receiver operating characteristic curve) showed the classification performance. The Receiver 

Operating Characteristics (ROC) curve, which shows the false positive rate (FPR) on the Y-axis versus the true 

positive rate (TPR) on the X-axis over a range of threshold values, was formerly a standard technique. 

 

  
Figure 3. ROC curve – artificial neural network & logit model. 

 

Simultaneously, the ANN algorithm-based model showed the best result under the ROC Curve metric in the 

other area. The ideal ROC curve is parallel to the Y-axis, which is impossible to accomplish in practice. The model 

that comes closest to it will be the best. Under the ROC Curve Value (AUROC), a number between 0 and 1 was 

traditionally used to summarize the ROC curve. The better the model, the greater the AUROC. AUC was a composite 

measure of success that considers all possible classification thresholds. The model's likelihood rates a random positive 

example higher than a random negative example was one way to view AUC. A receiver operating characteristic curve 

(ROC curve) was a graph that shows how well a classification model performs overall classification thresholds. Two 

parameters were plotted on this curve. TPR vs. FPR was plotted on a ROC curve at various classification thresholds. 

Lowering the classification threshold causes more things to be classified as positive, resulting in more False Positives 

and True Positives. A typical ROC curve for ANN was 0.73, and LR was at 0.72, shown in Figure 3. 

 

4.2. Creditworthiness Success Analysis 

Logistic regression was a subset of a neural network classifier. The researchers can simulate a logistic regression 

model using a neural network with one hidden node with the identity activation function and one output node with 

zero bias and logistic sigmoid activation. In principle, anything researchers can do with logistic regression with a 

neural network. Therefore, theoretically, a neural network was always better than logistic regression, or more 

precisely, a neural network can do no worse than logistic regression. The ESCO was more worried about elements 



Financial Risk and Management Reviews, 2022, 8(1): 1-11 

 

 
9 

© 2022 Conscientia Beam. All Rights Reserved. 

that enhance the likelihood of successful retrofitting than whether the model correctly forecasts the probability of 

credit risk scoring. This conclusion was made under the methods used by Liang and He (2020) and Cai et al. (2016) 

to assess the success of credit risk and loan prediction models. Thus we picked the logistic regression model over a 

neural network in this part. In addition, this section investigates all factors that influence the likelihood of effective 

retrofitting. Table 3 showed the results of the model's p-value was 1,852 x 10^-187, which was less than 0.01 or 1% 

as was thought 'excellent. The p=value of each variable as a list under P>|z| was less than 0.01 or 1%. Pseudo R-

Squared was 0.4810 or 48.19%.  From the pseudo-R-Square value, all independent variables' influence on dependent 

variables (retrofitting project) was 48.19%. Obtained multinomial logistics regression model for log category ratio 

retrofitting project=1(Take) and retrofitting project =0 (Hold and Reject) was -6.5619 + -4.0780 (Project Agreement) 

+ 0.5237 (Upfront Investment) + 2.0639 (Revenue Factors) + 0.4995 (Additional Revenue Factors) + 0.5579 

(Investment Return) + 1.3236 (Customer) - 0.7805 (Type of Customer).  

 

Table 3. Logistic results. 

EXPERT_JUDGE=0=1 Coeff Std. Error z P>|z| [0.025] [0.975] 

Constant (6.562) 0.694 (9.445) 0.000 (7.992) (5.202) 
Project agreement (4.078) 0.280 (14.546) 0.000 (4.627) (3.529) 
Upfront investment 0.524 0.104 5.058 0.000 0.321 0.727 
Revenue factors 2.064 0.139 14.826 0.000 1.791 2.337 
Additional revenue factors 0.500 0.105 4.768 0.000 0.294 0.705 
Investment return 0.558 0.104 5.366 0.000 0.354 0.762 
Customer 1.324 0.118 11.255 0.000 1.093 1.554 
Type of customer (0.781) 0.170 (4.603) 0.000 (1.113) (0.448) 

 

5. CONCLUSION 

This research briefly compares logistic regression and artificial neural networks with shared roots in statistical 

pattern recognition. Predictive modeling for creditworthiness detection was done in this report. The model was built 

on the LR and ANN model of machine learning. The model was developed and tested using the Python algorithm, 

and the proposed model's efficiency was demonstrated. The logistic regression calculations showed that the accuracy 

value of prediction data with test data was 88.3562 % and 87.67% for ANN and LR models.  The prediction rate result 

that refers to the correct predictions among all test data for ANN and LR model was 92.20% and 91.98%, respectively. 

The percentage of customers who were correct to the default of all customers predicted to default were 94.41% for 

ANN and 93.81% for the LR model. This case study reveals that the ANN model was better than the LR model, 

especially when a complex nonlinear relationship was presented between credit features. It was in line with the other 

empirical research that a neural network was always better than logistic regression. More precisely, a neural network 

can do no worse than logistic regression. 

On the other hand, the vast opportunities in retrofitting projects come with significant risks. The biggest issue 

was that the company was hampered by a lack of knowledge about the clients' reputation. The customer's 

creditworthiness was critical to ESCO's survival. The project's numerous factors, particularly the qualitative 

customer characteristic, were vital factors to consider. As a result, ESCO companies increasingly rely on machine 

learning techniques. The research aimed to determine how to construct a predictive model using machine learning 

techniques and whether it would be easy, convenient, and accessible for non-experts. We found that the machine 

learning methods for creditworthiness prediction in retrofitting projects were fresh and worth a shot. Machine 

learning used the logistic regression model's predictive efficiency by pre-processing data using ANN and LR models. 

It was hoped that this new practice would grow in popularity and become standard among ESCOs. Unfortunately, 

current machine-learning-based creditworthiness scoring practices lack explainability and interpretability. In terms 

of the credit approval process, most of these algorithms are called black boxes. Under the circumstances, ESCO must 

penalize the retrofitting project. As a result, since retrofitting was a new industry, the credit approval process was 



Financial Risk and Management Reviews, 2022, 8(1): 1-11 

 

 
10 

© 2022 Conscientia Beam. All Rights Reserved. 

challenging to communicate to consumers. The most important thing for ESCO to deal with the project was 

friendship and know-how with the client. 

Predictive modeling for creditworthiness detection was done in this report. The model was built on the LR and 

ANN models of machine learning. The model was developed and tested using the Python algorithm, and the proposed 

model's efficiency was demonstrated. Some of the limitations of this research related to creditworthiness prediction 

research were based on case studies and limited to ESCO companies, namely MWS. The prediction was reflected in 

the scoring value that can be an Early Warning Signal (EWS) tool for managers in 'MWS' companies in mitigating 

bad credit risk. The other limitation was that this research related to creditworthiness scoring with various industries 

was more varied considering the different business processes and models. This research was based on the energy 

efficiency industry, especially in the case study of MWS companies. Moreover, the research was finally limited to the 

effectiveness of efforts to prevent credit jams with alternative approaches compared to traditional approaches on 

MWS companies. 

 

Funding: This study received no specific financial support.    
Competing Interests: The authors declare that they have no competing interests.  
Authors’ Contributions: All authors contributed equally to the conception and design of the study. 

 

REFERENCES 

Abdou, H. A., & Pointon, J. (2011). Credit scoring, statistical techniques and evaluation criteria: A review of the literature. Intelligent 

Systems in Accounting, Finance and Management, 18(2-3), 59-88.Available at: https://doi.org/10.1002/isaf.325. 

Abdou, H. A. P. J. (2009). Intelligent systems in accounting, finance, and management. Intelligent Systems in Accounting, Finance, and 

Management, 16(1–2), 21–31. 

Angelis, D. I., & Nussbaum, D. (2015). Cost analysis. Military Cost-Benefit Analysis: Theory and Practice, 113-135. 

Cai, D. J., Aharoni, D., Shuman, T., Shobe, J., Biane, J., Song, W., . . . Lou, J. (2016). A shared neural ensemble links distinct 

contextual memories encoded close in time. Nature, 534(7605), 115-118.Available at: https://doi.org/10.1088/1741-

2552/ab2ffa. 

Cramer, J. A. (2004). A systematic review of adherence with medications for diabetes. Diabetes Care, 27(5), 1218-1224. 

Dobbs, R., Pohl, H., Lin, D.-Y., Mischke, J., Garemo, N., Hexter, J., & Nanavatty, R. (2013). Infrastructure productivity: How to save 

$1 trillion a year: McKinsey Global Institute (Issue January). 

Fornara, F., Pattitoni, P., Mura, M., & Strazzera, E. (2016). Predicting intention to improve household energy efficiency: The role 

of value-belief-norm theory, normative and informational influence, and specific attitude. Journal of Environmental 

Psychology, 45, 1-10.Available at: https://doi.org/10.1016/j.jenvp.2015.11.001. 

Frankel, D., Heck, S., & Tai, H. (2013). Sizing the potential of behavioral energy-efficiency initiatives in the US residential market: 

McKinsey & Company. 

Fuller, S. K., & Petersen, S. R. (1996). LC costing manual for the federal energy management program. NIST Handbook, 135. 

Hájek, P. (2011). Municipal credit rating modelling by neural networks. Decision Support Systems, 51(1), 108-118.Available at: 

https://doi.org/10.1016/j.dss.2010.11.033. 

Halladay, B. S. D. (2013). Using predictive analytics to improve decision making. Journal of Equipment Lease Financing, 31(2), 1–6. 

Heesen, F., & Madlener, R. (2016). Technology acceptance as part of the energy performance gap in energy- efficient retrofitted 

dwellings Florian Heesen and Reinhard Madlener December 2014 Revised February 2016 Institute for Future Energy 

Consumer Needs and Behavior ( FCN ) (FCN Working Paper No. 25/2014; Issue 25). 

Husin, N. I., Ahmad, A. C., Ab Wahid, A. M., & Kamaruzzaman, S. N. (2017). Energy efficiency criteria for green highway. Social 

Science & Humanities, 25(S), 115–124. 

Kumbaroğlu, G., & Madlener, R. (2012). Evaluation of economically optimal retrofit investment options for energy savings in 

buildings. Energy and Buildings, 49, 327-334.Available at: https://doi.org/10.1016/j.enbuild.2012.02.022. 



Financial Risk and Management Reviews, 2022, 8(1): 1-11 

 

 
11 

© 2022 Conscientia Beam. All Rights Reserved. 

Lee, T.-S., & Chen, I.-F. (2005). A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive 

regression splines. Expert Systems with Applications, 28(4), 743-752. 

Lee, I., & Lee, K. (2015). The internet of things (IoT): Applications, investments, and challenges for enterprises. Business Horizons, 

58(4), 431-440.Available at: https://doi.org/10.1016/j.bushor.2015.03.008. 

Lessmann, S., Baesens, B., Seow, H.-V., & Thomas, L. C. (2015). Benchmarking state-of-the-art classification algorithms for credit 

scoring: An update of research. European Journal of Operational Research, 247(1), 124-136.Available at: 

https://doi.org/10.1016/j.ejor.2015.05.030. 

Liang, K., & He, J. (2020). Analyzing credit risk among Chinese P2P-lending businesses by integrating text-related soft 

information. Electronic Commerce Research and Applications, 40, 100947.Available at: 

https://doi.org/10.1016/j.elerap.2020.100947. 

McWilliams, J. A., & Walker, I. S. (2005). Home energy article : A systems approach to retrofitting residential HVAC systems: Lawrence 

Berkeley National Laboratory (Issue April). 

Ruparathna, R., Hewage, K., & Sadiq, R. (2017). Economic evaluation of building energy retrofits: A fuzzy based approach. Energy 

and Buildings, 139, 395-406.Available at: https://doi.org/10.1016/j.enbuild.2017.01.031. 

Sarosa, S. (2012). Adoption of social media networks by Indonesian SME: A case study. Procedia Economics and Finance, 4, 244–

254.Available at: https://doi.org/10.1016/s2212-5671(12)00339-5. 

Stern, P. C. (2011). Contributions of psychology to limiting climate change. American Psychologist, 66(4), 303–314. 

The United States Environmental Protection, A. (2008). Energy star ® Building upgrade manual energy star ® Building Upgrade 

Manual Contents (Office of Air and Radiation 2008 Edition). 

West, D. (2000). Neural network credit scoring models. Computers & Operations Research, 27(11-12), 1131-1152. 

Wiginton, J. C. (1980). University of washington school of business administration Cambridge University Press. Journal of 

Financial and Quantitative Analysis, 15(3), 757–770. 

WSGR. (2015). Innovative financing for renewable energy innovative financing for renewable energy ARTICLE innovative 

financing for renewable energy. Retrieved from: https://digitalcommons.pace.edu/pelr. 

 

 

 

 

 

 

 

 

  

 

 

Views and opinions expressed in this article are the views and opinions of the author(s), Financial Risk and Management Reviews shall not be responsible or 
answerable for any loss, damage or liability etc. caused in relation to/arising out of the use of the content. 

 


