1 © 2022 Conscientia Beam. All Rights Reserved. PREDICTIVE CREDITWORTHINESS MODELING IN ENERGY-SAVING FINANCE: MACHINE LEARNING LOGIT AND NEURAL NETWORK Herlan 1 Eka Sudarmaji 2+ M. Rubiul Yatim 3 1,2,3 Faculty of Economics and Business, University of Pancasila, Jalan Srengseng Sawah, Pasar Minggu Jakarta, Indonesia. 1 Email: herlan@univpancasila.ac.id Tel: 0816946278 2 Email: esudarmaji@univpancasila.ac.id Tel: 087884964643 3 Email: 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.