Image of Predictive Creditworthiness Modeling in Energy-saving Finance: Machine Learning Logit and Neural Network

Karya Ilmiah Dosen

Predictive Creditworthiness Modeling in Energy-saving Finance: Machine Learning Logit and Neural Network



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.


Ketersediaan

EKIDUPT220047650/EKIDUPT220047Perpustakaan PusatB A C A
D I T E M P A T

Lampiran Berkas

Informasi Detil

Judul Seri
Financial Risk and Management Reviews
No. Panggil
650/EKIDUPT220047
Penerbit Conscientia Beam: New York.,
Deskripsi Fisik
18 p.
Bahasa
English
ISBN/ISSN/NPM
2411-6408
Klasifikasi
650
Tipe Isi
text
Tipe Media
Textbook
Tipe Pembawa
-
Edisi
Vol. 8 No. 1 (2022)
Subyek
Info Detil Spesifik
-
Pernyataan Tanggungjawab

Versi lain/terkait

Tidak tersedia versi lain



Pencarian Spesifik


Judul:
Pengarang:
Penerbit:
Koleksi:
Lokasi:

Informasi


DETAIL CANTUMAN


Kembali ke sebelumnyaXML DetailCite this