Application of credit‐scoring methods in a decision support system of investment for peer‐to‐peer lending

2021 ◽  
Author(s):  
Golnoosh Babaei ◽  
Shahrooz Bamdad
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Credit Scoring ◽  
Peer To Peer ◽  
Scoring Methods ◽  
Peer Lending

Network-aware credit scoring system for telecom subscribers using machine learning and network analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hongming Gao ◽  
Hongwei Liu ◽  
Haiying Ma ◽  
Cunjun Ye ◽  
Mingjun Zhan
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Scoring System ◽  
Credit Scoring ◽  
Prediction Performance ◽  
Content Type ◽  
Connected Subgroup ◽  
Source Data ◽  
Credit Scores

PurposeA good decision support system for credit scoring enables telecom operators to measure the subscribers' creditworthiness in a fine-grained manner. This paper aims to propose a robust credit scoring system by leveraging latent information embedded in the telecom subscriber relation network based on multi-source data sources, including telecom inner data, online app usage, and offline consumption footprint.Design/methodology/approachRooting from network science, the relation network model and singular value decomposition are integrated to infer different subscriber subgroups. Employing the results of network inference, the paper proposed a network-aware credit scoring system to predict the continuous credit scores by implementing several state-of-art techniques, i.e. multivariate linear regression, random forest regression, support vector regression, multilayer perceptron, and a deep learning algorithm. The authors use a data set consisting of 926 users of a Chinese major telecom operator within one month of 2018 to verify the proposed approach.FindingsThe distribution of telecom subscriber relation network follows a power-law function instead of the Gaussian function previously thought. This network-aware inference divides the subscriber population into a connected subgroup and a discrete subgroup. Besides, the findings demonstrate that the network-aware decision support system achieves better and more accurate prediction performance. In particular, the results show that our approach considering stochastic equivalence reveals that the forecasting error of the connected-subgroup model is significantly reduced by 7.89–25.64% as compared to the benchmark. Deep learning performs the best which might indicate that a non-linear relationship exists between telecom subscribers' credit scores and their multi-channel behaviours.Originality/valueThis paper contributes to the existing literature on business intelligence analytics and continuous credit scoring by incorporating latent information of the relation network and external information from multi-source data (e.g. online app usage and offline consumption footprint). Also, the authors have proposed a power-law distribution-based network-aware decision support system to reinforce the prediction performance of individual telecom subscribers' credit scoring for the telecom marketing domain.

Machine learning and decision support system on credit scoring

2019 ◽  
Vol 32 (14) ◽  
pp. 9809-9826 ◽  
Author(s):  
Gernmanno Teles ◽  
Joel J. P. C. Rodrigues ◽  
Kashif Saleem ◽  
Sergei Kozlov ◽  
Ricardo A. L. Rabêlo
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Credit Scoring

scholarly journals Credit Scoring Kelayakan Debitur Menggunakan Metode Hybrid ANN Backpropagation dan TOPSIS

2019 ◽  
Vol 3 (1) ◽  
pp. 73-78
Author(s):  
Susan Dwi Saputri ◽  
Ermatita Ermatita
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Credit Scoring ◽  
Training Data ◽  
Topsis Method ◽  
Wrong Decision ◽  
Artificial Neural

Credit is one of the common practices that provide benefits for financial or non-financial institutions. However on the other hand, aid loans also have higher risks if the institutions give the wrong decision in giving a loan. Credit Scoring is one of techniques that can determine whether it is feasible to given a loan or not. The selection of a credit scoring model greatly determines the value in classifying credit that is feasible or not to giving a loan. Decision Support System (DSS) is one system that can be used to overcome this problem. The advantages of DSS are being able to overcome the problems that have semi-structured and unstructured data. In this study, DSS was supported by using Artificial Neural Network Backpropagation method and TOPSIS method to find the priority for seeking eligibility. Accuracy results obtained in this study reached 98,69% with the number of iteration is 300, the number of training data is 30, neuron hidden 12 and error tolerance is 0.001. TOPSIS method succeeded in ranking 185 data selected as recipients of credit. Keywords:Credit Scoring, Decision Support System (DSS), Artificial Neural Network (ANN), Backpropagation, TOPSIS.

A fuzzy decision support system for credit scoring

2016 ◽  
Vol 29 (10) ◽  
pp. 921-937 ◽  
Author(s):  
Joshua Ignatius ◽  
Adel Hatami-Marbini ◽  
Amirah Rahman ◽  
Lalitha Dhamotharan ◽  
Pegah Khoshnevis
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Credit Scoring ◽  
Fuzzy Decision ◽  
Fuzzy Decision Support System

A dynamic decision support system for evaluating peer-to-peer rental accommodations in the sharing economy

2020 ◽  
Vol 91 ◽  
pp. 102653
Author(s):  
Madjid Tavana ◽  
Sayed Mohammad Hossein Mousavi ◽  
Hassan Mina ◽  
Farhad Salehian
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Sharing Economy ◽  
Peer To Peer

scholarly journals Sistem Pendukung Keputusan Kelayakan Kredit pada PT. BPR Mitra Catur Mandiri Menggunakan Metode Credit Scoring

Repositor ◽  
2020 ◽  
Vol 2 (5) ◽  
pp. 649
Author(s):  
Haris Diyaul Fata ◽  
Gita Indah Marthasari ◽  
Yufis Azhar
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Matrix Method ◽  
Confusion Matrix ◽  
Credit Scoring ◽  
Accuracy Rate ◽  
Scoring Method ◽  
The Creation ◽  
Credit Worthiness

Abstrak Kredit adalah suatu cara yang dapat dilakukan untuk mendapatkan modal usaha. Tetapi terkadang pihak bank mengalami kesulitan dalam melakukan penentuan kredit, hal ini dikarenakan terdapat beberapa kriteria yang tidak terpenuhi oleh calon nasabah. Maka dibutuhkan suatu sistem yang dapat mempermudah petugas bank dalam melakukan penentuan kelayakan kredit, yaitu dengan membangun sistem pendukung keputusan kelayakan kredit menggunakan metode credit scoring. Dalam penentuan kredit, metode credit scoring melakukan perhitungan berdasarkan kriteria-kriteria yang ada, sehingga dapat dihasilkan rekomendasi diterima atau ditolaknya sebuah pengajuan kredit. Berdasarkan penelitian yang telah dilakukan, hasil pembuatan sistem pedukung keputusan kelayakan kredit menggunakan metode credit scoring ini adalah mempermudah petugas bank dalam melakukan penentuan kredit. Berdasarkan pengujian menggunakan metode confusion matrix, sistem ini mempunyai performa yang sangat baik dengan tinggkat akurasi 93%.Abstract Credit is a way that can be done to obtain business capital. But sometimes the bank has difficulty in determining credit, this is because there are several criteria that are not met by prospective customers. Then a system is needed to facilitate bank officers in determining credit worthiness, namely by building a creditworthiness decision support system using the credit scoring method. In determining credit, the credit scoring method calculates based on existing criteria, so that recommendations can be generated or rejected for a credit proposal. Based on the research that has been done, the results of the creation of a support system for credit feasibility decisions using the credit scoring method is to facilitate bank officers in making credit determinations. Based on testing using the Confusion Matrix method, this system has a very good performance with an accuracy rate of 93%.

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