scholarly journals Machine Learning (ML) Technologies for Digital Credit Scoring in Rural Finance: A Literature Review

Risks ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 192
Author(s):  
Anil Kumar ◽  
Suneel Sharma ◽  
Mehregan Mahdavi
Keyword(s):  
Machine Learning ◽  
Literature Review ◽  
Financial Institutions ◽  
Developing Economies ◽  
Credit Scoring ◽  
Women Farmers ◽  
Advanced Technology ◽  
Ethical Challenges ◽  
Rural Finance ◽  
Banking Institutions

Rural credit is one of the most critical inputs for farm production across the globe. Despite so many advances in digitalization in emerging and developing economies, still a large part of society like small farm holders, rural youth, and women farmers are untouched by the mainstream of banking transactions. Machine learning-based technology is giving a new hope to these individuals. However, it is the banking or non-banking institutions that decide how they will adopt this advanced technology, to have reduced human biases in loan decision making. Therefore, the scope of this study is to highlight the various AI-ML- based methods for credit scoring and their gaps currently in practice by banking or non-banking institutions. For this study, systematic literature review methods have been applied; existing research articles have been empirically reviewed with an attempt to identify and compare the best fit AI-ML-based model adopted by various financial institutions worldwide. The main purpose of this study is to present the various ML algorithms highlighted by earlier researchers that could be fit for a credit assessment of rural borrowers, particularly those who have no or inadequate loan history. However, it would be interesting to recognize further how the financial institutions could be able to blend the traditional and digital methods successfully without any ethical challenges.

scholarly journals Review of Machine Learning models for Credit Scoring Analysis

2020 ◽  
Vol 16 (1) ◽  
Author(s):  
Madapuri Rudra Kumar ◽  
Vinit Kumar Gunjan
Keyword(s):  
Machine Learning ◽  
Financial Institutions ◽  
Profile Analysis ◽  
Credit Scoring ◽  
Process Models ◽  
Machine Learning Techniques ◽  
Process Conditions ◽  
Learning Models ◽  
Credit Score ◽  
Machine Learning Models

Introduction:Increase in computing power and the deeper usage of the robust computing systems in the financial system is propelling the business growth, improving the operational efficiency of the financial institutions, and increasing the effectiveness of the transaction processing solutions used by the organizations. Problem:Despite that the financial institutions are relying on the credit scoring patterns for analyzing the credit worthiness of the clients, still there are many factors that are imminent for improvement in the credit score evaluation patterns.  Objective:Machine learning is offering immense potential in Fintech space and determining a personal credit score. Organizations by applying deep learning and machine learning techniques can tap individuals who are not being serviced by traditional financial institutions. Methodology:One of the major insights into the system is that the traditional models of banking intelligence solutions are predominantly the programmed models that can align with the information and banking systems that are used by the banks. But in the case of the machine-learning models that rely on algorithmic systems require more integral computation which is intrinsic.  Results:The test analysis of the proposed machine learning model indicates effective and enhanced analysis process compared to the non-machine learning solutions. The model in terms of using various classifiers indicate potential ways in which the solution can be significant. Conclusion: If the systems can be developed to align with more pragmatic terms for analysis, it can help in improving the process conditions of customer profile analysis, wherein the process models have to be developed for comprehensive analysis and the ones that can make a sustainable solution for the credit system management. Originality:The proposed solution is effective and the one conceptualized to improve the credit scoring system patterns.  Limitations: The model is tested in isolation and not in comparison to any of the existing credit scoring patterns. 

scholarly journals Credit Scoring Using Machine Learning by Combing Social Network Information: Evidence from Peer-to-Peer Lending

Information ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 397 ◽  
Author(s):  
Beibei Niu ◽  
Jinzheng Ren ◽  
Xiaotao Li
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Social Network ◽  
Financial Institutions ◽  
Learning Algorithm ◽  
Credit Scoring ◽  
Peer To Peer ◽  
Machine Learning Algorithms ◽  
Loan Default ◽  
Network Information

Financial institutions use credit scoring to evaluate potential loan default risks. However, insufficient credit information limits the peer-to-peer (P2P) lending platform’s capacity to build effective credit scoring. In recent years, many types of data are used for credit scoring to compensate for the lack of credit history data. Whether social network information can be used to strengthen financial institutions’ predictive power has received much attention in the industry and academia. The aim of this study is to test the reliability of social network information in predicting loan default. We extract borrowers’ social network information from mobile phones and then use logistic regression to test the relationship between social network information and loan default. Three machine learning algorithms—random forest, AdaBoost, and LightGBM—were constructed to demonstrate the predictive performance of social network information. The logistic regression results show that there is a statistically significant correlation between social network information and loan default. The machine learning algorithm results show that social network information can improve loan default prediction performance significantly. The experiment results suggest that social network information is valuable for credit scoring.

scholarly journals Literature Review and Analysis of Current Research Status of Rural Financial Efficiency

2020 ◽  
Vol 218 ◽  
pp. 01038
Author(s):  
Yue Ma ◽  
Fu-Xiang Liu
Keyword(s):  
Economic Development ◽  
Literature Review ◽  
Financial Institutions ◽  
Relevant Literature ◽  
Rural Finance ◽  
Financial Efficiency ◽  
Factors Affecting ◽  
Economic Issues ◽  
Rural Economic Development ◽  
The Relationship

Rural economic issues have attracted more and more attention from china, but there are still many problems of rural finance in China. Therefore, it is necessary to sort out the relevant literature on rural financial efficiency, and analyze the rural financial efficiency’s connotation itself, the relationship with rural economic development, the factors affecting its efficiency, rural financial institutions and research methods from both macro and micro aspects. They are finally generalized and summarized to guide the direction of relevant policies.

scholarly journals Federated Learning in a Medical Context: A Systematic Literature Review

2021 ◽  
Vol 21 (2) ◽  
pp. 1-31
Author(s):  
Bjarne Pfitzner ◽  
Nico Steckhan ◽  
Bert Arnrich
Keyword(s):  
Machine Learning ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Data Privacy ◽  
Research Area ◽  
Learning Models ◽  
Related Data ◽  
Private Data ◽  
Large Databases ◽  
Machine Learning Models

Data privacy is a very important issue. Especially in fields like medicine, it is paramount to abide by the existing privacy regulations to preserve patients’ anonymity. However, data is required for research and training machine learning models that could help gain insight into complex correlations or personalised treatments that may otherwise stay undiscovered. Those models generally scale with the amount of data available, but the current situation often prohibits building large databases across sites. So it would be beneficial to be able to combine similar or related data from different sites all over the world while still preserving data privacy. Federated learning has been proposed as a solution for this, because it relies on the sharing of machine learning models, instead of the raw data itself. That means private data never leaves the site or device it was collected on. Federated learning is an emerging research area, and many domains have been identified for the application of those methods. This systematic literature review provides an extensive look at the concept of and research into federated learning and its applicability for confidential healthcare datasets.

A novel multi-stage ensemble model with multiple K-means-based selective undersampling: An application in credit scoring

2021 ◽  
Vol 40 (5) ◽  
pp. 9471-9484
Author(s):  
Yilun Jin ◽  
Yanan Liu ◽  
Wenyu Zhang ◽  
Shuai Zhang ◽  
Yu Lou
Keyword(s):  
Machine Learning ◽  
Predictive Accuracy ◽  
Credit Scoring ◽  
Imbalanced Data ◽  
Ensemble Model ◽  
Selective Sampling ◽  
Machine Learning Methods ◽  
Multi Stage ◽  
Proposed Model ◽  
New Feature

With the advancement of machine learning, credit scoring can be performed better. As one of the widely recognized machine learning methods, ensemble learning has demonstrated significant improvements in the predictive accuracy over individual machine learning models for credit scoring. This study proposes a novel multi-stage ensemble model with multiple K-means-based selective undersampling for credit scoring. First, a new multiple K-means-based undersampling method is proposed to deal with the imbalanced data. Then, a new selective sampling mechanism is proposed to select the better-performing base classifiers adaptively. Finally, a new feature-enhanced stacking method is proposed to construct an effective ensemble model by composing the shortlisted base classifiers. In the experiments, four datasets with four evaluation indicators are used to evaluate the performance of the proposed model, and the experimental results prove the superiority of the proposed model over other benchmark models.

Data Mining-based Financial Statement Fraud Detection: Systematic Literature Review and Meta-analysis to Estimate Data Sample Mapping of Fraudulent Companies Against Non-fraudulent Companies

2021 ◽  
pp. 097215092098485
Author(s):  
Sonika Gupta ◽  
Sushil Kumar Mehta
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Classification Accuracy ◽  
Meta Analysis ◽  
Financial Statement ◽  
Research Articles ◽  
Financial Statement Fraud ◽  
Data Mining Techniques

Data mining techniques have proven quite effective not only in detecting financial statement frauds but also in discovering other financial crimes, such as credit card frauds, loan and security frauds, corporate frauds, bank and insurance frauds, etc. Classification of data mining techniques, in recent years, has been accepted as one of the most credible methodologies for the detection of symptoms of financial statement frauds through scanning the published financial statements of companies. The retrieved literature that has used data mining classification techniques can be broadly categorized on the basis of the type of technique applied, as statistical techniques and machine learning techniques. The biggest challenge in executing the classification process using data mining techniques lies in collecting the data sample of fraudulent companies and mapping the sample of fraudulent companies against non-fraudulent companies. In this article, a systematic literature review (SLR) of studies from the area of financial statement fraud detection has been conducted. The review has considered research articles published between 1995 and 2020. Further, a meta-analysis has been performed to establish the effect of data sample mapping of fraudulent companies against non-fraudulent companies on the classification methods through comparing the overall classification accuracy reported in the literature. The retrieved literature indicates that a fraudulent sample can either be equally paired with non-fraudulent sample (1:1 data mapping) or be unequally mapped using 1:many ratio to increase the sample size proportionally. Based on the meta-analysis of the research articles, it can be concluded that machine learning approaches, in comparison to statistical approaches, can achieve better classification accuracy, particularly when the availability of sample data is low. High classification accuracy can be obtained with even a 1:1 mapping data set using machine learning classification approaches.

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