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A multi-objective approach for the prediction of loan defaults

Oluwarotimi Odeh, Praveen Koduru, Allen Featherstone, Sanjoy Das, Stephen M. Welch
2011 Expert systems with applications  
This study applies Fuzzy dominance based Simplex Genetic Algorithm (a multi-objective evolutionary optimization algorithm) in generating decision rules for predicting loan default in a typical credit institution  ...  Often, decisions involving optimizing two or more competing goals simultaneously need to be made, and conventional optimization routines and models are incapable of handling the problems.  ...  Another study [7] used the distance-to-default approach to determine the Value at Risk (VaR) for a sample of farmers' loan portfolios.  ... 
doi:10.1016/j.eswa.2011.01.096 fatcat:jj3ulbjwszaqblsgap2oje6z24

A multi-objective approach for the prediction of loan defaults

Oluwarotimi Odeh, Praveen Koduru, Sanjoy Das, Allen M. Featherstone, Stephen M. Welch
2007 Proceedings of the 9th annual conference on Genetic and evolutionary computation - GECCO '07  
This study applies Fuzzy dominance based Simplex Genetic Algorithm (a multi-objective evolutionary optimization algorithm) in generating decision rules for predicting loan default in a typical credit institution  ...  Often, decisions involving optimizing two or more competing goals simultaneously need to be made, and conventional optimization routines and models are incapable of handling the problems.  ...  Another study [7] used the distance-to-default approach to determine the Value at Risk (VaR) for a sample of farmers' loan portfolios.  ... 
doi:10.1145/1276958.1277370 dblp:conf/gecco/OdehKDFW07 fatcat:ah2c4l4erbcazn7n7vdiuouziu

A COST-SENSITIVE LOGISTIC REGRESSION CREDIT SCORING MODEL BASED ON MULTI-OBJECTIVE OPTIMIZATION APPROACH

Feng Shen, Run Wang, Yu Shen
2019 Technological and Economic Development of Economy  
Therefore, in this paper, a new cost-sensitive logistic regression credit scoring model based on a multi-objective optimization approach is proposed that has two objectives in the cost-sensitive logistic  ...  Credit scoring is an important process for peer-to-peer (P2P) lending companies as it determines whether loan applicants are likely to default.  ...  It was also supported by the Fundamental Research Funds for the Central Universities (no. JBK1801009) and the project of Research Center for System Sciences and Enterprise Development (no. Xq18B07).  ... 
doi:10.3846/tede.2019.11337 fatcat:bxk3vxkmvja63pxl6anfngzhzi

Data‐driven optimization of peer‐to‐peer lending portfolios based on the expected value framework

Ajay Byanjankar, József Mezei, Markku Heikkilä
2021 International Journal of Intelligent Systems in Accounting, Finance & Management  
As the starting point of the model, we use machine-learning algorithms to predict the default probabilities and calculate expected values for the loans based on historical data.  ...  We treat the loan selection process in P2P lending as a portfolio optimization problem, with the aim being to select a set of loans that provide a required return while minimizing risk.  ...  The comparison with the single-objective model, however, was limited, as portfolio return was constrained to have a lower value than the optimal solution of the multi-objective model.  ... 
doi:10.1002/isaf.1490 fatcat:cildgz3ehbbk5il57fzsxrhzfu

Overview of Classification and Risk Evaluation in Multi-dimensional Datasets

Dr. K. Kavitha
2016 International Journal Of Engineering And Computer Science  
The classification of multi-dimensional data is one of the major challenges in data mining and data warehousing.  ...  In a classification problem, each object is defined by its attribute values in multidimensional space.  ...  Credit scoring is defined as a statistical method that is used to predict the probability that a loan applicant will default or become delinquent..  ... 
doi:10.18535/ijecs/v5i3.16 fatcat:emceen3d5bbfxdvkdk6qlr4phm

Borrower-lender Information Fusion for P2P Lending: A Nonparametric Approach

Yanhong Guo, Shuai Jiang, Feiting Chen, Yaocong Li, Chunyu Luo
2019 Ingénierie des Systèmes d'Information  
Then, based on the above two quantitative models and correlation, we define a multi-kernel weight and develop an integrated loan assessment model that can evaluate a loan with both the return and risk.  ...  To this end, we propose an integrated loan evaluation model that exploits and fuses multi-source information from both the borrower and the investor for improving investment decisions in P2P lending.  ...  The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve the quality of the paper.  ... 
doi:10.18280/isi.240307 fatcat:jmqpzjmycvaw3cwvygnw323j6i

Comparison of Profit-Based Multi-Objective Approaches for Feature Selection in Credit Scoring

Naomi Simumba, Suguru Okami, Akira Kodaka, Naohiko Kohtake
2021 Algorithms  
It is clear that the base classifier has a significant impact on the results of multi-objective optimization.  ...  Feature selection is crucial to the credit-scoring process, allowing for the removal of irrelevant variables with low predictive power.  ...  Acknowledgments: The authors would like to thank Agribuddy Ltd. for their kind assistance in providing data. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/a14090260 fatcat:lw27qzw7yncfpk3y474xn3hkie

Variable reduction, sample selection bias and bank retail credit scoring

Andrew Marshall, Leilei Tang, Alistair Milne
2010 Journal of Empirical Finance  
This paper investigates the quantitative and business importance of this bias using a large data set on the performance of retail loans by a major UK retail bank.  ...  It uses a multi-process probit model in order to overcome this problem of non-random sample selection bias. There is significant correlation between the disturbance terms . JEL: G21 G32 C35  ...  The predicted probability of default for single process is straightforward.  ... 
doi:10.1016/j.jempfin.2009.12.003 fatcat:c6zh5mcrbbcpnakl34k4pavj6q

Algorithmic Fairness in Mortgage Lending: from Absolute Conditions to Relational Trade-offs

Michelle Seng Ah Lee, Luciano Floridi
2020 Minds and Machines  
of the decision-maker, as well as forcing a more explicit representation of which values and objectives are prioritised.  ...  We introduce a new approach that considers fairness-not as a binary, absolute mathematical condition-but rather, as a relational notion in comparison to alternative decisionmaking processes.  ...  In other words, if the accuracy of the algorithm is 60% in predicting default, and the loan is for $1 million, then the expected value is $600,000 given there is a 60% chance of full repayment vs. default  ... 
doi:10.1007/s11023-020-09529-4 fatcat:2okzjxh7xjf3hi66tjmr5dticq

Machine learning application in online lending risk prediction [article]

Xiaojiao Yu
2017 arXiv   pre-print
Online leading has disrupted the traditional consumer banking sector with more effective loan processing. Risk prediction and monitoring is critical for the success of the business model.  ...  Top 10 important features from the two models suggest external data such as zhimaScore, multi-platform stacking loans information, and social network information are important factors in predicting loan  ...  The objective of the credit score is to evaluate the risk profiles of potential customers and assess their probability of default.  ... 
arXiv:1707.04831v1 fatcat:lrg54o7dfvcnffjniws5xxpj7a

Technology Credit Scoring Based on a Quantification Method

Yonghan Ju, So Young Sohn
2017 Sustainability  
Credit scoring models are usually formulated by fitting the probability of loan default as a function of individual evaluation attributes.  ...  Typically, these attributes are measured using a Likert-type scale, but are treated as interval scale explanatory variables to predict loan defaults.  ...  Considering a multi-level target can provide more valuable information compared to simple loan default predictions, such as default and non-default types.  ... 
doi:10.3390/su9061057 fatcat:2gqr4keifjcj3hsflt43h4rf24

Improving Investment Suggestions for Peer-to-Peer Lending via Integrating Credit Scoring into Profit Scoring

Yan Wang, Xuelei Sherry Ni
2020 Proceedings of the 2020 ACM Southeast Conference  
Therefore, many studies have used machine learning algorithms to help the lenders identify the "best" loans for making investments.  ...  approach.  ...  The profit scoring approach poses a regression problem that predicts the IRR for each loan and the loans with a high predicted IRR are the good candidates for investors.  ... 
doi:10.1145/3374135.3385272 dblp:conf/ACMse/WangN20 fatcat:fponq52ebjd6hdtcujunzgrnwu

Heterogeneous Information Network based Default Analysis on Banking Micro and Small Enterprise Users [article]

Zheng Zhang, Yingsheng Ji, Jiachen Shen, Xi Zhang, Guangwen Yang
2022 arXiv   pre-print
However, the main obstacle for MSEs involves severe deficiency in credit-related information, which may degrade the performance of prediction.  ...  In this paper, we consider a graph of banking data, and propose a novel HIDAM model for the purpose.  ...  Model learning Our proposed model is to predict the default probability that a MSE 𝑢 will fail to repay the loan.  ... 
arXiv:2204.11849v2 fatcat:jljivqnsrvdczi26valy5o6luy

IGNITE: A Minimax Game Toward Learning Individual Treatment Effects from Networked Observational Data

Ruocheng Guo, Jundong Li, Yichuan Li, K. Selçuk Candan, Adrienne Raglin, Huan Liu
2020 Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence  
On the individual level, it is desirable to capture patterns of hidden confounders that predict treatment assignments.  ...  In this work, we formulate the two desiderata as a minimax game.  ...  Acknowledgments The work is supported by the National Key R&D Program of China (2018AAA0100704) and the China Postdoctoral Science Foundation.  ... 
doi:10.24963/ijcai.2020/618 dblp:conf/ijcai/ChengWZ020 fatcat:kmocgyioivbh5alguamwibopxu

Modelling credit risk of portfolio of consumer loans

M Malik, L C Thomas
2010 Journal of the Operational Research Society  
Such models then can be used as the basis for simulation approaches to estimate the credit risk of portfolios of consumer loans.  ...  One of the issues that the Basel Accord highlighted was that though techniques for estimating the probability of default and hence the credit risk of loans to individual consumers are well established,  ...  Forecasting Multi-Period Hazard Rate and Default Probability To predict the probability of default of customers for one year ahead in calendar time we first predict their hazard rate in time since loan  ... 
doi:10.1057/jors.2009.123 fatcat:e54vinkxebdpbaven42dpcqgbu
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