An Application of Locally Linear Model Tree Algorithm for Predictive Accuracy of Credit Scoring

Siami, Mohammad; Gholamian, Mohammad Reza; Basiri, Javad; Fathian, Mohammad

doi:10.1007/978-3-642-24443-8_15

Cited by 9 publications

(4 citation statements)

References 16 publications

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“…The artificial intelligence methods include decision trees, random forest, support vector machine, neural networks, and naïve Bayes. Despite the development of more advanced AI methods for evaluating the credit risk of borrowers, simple statistical approaches such as linear discriminate analysis and logistic regression still remain popular because of their high accuracy and ease of implementation [6][7][8][9].…”

Section: Loan Evaluation In P2p Lendingmentioning

confidence: 99%

Credit risk prediction in an imbalanced social lending environment

Namvar¹,

Siami²,

Rabhi³

et al. 2018

IJCIS

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Credit risk prediction is an effective way of evaluating whether a potential borrower will repay a loan, particularly in peer-to-peer lending where class imbalance problems are prevalent. However, few credit risk prediction models for social lending consider imbalanced data and, further, the best resampling technique to use with imbalanced data is still controversial. In an attempt to address these problems, this paper presents an empirical comparison of various combinations of classifiers and resampling techniques within a novel risk assessment methodology that incorporates imbalanced data. The credit predictions from each combination are evaluated with a G-mean measure to avoid bias towards the majority class, which has not been considered in similar studies. The results reveal that combining random forest and random under-sampling may be an effective strategy for calculating the credit risk associated with loan applicants in social lending markets.

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Section: Loan Evaluation In P2p Lendingmentioning

confidence: 99%

Credit risk prediction in an imbalanced social lending environment

Namvar¹,

Siami²,

Rabhi³

et al. 2018

IJCIS

Self Cite

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“…CDRs that are available in telecom databases are raw data, and Data mining can change them to meaningful information to solve business challenges and issues [7]. Data mining is the process of extracting hidden patterns, rules and behavior from a large amount of data [8,10].…”

Section: A Crm and Data Miningmentioning

confidence: 99%

A customer segmentation framework for targeted marketing in telecommunication

Namvar

Ghazanfari

Naderpour

2017

2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)

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Telecommunication industry is highly competitive, and mass marketing is not applicable anymore. Moreover, Mobile customers have different behaviors that urge telecom industries to differentiate their strategies to meet customers' needs. At the same time, mobile operators have an enormous amount of customer records, and data-driven approaches can help them to draw insights from this huge amount of data. Therefore, a data-driven segmentation approach can support marketing strategies to tailor their marketing plans. In this research, we adopt behavior and beneficial segmentation in a two-dimensional framework to segment customers. The results indicate that our method has an outstanding performance for customer segmentation. Moreover, we have recommended some marketing strategies based on each segment's behavior with the aim of increasing in Average Revenue Per User (ARPU) and decreasing in marketing expenses.

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“…To the best of our knowledge, in the credit scoring literature, the LOLIMOT algorithm is one the most accurate models (Siami, Gholamian, Basiri, and Fathian 2011).…”

Section: Introductionmentioning

confidence: 99%

An application of locally linear model tree algorithm with combination of feature selection in credit scoring

Siami

Gholamian

Basiri

2013

International Journal of Systems Science

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Nowadays, credit scoring is one of the most important topics in the banking sector. Credit scoring models have been widely used to facilitate the process of credit assessing. In this paper, an application of the locally linear model tree algorithm (LOLIMOT) was experimented to evaluate the superiority of its performance to predict the customer's credit status. The algorithm is improved with an aim of adjustment by credit scoring domain by means of data fusion and feature selection techniques. Two real world credit data sets -Australian and German -from UCI machine learning database were selected to demonstrate the performance of our new classifier. The analytical results indicate that the improved LOLIMOT significantly increase the prediction accuracy.

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An Application of Locally Linear Model Tree Algorithm for Predictive Accuracy of Credit Scoring

Cited by 9 publications

References 16 publications

Credit risk prediction in an imbalanced social lending environment

Credit risk prediction in an imbalanced social lending environment

A customer segmentation framework for targeted marketing in telecommunication

An application of locally linear model tree algorithm with combination of feature selection in credit scoring

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