Consumer credit scoring using an artificial immune system algorithm

Leung, Kevin; Cheong, F; Cheong, Christopher

doi:10.1109/cec.2007.4424908

Cited by 21 publications

(18 citation statements)

References 30 publications

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“…In assessing personal consumer creditworthiness explanatory variables can include income, age, occupation, current employment status, past borrowing record etc. [118,124,69]. Closely associated streams of academic literature include corporate failure or bankruptcy prediction [6], and the reverse engineering of the bond-rating models used by rating firms such as Standard & Poor's (S&P), Moody's, Fitches' or Dominion Bond Rating Service [43,39,48].…”

Section: Credit Risk Assessmentmentioning

confidence: 99%

Natural Computing in Finance – A Review

Brabazon¹,

Dang²,

Dempsey³

et al. 2012

Handbook of Natural Computing

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Section: Credit Risk Assessmentmentioning

confidence: 99%

Natural Computing in Finance – A Review

Brabazon¹,

Dang²,

Dempsey³

et al. 2012

Handbook of Natural Computing

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show abstract

“…Fisher's publication in 1936 is known as the first publication that introduce credit scoring system [8]. Recent years, there are many studies on credit scoring such as [4], [5], [7], [10], [16], [11], [8], [19], [20]. It is interesting to study because of the complexity in processes and data behavior that changes dynamically.…”

Section: Introductionmentioning

confidence: 99%

AN IT2FS MODEL FOR SHARIA CREDIT SCORING: ANALYSIS & DESIGN

Sidik

Djatna

Buono

2014

Jurnal Sistem Informasi

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Credit scoring system is a classic problem which is still interesting to study. There are many studies on credit scoring. But, most of them only discuss feasibility analysis. In fact, credit scoring system should accommodate all processes from feasibility analysis until the end of contract. This study is aimed to analyze and design scoring of default status and fines computation processes in Islamic bank. BPMN 2.0 was used to model their processes. Beside that, this study proposed new mechanisms and algorithms using Interval Type-2 Fuzzy Sets for maintaining Sharia rules and fairness guarantee. The results showed that the new methods offer more fair and comply to sharia than existing methods. Keywords : information system, credit scoring, bank sharia, economy AbstrakSistem penilaian kredit merupakan masalah klasik yang masih menarik untuk dikaji. Terdapat banyak studi tentang penilaian kredit. Namun, kebanyakan hanya membahas analisis kelayakan. Bahkan, sistem penilaian kredit harus mengakomodasi semua proses dari analisis kelayakan sampai akhir kontrak. Penelitian ini bertujuan untuk menganalisis dan desain scoring status default dan proses perhitungan denda di bank syariah. BPMN 2.0 digunakan untuk memodelkan proses tersebut. Selain itu, penelitian ini mengusulkan mekanisme baru dan algoritma menggunakan Interval Type-2 Fuzzy Set untuk menjaga aturan Syariah dan jaminan keadilan. Hasil penelitian menunjukkan bahwa metode baru menawarkan keadilan lebih baik dan mematuhi syariah dibandingkan metode yang ada.Kata Kunci: sistem informasi, credit scoring, bank syariah, ekonomi

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“…Among these approaches, AIS is one of the newest methods that has been applied for the credit scoring purposes. Leung et al [9] proposed a simple AIS (SAIS) algorithm that adopted few key concepts of AIS (affinity measure, cloning, and mutation). They found SAIS a very competitive classifier.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The first investigation into credit scoring was started by Olson and Wu in 2010 to classify credit applications as good or bad payers [8]. Fair and Isaac presented a credit scoring model in the early 60s [9]. Since then, various models have been developed using traditional statistical methods such as discriminant analysis method in [10,11].…”

Section: Introductionmentioning

confidence: 99%

Credit Risk Prediction Using Fuzzy Immune Learning

Kamalloo

Abadeh

2014

Advances in Fuzzy Systems

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The use of credit has grown considerably in recent years. Banks and financial institutions confront credit risks to conduct their business. Good management of these risks is a key factor to increase profitability. Therefore, every bank needs to predict the credit risks of its customers. Credit risk prediction has been widely studied in the field of data mining as a classification problem. This paper proposes a new classifier using immune principles and fuzzy rules to predict quality factors of individuals in banks. The proposed model is combined with fuzzy pattern classification to extract accurate fuzzy if-then rules. In our proposed model, we have used immune memory to remember good B cells during the cloning process. We have designed two forms of memory: simple memory and k-layer memory. Two real world credit data sets in UCI machine learning repository are selected as experimental data to show the accuracy of the proposed classifier. We compare the performance of our immune-based learning system with results obtained by several well-known classifiers. Results indicate that the proposed immune-based classification system is accurate in detecting credit risks.

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Consumer credit scoring using an artificial immune system algorithm

Cited by 21 publications

References 30 publications

Natural Computing in Finance – A Review

Natural Computing in Finance – A Review

AN IT2FS MODEL FOR SHARIA CREDIT SCORING: ANALYSIS & DESIGN

Credit Risk Prediction Using Fuzzy Immune Learning

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