Classification methods applied to credit scoring: Systematic review and overall comparison

Louzada, Francisco; Ara, Anderson; Fernandes, Guilherme B.

doi:10.1016/j.sorms.2016.10.001

Cited by 136 publications

(146 citation statements)

References 152 publications

(238 reference statements)

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“…Risk management and investment banking [33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51] classification (DT, NN, SVM, NB, LR), k-mean clustering UCI Repository International Dataset [41,42,45], Australia [51], Iran [37], Indonesia [34], China [35], German [36,38,39,51], Taiwan [51], US [49], Canada [46] Credit scoring, credit granting, risk management for peer-to-peer lending.…”

Section: References Key Techniques Regions Purposesmentioning

confidence: 99%

“…For instance, Lessmann et al [43] reported on relevant research up until 2014 and conducted comprehensive experiments with real life Australian and German credit data sets for seeking the optimal classifier. Louzada et al [44] recently produced a systematic review that specifically focused on the applications of classification techniques for credit scoring. Here, the main classification methods for credit scoring were summarized and introduced along with a detailed analysis of theoretical and paradigm trends.…”

Section: Risk Management and Investment Bankingmentioning

confidence: 99%

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Digitalisation and Big Data Mining in Banking

Hassani

Huang

Silva

2018

BDCC

120

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Banking as a data intensive subject has been progressing continuously under the promoting influences of the era of big data. Exploring the advanced big data analytic tools like Data Mining (DM) techniques is key for the banking sector, which aims to reveal valuable information from the overwhelming volume of data and achieve better strategic management and customer satisfaction. In order to provide sound direction for the future research and development, a comprehensive and most up to date review of the current research status of DM in banking will be extremely beneficial.Since existing reviews only cover the applications until 2013, this paper aims to fill this research gap and presents the significant progressions and most recent DM implementations in banking post 2013. By collecting and analyzing the trends of research focus, data resources, technological aids, and data analytical tools, this paper contributes to bringing valuable insights with regard to the future developments of both DM and the banking sector along with a comprehensive one stop reference table. Moreover, we identify the key obstacles and present a summary for all interested parties that are facing the challenges of big data.

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Section: References Key Techniques Regions Purposesmentioning

confidence: 99%

Section: Risk Management and Investment Bankingmentioning

confidence: 99%

Digitalisation and Big Data Mining in Banking

Hassani

Huang

Silva

2018

BDCC

120

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“…As a result of such use of SIS in insurance, valuable expert time can be allocated where it is most needed while keeping the risk assessment up-to-date and available for all of its users. Automatic credit risk scoring systems, based on ML algorithms, are behind most current credit decisions and aid in calculating insurance premiums to cover different types of credit based on the risk of defaulting payment (Louzada, Ara, & Fernandes, 2016).…”

Section: Predictive Risk Intelligence In Insurancementioning

confidence: 99%

Ethical Implications of Predictive Risk Intelligence

Jiya

2019

The ORBIT Journal

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This paper presents a case study on the ethical issues that relate to the use of Smart Information Systems (SIS) in predictive risk intelligence. The case study is based on a company that is using SIS to provide predictive risk intelligence in supply chain management (SCM), insurance, finance and sustainability. Thepaper covers an assessment of how the company recognises ethical concerns related to SIS and the ways it deals with them. Data was collected through a document review and two in-depth semi-structured interviews.Results from the case study indicate that the main ethical concerns with the use of SIS in predictive risk intelligence include protection of the data being used in predicting risk, data privacy and consent from those whose data has been collected from data providers such as social media sites. Also, there are issues relating to the transparency and accountability of processes used in predictive intelligence. The interviews highlighted the issue of bias in using the SIS for making predictions for specific target clients. The last ethical issue was related to trust and accuracy of the predictions of the SIS. In response to these issues,the company has put in place different mechanisms to ensure responsible innovation through what it calls Responsible Data Science. Under Responsible Data Science, the identified ethical issues are addressed by following a code of ethics, engaging with stakeholders and ethics committees.This paper is important because it provides lessons for the responsible implementation of SIS in industry, particularly for start-ups. The paper acknowledges ethical issues with the use of SIS in predictive risk intelligence and suggests that ethics should be a central consideration for companies and individuals developing SIS to create meaningful positive change for society.ORBIT Journal DOI: https://doi.org/10.29297/orbit.v2i2.112 2

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“…General literature reviews of issues related to credit scoring modeling may be found in Thomas et al (2005), Abdou & Pointon (2011), Lessmann et al (2015), Louzada et al (2016) and Für et al (2017), for instance.…”

Section: Bibliographic Reviewmentioning

confidence: 99%

Application of bayesian additive regression trees in the development of credit scoring models in Brazil

Filho

Artes

2018

Prod.

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Paper aims: This paper presents a comparison of the performances of the Bayesian additive regression trees (BART), Random Forest (RF) and the logistic regression model (LRM) for the development of credit scoring models. Originality:It is not usual the use of BART methodology for the analysis of credit scoring data. The database was provided by Serasa-Experian with information regarding direct retail consumer credit operations. The use of credit bureau variables is not usual in academic papers.Research method: Several models were adjusted and their performances were compared by using regular methods. Main findings:The analysis confirms the superiority of the BART model over the LRM for the analyzed data. RF was superior to LRM only for the balanced sample. The best-adjusted BART model was superior to RF. Implications for theory and practice:The paper suggests that the use of BART or RF may bring better results for credit scoring modelling. KeywordsCredit. Machine learning. Logistic regression. BART. Random Forest.

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Classification methods applied to credit scoring: Systematic review and overall comparison

Cited by 136 publications

References 152 publications

Digitalisation and Big Data Mining in Banking

Digitalisation and Big Data Mining in Banking

Ethical Implications of Predictive Risk Intelligence

Application of bayesian additive regression trees in the development of credit scoring models in Brazil

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