Personal values and credit scoring: new insights in the financial prediction

Liberati, Caterina; Camillo, Furio

doi:10.1080/01605682.2017.1417684

Cited by 20 publications

(12 citation statements)

References 51 publications

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“…In comparison, Berg et al (2018) gained an AUC of around 0.73 when using digital footprint characteristics, Iyer et al In some respects, it is difficult to compare AUC results across studies because they all contain different additional variables that often contribute significantly to predictive accuracy. In psychometric studies, Dlogosch (2017) gained an AUC of around 0.67 and Liberati and Camillo (2018) gained a figure of 0.85. In most cases, we gain equal or higher predictive accuracy than published studies from psychometric or psychometric and alternative data.…”

Section: Discussionmentioning

confidence: 99%

“…In contrast, Liberati and Camillo (2018) extracted six psychological constructs using principal components analysis from responses to a Semiometrie that had been administered by an Italian bank. The six dimensions were interpreted as being along with the participation, duty/pleasure, attachment/detachment, sublimation/materialism, idealisation/pragmatism and humility/sovereignty scales.…”

Section: Literature Reviewmentioning

confidence: 99%

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Enhancing credit scoring with alternative data

Djeundje

Crook

Calabrese

et al. 2021

Expert Systems with Applications

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Hundreds of millions of people in low-income economies do not have a credit or bank account because they have insufficient credit history for a credit score to be ascribed to them. In this paper, we evaluate the predictive accuracy of models using alternative data, that may be used instead of credit history, to predict the credit risk of a new account.Without alternative data, the type of data that is typically available is demographic data. We show that a model that contains email usage and psychometric variables, as well as demographic variables, can give greater predictive accuracy than a model that uses demographic data only and that the predictive accuracy is sufficiently high for the demographic and email data to be used when conventional credit history data is unavailable. The same applies if merely psychometric data is included together with demographic data. However, we show that different randomly selected training: test sample splits give a wide range of predictive accuracies. In the second part of the paper, using two datasets that include only email usage as a predictor, we compare the predictive performances of a wide range of machine learning and statistical classifiers. We find that some classifiers applied to these alternative predictors give sufficiently accurate predictions for these variables to be used when no other data is available.

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Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Enhancing credit scoring with alternative data

Djeundje

Crook

Calabrese

et al. 2021

Expert Systems with Applications

View full text Add to dashboard Cite

show abstract

“…Their data was extracted from mobile phones, and the authors concluded that those features helped improve the results. In [Liberati and Camillo 2018], the authors explore features that come from the customers' psychological traits and found that they decreased the employed models' error.…”

Section: Data Acquisitionmentioning

confidence: 99%

Analysis of Distinct Feature Groups in the Credit Scoring Problem

Vercosa

Lira

Monteiro

et al. 2021

jidm

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Registration and financial data have been traditionally used for the credit scoring problem. However,slight improvements in the reliability of the scores positively impacts financial companies. Therefore, exploring newfeatures is a strategic task. This work analyzes the importance of new feature groups not commonly employed forthe credit scoring task and others already used. We categorized features from open credit scoring datasets, suchas German and Australian and compared their groups with the ones of a company dataset used in this work. Ourdataset contains unusual feature groups, such as historical, geolocation, web behavior, and demographic data. In ouranalyzes, we first conducted bivariate tests with each feature-pair to assess their individual importance. Secondly, weran XGBoost machine learning model with each feature group to evaluate each group importance. We also appliedfeature selection with binary Particle Swarm Optimization to assess the groups importance when combined. Next, weemployed correlation tests to find inner and inter-correlation among the features groups. Finally, we used the companydataset and employed AdaBoost, Multilayer Perceptron, and XGBoost algorithms to find the best model for the task.Some of our main findings were that the unusual features added a slight improvement to registration features. We alsodetected reasonable inner correlation among some feature groups and found that all groups were relevant for the taskwith the Historical Group as the most promising. Lastly, XGBoost obtained the best performance over AdaBoost andMultilayer-perceptron for the task.

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“…Their data were extracted from mobile phones and the authors concluded that those features were useful to improve the results. In [Liberati and Camillo 2018] the authors explore features that come from the psychological trait of the customers and found that they decreased the error of the employed models. Encouraged by recent success obtained by the novel features explored in other works, our article goes further in this endeavor and investigates novel feature groups obtained mainly through web crawling and including categories such as demographic, social networks, social programs, and web.…”

Section: Data Acquisitionmentioning

confidence: 99%

Impact of Unusual Features in Credit Scoring Problem

Vercosa¹,

Lira²,

Monteiro³

et al. 2020

Anais Do Symposium on Knowledge Discovery, Mining and Learning (KDMiLe 2020)

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Standard features used for Credit Scoring includes mainly registration and financial data from customers. However, exploring new features is of great interest for financial companies, since slight improvements in the person score directly impact the company revenue. In this work, we categorize features from open credit scoring datasets and compare them with the features found in a real company dataset. The company dataset contains unusual feature groups such as historical, geolocation, web behavior, and demographic data. We performed bivariate tests using the Kolmogorov-Smirnov metric and features to assess the performance of the particular feature groups. We also generated a score of good payer by using AdaBoost, Multilayer Perceptron, and XGBoost algorithms. Then, we analyzed the results with different metrics and compared them with the real company results. Our main finding was that these features added a small improvement to current datasets. We also identified the most promising feature groups and noticed that the tuned XGBoost performed better than the company solution in three out of four deployed metrics.

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Personal values and credit scoring: new insights in the financial prediction

Cited by 20 publications

References 51 publications

Enhancing credit scoring with alternative data

Enhancing credit scoring with alternative data

Analysis of Distinct Feature Groups in the Credit Scoring Problem

Impact of Unusual Features in Credit Scoring Problem

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