Mobile User Credit Prediction Based on LightGBM

Guo, Qiangqiang; Zhu, Zhihui; Pei, Hongli; Xu, Fuyong; Lü, Qiang; Zhang, Dianyuan; Wu, Wenqing

doi:10.2991/acsr.k.191223.033

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…XGBoost was used to detect the risk of credit fraud. Similarly, lightGBM has been applied in different financial applications such as the credit predictions of mobile users [28] to be used by digital banks or for cryptocurrency predictions [29]. We underline that our approach presented in Section 4 can be easily used with any boosting algorithm, including LightGBM and XGBoost.…”

Section: Related Workmentioning

confidence: 99%

XtracTree: a Simple and Effective Method for Regulator Validation of Bagging Methods Used in Retail Banking

Charlier,

Makarenkov

2020

Preprint

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Bootstrap aggregation, known as bagging, is one of the most popular ensemble methods used in machine learning (ML). An ensemble method is a supervised ML method that combines multiple hypotheses to form a single hypothesis used for prediction. A bagging algorithm combines multiple classifiers modelled on different sub-samples of the same data set to build one large classifier. Large retail banks are nowadays using the power of ML algorithms, including decision trees and random forests, to optimize the retail banking activities. However, AI bank researchers face a strong challenge from their own model validation department as well as from national financial regulators. Each proposed ML model has to be validated and clear rules for every algorithm-based decision have to be established. In this context, we propose XtracTree, an algorithm that is capable of effectively converting an ML bagging classifier, such as a decision tree or a random forest, into simple "if-then" rules satisfying the requirements of model validation. Our algorithm is also capable of highlighting the decision path for each individual sample or a group of samples, addressing any concern from the regulators regarding ML "black-box". We use a public loan data set from Kaggle to illustrate the usefulness of our approach. Our experiments indicate that, using XtracTree, we are able to ensure a better understanding for our model, leading to an easier model validation by national financial regulators and the internal model validation department.

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Section: Related Workmentioning

confidence: 99%

XtracTree: a Simple and Effective Method for Regulator Validation of Bagging Methods Used in Retail Banking

Charlier,

Makarenkov

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Li Xin et al (Li and Yu, 2020) consider that the traditional grid search method takes a long time to optimize parameters, use random forest for feature selection and optimize the parameters in XGBoost by the improved grid search method. Guo Qiangqiang et al (Guo and Zhu, 2020) consider the Incorporating user behavior problem of credit score prediction in the environment of large sample and high dimension of current data, first analyze the linear correlation to construct a feature set, perform cluster analysis by K-means algorithm and finally construct a credit score model through the LightGBM model. Liu Xiaoya et al (Liu and Wang, 2020) proposed a model based on support vector machine integration, and applied the C4.5 decision tree information entropy gain rate theory to reduce the redundancy attribute.…”

Section: Related Workmentioning

confidence: 99%

Incorporating user behavior flow for user risk assessment

Shan

Ren

et al. 2023

IJWIS

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Purpose Internet marketing underground industry users refer to people who use technology means to simulate a large number of real consumer behaviors to obtain marketing activities rewards illegally, which leads to increased cost of enterprises and reduced effect of marketing. Therefore, this paper aims to construct a user risk assessment model to identify potential underground industry users to protect the interests of real consumers and reduce the marketing costs of enterprises. Design/methodology/approach Method feature extraction is based on two aspects. The first aspect is based on traditional statistical characteristics, using density-based spatial clustering of applications with noise clustering method to obtain user-dense regions. According to the total number of users in the region, the corresponding risk level of the receiving address is assigned. So that high-quality address information can be extracted. The second aspect is based on the time period during which users participate in activities, using frequent item set mining to find multiple users with similar operations within the same time period. Extract the behavior flow chart according to the user participation, so that the model can mine the deep relationship between the participating behavior and the underground industry users. Findings Based on the real underground industry user data set, the features of the data set are extracted by the proposed method. The features are experimentally verified by different models such as random forest, fully-connected layer network, SVM and XGBOST, and the proposed method is comprehensively evaluated. Experimental results show that in the best case, our method can improve the F1-score of traditional models by 55.37%. Originality/value This paper investigates the relative importance of static information and dynamic behavior characteristics of users in predicting underground industry users, and whether the absence of features of these categories affects the prediction results. This investigation can go a long way in aiding further research on this subject and found the features which improved the accuracy of predicting underground industry users.

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“…This study used machine learning to verify the contribution of items related to fuel economy. Machine learning is used in various research fields such as network security [8], transportation engineering [9], finance and credit rating [10,11], and medical science [12] to establish a relationship between a target value (label) and a wide range of data. In the automobile industry, efforts are being undertaken to use machine learning in areas that incur substantial time and cost.…”

Section: Introductionmentioning

confidence: 99%

Fuel Economy Improvement of Urban Buses with Development of an Eco-Drive Scoring Algorithm Using Machine Learning

2021

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Eco-drive is a widely used concept. It can improve fuel economy for different driving behaviors such as vehicle acceleration or accelerator pedal operation, deceleration or coasting while slowing down, and gear shift timing difference. The feasibility of improving the fuel economy of urban buses by applying eco-drive was verified by analyzing data from drivers who achieved high fuel efficiencies in urban buses with a high frequency of acceleration/deceleration and frequent operation. The items that were monitored for eco-drive were: rapid take-off/acceleration/deceleration, accelerator pedal gradient, coasting rate, shift indicator violation, average engine speed, over speed, and gear shifting under low-end engine speed. The monitoring method for each monitored item was set up, and an index was produced using driving data. A fuel economy prediction model was created using machine learning to determine the contribution of each index to the fuel economy. Furthermore, the contribution of each monitoring item was analyzed using the prediction model explainer. Accordingly, points (defined as the eco-drive score) were allocated for each monitoring item. It was verified that this score can represent the eco-drive characteristics based on the relationship between the score and fuel economy. In addition, it resulted in an average annual fuel economy improvement of 12.1%.

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Mobile User Credit Prediction Based on LightGBM

Cited by 5 publications

References 7 publications

XtracTree: a Simple and Effective Method for Regulator Validation of Bagging Methods Used in Retail Banking

XtracTree: a Simple and Effective Method for Regulator Validation of Bagging Methods Used in Retail Banking

Incorporating user behavior flow for user risk assessment

Fuel Economy Improvement of Urban Buses with Development of an Eco-Drive Scoring Algorithm Using Machine Learning

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