Comparison of Several Data Mining Methods in Credit Card Default Prediction

Yang, Shenghui; Zhang, Haomin

doi:10.4236/iim.2018.105010

Cited by 24 publications

(13 citation statements)

References 3 publications

(2 reference statements)

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“…After that the golden, bronze, silver and classic clusters with 5580, 5171, 6832 and 5858 customers respectively. Table 5 shows that by comparing our results with paper [7], [22]- [24]; we found that our proposed model achieved the best result in the accuracy measures. The earlier researches we have found using the same dataset and the same technique (ANN).…”

Section: ) Neural Network Evaluationmentioning

confidence: 72%

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Improve Profiling Bank Customer’s Behavior Using Machine Learning

Dawood¹,

Elfakhrany²,

Maghraby³

2019

IEEE Access

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In the banking industry, credit card evolution is a noticeable occurrence. Each banking system includes a huge dataset for customer's transactions of their credit cards. Therefore, banks would be in need of customer profiling. Profiling bank customer's cognize the issuer's decisions about whom to give banking facilities and what a credit limit to provide. It also helps the issuers get a better understanding of their potential and current customers. In previous research, Customer profiling mainly depends on transaction data or demographic data, but in this research, we merge both data in order to get a more accurate result and minimize the risk. By finding the best technique, it leads to improvement in accuracy and helps banks to get higher profitability by customer satisfaction through a focus on the valuable customer (companies) which consider as the main engine in the bank's profitability. This study aims at using k-mean, improved k-mean, fuzzy c-means and neural networks. The used dataset is labeled and creating a ýnew label as a target for neural network classification is the main aspect of this study, which helps to reduce the clustering execution time and get the best accuracy results. Finally, by comparing the accuracy ratio it shows that the neural network ýis the best clustering technique. INDEX TERMS Profiling, banking, machine learning, k-mean, fuzzy c-mean, neural network classifier.

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Section: ) Neural Network Evaluationmentioning

confidence: 72%

“…In 2018, Yang and Zhang [7] presented a classification model for the credit card default data set in the bank from Taiwan using five clustering algorithms. 10-fold cross-validation was used to get the average area under the curve (AUC) and the correct rate of the model.…”

Section: Literature Surveymentioning

confidence: 99%

Improve Profiling Bank Customer’s Behavior Using Machine Learning

Dawood¹,

Elfakhrany²,

Maghraby³

2019

IEEE Access

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“…General Terms Pattern Recognition, Medical Image Processing. [8] demonstrates that LightGBM or Xgboost has a decent presentation in the expectation of clear-cut reaction factors and has a decent application esteem in the huge information period. The examination of models with boosting and smoothing shows that blunder rate is preferred measurement over region under bend (AUC) proportion.…”

Section: Literature Surveymentioning

confidence: 99%

Certain Investigations on Bank Customer and Relationship Improvement using Machine Learning

Duela¹,

Sara²

2021

Proceedings of the Fist International Conference on Advanced Scientific Innovation in Science, Engineering and Technology, ICA

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Visual information investigation includes utilizing present-day P.C. illustrations and show instruments to investigate information. The utilization of such visual research techniques has gotten progressively across the board all through the sociologies. This proposed framework centres around client information in business banks and expects to utilize visual portrayals and connection strategies to give another vision of client relationship the board or customer relationship management (CRM).Features of the discoveries incorporate that consumer loyalty has a remarkable positive impact on client dedication in the financial assistance industry, particularly with the salary improving, the connection between them turns out to be increasingly robust. At last, recommendations are advanced on the most proficient method to enhance consumer loyalty and increment client dedication. They are continually vigilant for utilizing them to improve their business advantages, for example, right item advancement to right sort of clients, typical usage of self-administration channels, diminished visits to branches for money exchanges, etc. Select Bank's analysis of bank customer awareness about who to bank financing, credit to give something constraints. In addition, it can make the guarantor to show signs of improvement in their potential and existing customers to understand. As customers analyze is significantly essential for such activities, analysis of a client to create a clear framework for banks. The central aspect of this study is to use the used dataset (labelled) and to create a new label as the target for classification, which reduces the clustering execution time and gets the best accuracy results. The data set ('default of credit card clients) is obtained from the archive of UCI (University of California, Irvine) M.L. Repository.

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“…Based on the data at hand, the prediction of perceived age and health is a supervised learning problem and more specifically a regression problem. Therefore, we decided to use the gradient boosting framework Light Gradient Boosting Machine (lightGBM) developed by Microsoft, which has been shown to outperform other gradient boosting frameworks and even neural networks under specific condition [18][19]. We used the Dropouts meet multiple Additive Regression Trees (DART) algorithm as the boosting method within the context of the lightGBM framework [20].…”

Section: Framework For Development Of Predictive Models For Perceivedmentioning

confidence: 99%

Perceived age and perceived health among a Chinese cohort: Does it mean the same thing?

Messaraa¹,

Richard

Walsh³

et al. 2020

Intern J of Cosmetic Sci

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Background & Aims Previous investigations have aimed at investigating parameters affecting age perception on several ethnicities. Perceived health has been a newer focus on Caucasian skin, yet little is known on the skin features used to estimate the health status of Chinese women and we aimed to investigate whether these cues are the same as those used for age perception. Methods Age and health appearance of 276 Chinese female volunteers were estimated from their photographs by 1025 female naïve Chinese graders 20–69 years old. Models were built to predict perceived age and health from topographic, colour and biophysical measured variables, in two subsets of the studied volunteers: below and above 50 years. Machine learning‐based predictive models for age and health perception were built on the collected data, and the interpretability of the models was established by measuring feature importance. Results Age perception was mostly driven by topographic features, particularly eye bags and eyelid sagging in the group below 50 years old. Wrinkles, notably from the lower part of the face and oval of the lower face, were found to be more relevant in the group above 50 years. Health appearance was primarily signalled by skin imperfections and global pigmentation in the subset below 50 years, whereas colour‐related parameters and skin hydration acted as health cues for the subset above 50 years. Conclusion Distinct skin features were acting as cues for age perception and/or health perception and varied per age subset. Their contribution should be borne in mind when designing products for ‘younger looking skin’ and ‘healthier looking skin’.

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Comparison of Several Data Mining Methods in Credit Card Default Prediction

Cited by 24 publications

References 3 publications

Improve Profiling Bank Customer’s Behavior Using Machine Learning

Improve Profiling Bank Customer’s Behavior Using Machine Learning

Certain Investigations on Bank Customer and Relationship Improvement using Machine Learning

Perceived age and perceived health among a Chinese cohort: Does it mean the same thing?

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