A Customer Credit Visual Analytics Model Based on Markov Logic Networks

Abstract:

Customer credit evaluation is very important for customer relationship management in Enterprise Resource Planning. However, how to evaluate the customers’ credit is a complicated problem. In this paper, we present a Customer Credit Visual Analysis Model (CCVAM) that can be used to evaluate and classify the credibility of new customers according to the historical data about past customers. The model is based on Markov Logic Networks (MLNs) that combines probability and first-order logic with a weight attached to each formula. In this model, the basic rules or indexes based on expert knowledge are transformed into the representation of normal form. Then MLNs is obtained by combining first-order logic and probabilistic graphical models of the rules. After acquiring the weights attached to the rules in a first-order logic knowledge base, the model provides an interface for visualize the corresponding relationships among the rules and weights. The model has been applied to grade clients’ credit degree in an international enterprise and achieved anticipative results. This model can also be used in other areas where level evaluation is required.

• Periodical: Key Engineering Materials (Volumes 474-476)
• Pages: 1874-1880
• DOI: https://doi.org/10.4028/www.scientific.net/KEM.474-476.1874
• Online since: April 2011
• Authors: Zhen Zi Chen, Yi Chen
• Keywords: CCVAM, Customer Credit Evaluation, Markov Logic Networks (MLNs), Visual Analytics

References

[1] Jordan M I: Graphical models in Special Issue on Bayesian Statistics, Statistical Science (2004), 19(1): 140-155.

[2] M Richardson, P Domingos: Markov Logic Networks, Proc. of the ICML Workshop on Statistical Relational Learning and its Connections to Other Fields (2004), pages 49-54.

[3] Michael JABerry, Gordon S. Linoff: Data Mining: Application in Marketing, Sales and Customer Relationship Management, Beijing: Mechanical Industry Press ( 2006).

[4] Junjie Lou, CongFu XU, Chunliang Hao: the Physical Analysis Improvement Algorithm Based on Markov Logic Networks, Computer Science( 2010), Volume 37, No. 8, pages 243-247.

[5] Vapnik V N: The Nature of Statistical Learning Theory, Tsinghua University Press (2000).

[6] Shuyang Sun, Dahu Liu, Chengmin Sun, Guanli Huang: Survey of Markov Logic Networks, Computer Application Research (2007), Volume 24, No. 2, pages 1-3.

[7] Congfu XU, Feng Chen, Jing Fan: Several Cutting-Edge Artificial Intelligence Technology and its Application in the Against Information and Prospect, Communication Confrontation (2007), pages 8-11.

[8] Mitchell T M: The Discipline of Machine Learning, Technique Report CMU-ML (2006), pages 06-108.

[9] Domingos P, Kok Stanley, Poon H, et al: Unifying Logical and Statistical AI, Proceedings of 21st National Conference on Artificial Intelligence (AAAI- 2006), Boston, MA: 2- 7.

[10] Bilenko M, Mooney R: On Evaluation and Training-Set Construction for Duplicate Detection Workshop on Data Cleaning (2003), pages 7-12. Proc. KDD-03.

[11] Ning Zhou, Yufeng Zhang, Liyi Zhang: Information Visualization and Knowledge Retrieval, Beijing: Science Press (2005).

[12] Maolin Huang, NGUYEN Quang Vinh: the Visualization of Large-Scale Relational Graph with Multi-Level Clustering, Journal of Software (2008), Vol. 19, No. 8, PP. 1933-(1946).

[13] Linming Zhao: Credit Card User's Classifying Rule Acquirement Based on Data Warehouse Theory, Micro Computer Information (2008), Volume 24, No. 3-3, pages 160-161.

[14] Xueli Zhang, Zhongyuan Yang: Evaluation Model of Bank Credit Based on Combination Weights, Commercial Research (2010), No. 9, pages 36-38.

[15] Xiaolian Meng, Yu Yang: The Study on the Customer Identifying Model Based on Customer Value in Commercial Banks, Value Engineering (2007), No. 6, pages 6-10.