Abstract
The automation process helps in improving the efficiency of the detection process, and it may also provide higher detection accuracy by removing the internal subjective human factors in the process. If machine learning can automatically identify bad customers, it will provide considerable benefits to the banking and financial system. The goal is to calculate the credit score and categorize customers into good or bad. Algorithms of machine learning library is used to classify the data sets of finance sectors. A large volume of multi structured customer data is generated. When the quality of this data is incomplete the exactness of study is reduced. In the proposed system, we provide machine learning algorithms for effective prediction of various occurrences in societies. We experiment the altered estimate models over real-life bank data collected. Compared to several typical estimate algorithms, the calculation exactness of our proposed algorithm is high.
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References
Peng Y, Xu R, Zhao H, Zhou Z, Wu N, Yang Y (2017) Random walk based trade reference computation for personal credit scoring. In: 2017 IEEE 13th international symposium on autonomous decentralized system (ISADS)
Li W (2011) An empirical study on credit scoring model for credit card by using data mining technology. In: 2011 seventh international conference on computational intelligence and security
Rushin G, Stancil C, Sun M, Adams S, Beling P (2017) Horse race analysis in credit card fraud—deep learning, logistic regression, and gradient boosted tree. In: 2017 systems and information engineering design symposium (SIEDS)
Liu Y, Du J, Wang F (2013) Non-negative matrix factorization with sparseness constraints for credit risk assessment. In: Proceedings of 2013 IEEE international conference on grey systems and intelligent services (GSIS)
Kraus A (2014) Recent methods from statistics and machine learning for credit scoring
Acknowledgments
I would like to express my special thanks of gratitude to my guide Ch. Ramesh, Assistant Professor in G. Narayanamma Institute of Technology and Science, as well as our head of the department Information technology Dr I. Ravi Prakash Reddy in G. Narayanamma Institute of Technology and Science, who gave me the golden opportunity to do this wonderful project, which also helped me in doing a lot of Research and I came to know about so many new things I am really thankful to them. Secondly I would also like to thank my parents and friends who helped me a lot in finalizing this paper within the limited time frame.
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Kovvuri, R.S., Cheripelli, R. (2020). Credit Risk Valuation Using an Efficient Machine Learning Algorithm. In: Satapathy, S.C., Raju, K.S., Shyamala, K., Krishna, D.R., Favorskaya, M.N. (eds) Advances in Decision Sciences, Image Processing, Security and Computer Vision. ICETE 2019. Learning and Analytics in Intelligent Systems, vol 4. Springer, Cham. https://doi.org/10.1007/978-3-030-24318-0_74
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DOI: https://doi.org/10.1007/978-3-030-24318-0_74
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