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Loan Default Prediction Using Machine Learning Techniques

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Innovations in Computer Science and Engineering

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 385))

Abstract

Loans are a very fundamental source of any bank’s revenue, so they work tirelessly to make sure that they only give loans to customers who will not default on the monthly payments. They pay a lot of attention to this issue and use various ways to detect and predict the default behaviors of their customers. However, a lot of the time, because of human error, they may fail to see some key information. This paper proposes a better approach using machine learning approaches like KNN, decision tree, SVM and logistic regression to predict defaulters. The accuracy of these methods will also be tested using metrics like log loss, Jaccard similarity coefficient and F1 Score. These metrics are compared to determine the accuracy of prediction. This can help banks conserve their manpower and fiscal resources by reducing the number of steps they have to take in order to check if somebody is eligible for a loan.

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References

  1. Aha DW, Kibler D, Albert MK (1991) Instance-based learning algorithms. Mach Learn 6:37–66

    Google Scholar 

  2. Beygelzimer A, Kakade S, Langford J (2006) Cover trees for nearest neighbor. In: Proceedings of 23rd international conference on machine learning (ICML 2006)

    Google Scholar 

  3. Brighton H, Mellish C (2002) Advances in instance selection for instance-based learning algorithms. Data Min Knowl Disc 6(2):153–172

    Article  MathSciNet  Google Scholar 

  4. Brodley C (1993) Addressing the selective superiority problem: automatic algorithm/mode class selection. In: Proceedings of the 10th international conference on machine learning (ICML 93). Morgan Kaufmann Publishers Inc, pp 17–24

    Google Scholar 

  5. Cameron-Jones RM (1992) Minimum description length instance-based learning. In: Proceedings of the 5th Australian joint conference on artificial intelligence. Morgan Kaufmann Publishers Inc, pp 368–373

    Google Scholar 

  6. Delany SJ, Bridge D (2006) Feature-based and feature-free textual cbr: a comparison in spam filtering. In: Bell D, Milligan P, Sage P (eds) Proceedings of the 17th Irish conference on artificial intelligence and cognitive science (AICS’06), pp 244–253

    Google Scholar 

  7. Basu S, Kannayaram G, Ramasubbareddy S, Venkatasubbaiah C (2019) Improved genetic algorithm for monitoring of virtual machines in cloud environment. In: Smart intelligent computing and applications. Springer, Singapore, pp 319–326

    Google Scholar 

  8. Somula R, Sasikala R (2018) Round robin with load degree: an algorithm for optimal cloudlet discovery in mobile cloud computing. Scalable Comput Pract Exp 19(1):39–52

    Google Scholar 

  9. Somula R, Anilkumar C, Venkatesh B, Karrothu A, Kumar CP, Sasikala R (2019) Cloudlet services for healthcare applications in mobile cloud computing. In: Proceedings of the 2nd international conference on data engineering and communication technology. Springer, Singapore, pp 535–543

    Google Scholar 

  10. Somula RS, Sasikala R (2018) A survey on mobile cloud computing: mobile computing+ cloud computing (MCC= MC+ CC). Scalable Comput Pract Exp 19(4):309–337

    Article  Google Scholar 

  11. Somula R, Sasikala R (2019) A load and distance aware cloudlet selection strategy in multi-cloudlet environment. Int J Grid High-Perform Comput (IJGHPC) 11(2):85–102

    Article  Google Scholar 

  12. Somula R, Sasikala R (2019) A honeybee inspired cloudlet selection for resource allocation. Smart intelligent computing and applications. Springer, Singapore, pp 335–343

    Chapter  Google Scholar 

  13. Nalluri S, Ramasubbareddy S, Kannayaram G (2019) Weather prediction using clustering strategies in machine learning. J Comput Theor Nanosci 16(5–6):1977–1981

    Article  Google Scholar 

  14. Sahoo KS, Tiwary M, Mishra P, Reddy SRS, Balusamy B, Gandomi AH (2019) Improving end-users utility in software-defined wide area network systems. IEEE Trans Netw Serv Manag

    Google Scholar 

  15. Sahoo KS, Tiwary M, Sahoo B, Mishra BK, Rama Subba Reddy S, Luhach AK (2019) RTSM: response time optimisation during switch migration in software-defined wide area network. IET Wireless Sens Syst

    Google Scholar 

  16. Somula R, Kumar KD, Aravindharamanan S, Govinda K (2020) Twitter sentiment analysis based on US Presidential Election 2016. In: Smart intelligent computing and applications. Springer, Singapore, pp 363–373

    Google Scholar 

  17. Sai KBK, Subbareddy SR, Luhach AK (2019) IOT based air quality monitoring system using MQ135 and MQ7 with machine learning analysis. Scalable Comput Pract Exp 20(4):599–606

    Article  Google Scholar 

  18. Somula R, Narayana Y, Nalluri S, Chunduru A, Sree KV (2019) POUPR: properly utilizing user-provided recourses for energy saving in mobile cloud computing. In: Proceedings of the 2nd international conference on data engineering and communication technology. Springer, Singapore, pp 585–595

    Google Scholar 

  19. Vaishali R, Sasikala R, Ramasubbareddy S, Remya S, Nalluri S. (2017) Genetic algorithm-based feature selection and MOE Fuzzy classification algorithm on Pima Indians Diabetes dataset. In: 2017 International conference on computing networking and informatics (ICCNI). IEEE, pp 1–5

    Google Scholar 

  20. Somula R, Sasikala R (2019) A research review on energy consumption of different frameworks in mobile cloud computing. Innovations in computer science and engineering. Springer, Singapore, pp 129–142

    Chapter  Google Scholar 

  21. Kumar IP, Gopal VH, Ramasubbareddy S, Nalluri S, Govinda K (2020) Dominant color palette extraction by K-means clustering algorithm and reconstruction of image. Data engineering and communication technology. Springer, Singapore, pp 921–929

    Chapter  Google Scholar 

  22. Nalluri S, Saraswathi RV, Ramasubbareddy S, Govinda K, Swetha E (2020) Chronic heart disease prediction using data mining techniques. Data engineering and communication technology. Springer, Singapore, pp 903–912

    Chapter  Google Scholar 

  23. Krishna AV, Ramasubbareddy S, Govinda K (2020) Task scheduling based on hybrid algorithm for cloud computing. International conference on intelligent computing and smart communication 2019. Springer, Singapore, pp 415–421

    Chapter  Google Scholar 

  24. Srinivas TAS, Ramasubbareddy S, Govinda K, Manivannan SS (2020) Web image authentication using embedding invisible watermarking. In: International conference on intelligent computing and smart communication 2019. Springer, Singapore, pp 207–218

    Google Scholar 

  25. Krishna AV, Ramasubbareddy S, Govinda K (2020) A unified platform for crisis mapping using web enabled crowdsourcing powered by knowledge management. In: International conference on intelligent computing and smart communication 2019. Springer, Singapore, pp 195–205

    Google Scholar 

  26. Saraswathi RV, Nalluri S, Ramasubbareddy S, Govinda K, Swetha E (2020) Brilliant corp yield prediction utilizing internet of things. In: Data engineering and communication technology. Springer, Singapore, pp 893–902

    Google Scholar 

  27. Baliarsingh SK, Vipsita S, Gandomi AH, Panda A, Bakshi S, Ramasubbareddy S (2020) Analysis of high-dimensional genomic data using map reduce based probabilistic neural network. Comput Meth Programs Biomed 105625

    Google Scholar 

  28. Lavanya V, Ramasubbareddy S, Govinda K (2020) Fuzzy keyword matching using n-gram and cryptographic approach over encrypted data in cloud. Embedded systems and artificial intelligence. Springer, Singapore, pp 551–558

    Chapter  Google Scholar 

  29. Mahesh B, Kumar KP, Ramasubbareddy S, Swetha E (2020) A review on data deduplication techniques in cloud. Embedded systems and artificial intelligence. Springer, Singapore, pp 825–833

    Chapter  Google Scholar 

  30. Sathish K, Ramasubbareddy S, Govinda K (2020) Detection and localization of multiple objects using VGG net and single shot detection. Emerging research in data engineering systems and computer communications. Springer, Singapore, pp 427–439

    Chapter  Google Scholar 

  31. Rakshitha K, Rao AS, Sagar Y, Ramasubbareddy S (2020) Demonstrating broadcast aggregate keys for data sharing in cloud. Innovations in computer science and engineering. Springer, Singapore, pp 185–193

    Chapter  Google Scholar 

  32. Ramasubbareddy S, Srinivas TAS, Govinda K, Manivannan SS (2020) Comparative study of clustering techniques in market segmentation. Innovations in computer science and engineering. Springer, Singapore, pp 117–125

    Chapter  Google Scholar 

  33. Ramasubbareddy S, Srinivas TAS, Govinda K, Manivannan SS (2020) Crime prediction system. Innovations in computer science and engineering. Springer, Singapore, pp 127–134

    Chapter  Google Scholar 

  34. Sahoo KS, Tiwary M, Sahoo S, Nambiar R, Sahoo B, Dash R (2018) A learning automata-based DDoS attack defense mechanism in software defined networks. In: Proceedings of the 24th annual international conference on mobile computing and networking, pp 795–797

    Google Scholar 

  35. Sahoo KS, Sahoo S, Sarkar A, Sahoo B, Dash R (2017) On the placement of controllers for designing a wide area software defined networks. In: TENCON 2017–2017 IEEE region 10 conference. IEEE, pp 3123–3128

    Google Scholar 

  36. Ramasubbareddy S, Ramasamy S, Sahoo KS, Kumar RL, Pham QV, Dao NN (2020) Cavms: application-aware cloudlet adaption and vm selection framework for multicloudlet environment. IEEE Syst J

    Google Scholar 

  37. Sankar S, Ramasubbareddy S, Chen F, Gandomi AH (2020) Energy-efficient cluster-based routing protocol in internet of things using swarm intelligence. In: 2020 IEEE symposium series on computational intelligence (SSCI). IEEE, pp 219–224

    Google Scholar 

  38. Srinivas AST, Somula R, Govinda K, Saxena A, Reddy PA (2020) Estimating rainfall using machine learning strategies based on weather radar data. Int J Commun Syst 33(13)

    Google Scholar 

  39. Ravindranath V, Ramasamy S, Somula R, Sahoo KS, Gandomi AH (2020) Swarm intelligence-based feature selection for intrusion and detection system in cloud infrastructure. In: 2020 IEEE congress on evolutionary computation (CEC). IEEE, pp 1–6

    Google Scholar 

  40. Attili VR, Annaluri SR, Gali SR, Somula R (2020) Behaviour and emotions of working professionals towards online learning systems: sentiment analysis. Int J Gam Comput-Mediated Simulations (IJGCMS) 12(2):26–43

    Article  Google Scholar 

  41. Sennan S, Ramasubbareddy S, Nayyar A, Nam Y, Abouhawwash M, LOA-RPL: novel energy-efficient routing protocol for the internet of things using lion optimization algorithm to maximize network lifetime

    Google Scholar 

  42. Pulipati S, Somula R, Parvathala BR (2021) Nature inspired link prediction and community detection algorithms for social networks: a survey. Int J Syst Assur Eng Manag 1–18

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of CSE, G. Pullaiah College of Engineering and Technology, Kurnool, India

    T. Aditya Sai Srinivas

  2. Information Technology, VNRVJIET, Hyderabad, Telangana, India

    Somula Ramasubbareddy

  3. Scope, VIT University, Vellore, Tamilnadu, India

    K. Govinda

Authors
  1. T. Aditya Sai Srinivas
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  2. Somula Ramasubbareddy
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  3. K. Govinda
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Editor information

Editors and Affiliations

  1. Guru Nanak Institutions, Ibrahimpatnam, Telangana, India

    H. S. Saini

  2. Guru Nanak Institutions, Ibrahimpatnam, Telangana, India

    Rishi Sayal

  3. Jawaharlal Nehru Technological University, Hyderabad, India

    A. Govardhan

  4. Cloud Computing and Distributed Systems (CLOUDS) Laboratory, University of Melbourne, Melbourne, VIC, Australia

    Rajkumar Buyya

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Aditya Sai Srinivas, T., Ramasubbareddy, S., Govinda, K. (2022). Loan Default Prediction Using Machine Learning Techniques. In: Saini, H.S., Sayal, R., Govardhan, A., Buyya, R. (eds) Innovations in Computer Science and Engineering. Lecture Notes in Networks and Systems, vol 385. Springer, Singapore. https://doi.org/10.1007/978-981-16-8987-1_56

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