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Use of Artificial Intelligence for Health Insurance Claims Automation

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Advances in Machine Learning and Computational Intelligence

Part of the book series: Algorithms for Intelligent Systems ((AIS))

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Abstract

The research focuses on the automation of health insurance claims by using artificial intelligence, more specifically, machine learning. In today’s world, where every bit of a record collected is considered information and where every bit of this information plays a vital role in the future decisions, traditional, and manual methods of determining whether a claim made are authentic or fake, of deciding whether to accept or reject that claim in the health insurance business are no longer viable. On the other hand, artificial intelligence the ability of machines to perform equal or more than a human mind is taking over the world. Applying those abilities in place of traditional claim assessment, we get a system that is not only efficient and fast, but it also finds the trends and patterns in the data that were previously not known to exist. The motivation behind this project is to save time, be efficient, avoid tedious manual work, and most importantly human error. The implemented system will help to distinguish between absolutely deserved and undeserved health insurance claims. 

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References

  1. R. Malhotra, S. Sharma, Machine Learning in Insurance (2018)

    Google Scholar 

  2. J.M. Johnson, T.M. Khoshgoftaar, Deep Learning and Data Sampling with Imbalanced Big Data (2019)

    Google Scholar 

  3. E.A. Duman, S. Sağıroğlu, Health Care Fraud Detection Methods and New Approaches (2017)

    Google Scholar 

  4. R.A. Bauder, T.M. Khoshgoftaar, Medicare Fraud Detection Using Machine Learning Methods (2017)

    Google Scholar 

  5. R.A. Bauder, T.M. Khoshgoftaar, A. Richter, M. Herland, Predicting Medical Provider Specialties to Detect Anomalous Insurance Claims (2016)

    Google Scholar 

  6. R.A. Bauder, T.M. Khoshgoftaar, M. Herland, Medical Provider Specialty Predictions for the Detection of Anomalous Medicare Insurance Claims (2017)

    Google Scholar 

  7. M. Kirlidog, C. Asuk, A Fraud Detection Approach with Data Mining in Health Insurance (2012)

    Google Scholar 

  8. H. Shin, H. Park, J. Lee, W.C. Jhee, A Scoring Model to Detect Abusive Billing Patterns in Health Insurance Claims

    Google Scholar 

  9. M. Kumar, R. Ghani, Z.-S. Mei, Data Mining to Predict and Prevent Errors in Health Insurance Claims Processing, in Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2010)

    Google Scholar 

  10. Ultra-modern medicine: examples of machine learning in healthcare (2019)

    Google Scholar 

  11. System and method for detecting healthcare insurance fraud (2014)

    Google Scholar 

  12. V. Rawte, G. Anuradha, Fraud Detection in Health Insurance Using Data Mining Techniques (2015)

    Google Scholar 

  13. H.C. Koh, G. Tan, Data Mining Applications in Healthcare (2005)

    Google Scholar 

  14. M. Tyler, N. Basant, P. Robin, S. Rahman, Healthcare Insurance Claim Fraud Detection Using Datasets Derived from Multiple Insurers (2010)

    Google Scholar 

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Correspondence to Siddhaling Urolagin .

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Singh, J., Urolagin, S. (2021). Use of Artificial Intelligence for Health Insurance Claims Automation. In: Patnaik, S., Yang, XS., Sethi, I. (eds) Advances in Machine Learning and Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-5243-4_35

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  • DOI: https://doi.org/10.1007/978-981-15-5243-4_35

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-5242-7

  • Online ISBN: 978-981-15-5243-4

  • eBook Packages: EngineeringEngineering (R0)

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