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Accurate and Interpretable Machine Learning for Transparent Pricing of Health Insurance Plans [article]

Rohun Kshirsagar, Li-Yen Hsu, Vatshank Chaturvedi, Charles H. Greenberg, Matthew McClelland, Anushadevi Mohan, Wideet Shende, Nicolas P. Tilmans, Renzo Frigato, Min Guo, Ankit Chheda, Meredith Trotter (+3 others)
2021 arXiv   pre-print
Health insurance companies cover half of the United States population through commercial employer-sponsored health plans and pay 1.2 trillion US dollars every year to cover medical expenses for their members. The actuary and underwriter roles at a health insurance company serve to assess which risks to take on and how to price those risks to ensure profitability of the organization. While Bayesian hierarchical models are the current standard in the industry to estimate risk, interest in machine
more » ... learning as a way to improve upon these existing methods is increasing. Lumiata, a healthcare analytics company, ran a study with a large health insurance company in the United States. We evaluated the ability of machine learning models to predict the per member per month cost of employer groups in their next renewal period, especially those groups who will cost less than 95\% of what an actuarial model predicts (groups with "concession opportunities"). We developed a sequence of two models, an individual patient-level and an employer-group-level model, to predict the annual per member per month allowed amount for employer groups, based on a population of 14 million patients. Our models performed 20\% better than the insurance carrier's existing pricing model, and identified 84\% of the concession opportunities. This study demonstrates the application of a machine learning system to compute an accurate and fair price for health insurance products and analyzes how explainable machine learning models can exceed actuarial models' predictive accuracy while maintaining interpretability.
arXiv:2009.10990v2 fatcat:xdrfiu5u4ngxhakw65s6mydn64

Sentiment Analysis of Mobile Product Reviews

Saud Anjum
2019 International Journal for Research in Applied Science and Engineering Technology  
Aashutosh Bhatt, Ankit Patel, Harsh Chheda, Kiran Gawande have tried to find ratings for feature reviews and not service and product review and hence avoiding the computa-tional task for the both.  ... 
doi:10.22214/ijraset.2019.6418 fatcat:caaedd6e3radfmopxoxxttbtpu

Conference Schedule

2021 2021 2nd Global Conference for Advancement in Technology (GCAT)  
Singam ; Samal, Abhaya Kumar; Pramanik, Jitendra*; Pani, Subhendu kumar Ponnampalam, Pirapuraj* Singh, Anmol*; Sharma, Sugandha k, Suresh*; N, Kumaratharan Sanghavi, Rohan N*; KANCHAN, SACHIN RAJESH; Chheda  ...  Sreenivasa T V Session Chair : TRACK A2: Computer Networking /Antennas & Propogation, Control, Instrumentation Tirpude, Saket*; Bharadwaj, Ankit; Holmukhe, Rajesh Retrofitting of a ultracapacitor/battery  ... 
doi:10.1109/gcat52182.2021.9587547 fatcat:y7haykvnezcrjedzk7wksflm3i

Presentation Schedule

2020 2020 IEEE International Conference for Innovation in Technology (INOCON)  
time End Time Room / Hall Name 1 606 Basant Tomar and Narendra Kumar PLC and SCADA based Industrial Automated System 4.00 PM 4.15 PM A-103 (First Floor) 2 623 Mahesh Pawaskar, Ankit  ...  Malignant Lesions 2.01 PM 2.15 PM 3 452 Harshini D, Rushali Jadon, Ranjitha M and Natarajan Subramanyam A single electrode blink for text interface (BCI) 2.16 PM 2.30 PM 4 496 Chintan Chheda  ... 
doi:10.1109/inocon50539.2020.9298402 fatcat:7gj4wwbskjedlbsl7csho3pcf4