Using Interpretable Machine Learning Methods: An Application to Health Insurance Fraud Detection

January 2024

Authors

Satya Sai Mudigonda, AIAI
Adjunct Professor of Actuarial Data Science
Sri Sathya Sai Institute of Higher Learning

Prof Pallav Kumar Baruah, PhD
Professor of Computer Science
Sri Sathya Sai Institute of Higher Learning

Phani Krishna Kandala, AIAI
Visiting Faculty of Actuarial Data Science
Sri Sathya Sai Institute of Higher Learning

Rohan Yashraj Gupta, PhD, ASA, AIA
Visiting Faculty of Actuarial Data Science
Sri Sathya Sai Institute of Higher Learning

Srinand N Hegde
MSc Mathematics student specializing in Actuarial Science
Sri Sathya Sai Institute of Higher Learning

Sumanth Chebrolu
MSc Mathematics student specializing in Actuarial Science
Sri Sathya Sai Institute of Higher Learning

Description

This project establishes a foundational framework for implementing interpretable machine learning techniques in the context of health insurance fraud detection. Machine learning algorithms excel at constructing intricate models by discerning patterns in data, yet the risk of overfitting to training data necessitates rigorous testing by modelers and users. While certain validation practices for linear models apply to machine learning, the challenge of interpretability remains pronounced. This report endeavors to enhance transparency and understanding in the realm of health insurance fraud detection through interpretable machine learning.

Materials

Using Interpretable Machine Learning Methods

Acknowledgments

The researchers’ deepest gratitude goes to those without whose efforts this project could not have come to fruition: the Project Oversight Group and others for their diligent work overseeing questionnaire development, analyzing and discussing respondent answers, and reviewing and editing this report for accuracy and relevance. Any opinions expressed may not reflect their opinions nor those of their employers. Any errors belong to the authors alone.

The authors also thank the Project Oversight Group for their diligent work overseeing project development and reviewing and editing this report for accuracy and relevance.

Project Oversight Group members:
R. Dale Hall, FSA, MAAA, CERA, CFA, Managing Director, Research, Society of Actuaries
Achilles Natsis, FSA, Health Research Actuary, Society of Actuaries

The authors would also like to thank the following members of SSSIHL Center of Excellence for Actuarial Data Science (CADS) for their significant work on this project.

Pyla Pavan, Postgraduate in Actuarial Data Science, SSSIHL
Hima Sai Swaroop Srisailam, Postgraduate in Actuarial Data Science, SSSIHL
Padmanaban Aniruddha, Postgraduate in Actuarial Data Science, SSSIHL
Reddy A. Sai Kumar, Postgraduate in Actuarial Data Science, SSSIHL
Lalith Adithya Kalur, Postgraduate in Actuarial Data Science, SSSIHL
Sai Kumar Varanasi, Postgraduate in Actuarial Data Science, SSSIHL
Sai Siddhanth S, Postgraduate in Actuarial Data Science, SSSIHL
Abhiishek Sanjeev Chugh, Postgraduate in Actuarial Data Science, SSSIHL
Sankar Krishna, Postgraduate in Actuarial Data Science, SSSIHL
Eswar Prem Sai Gupta, Postgraduate in Actuarial Data Science, SSSIHL
P Sunil Kumar, Faculty in Actuarial Data Science, SSSIHL

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