Integrating AI/ML Models for Cross-Domain Insurance Solutions: Auto, Home, and Life
DOI:
https://doi.org/10.5281/zenodo.15967819Keywords:
Insurance Fraud Detection, Systematic Modeling Potpourri, Cross-Domain Fraud Detection, Model Pipeline Automation, Explainable AI in Insurance, Sybil Detection in Insurance, End-to-End AI Solutions, Bulk Data Processing for Fraud Detection, Pre-Processing and Feature Engineering, Multi-Task Learning Paradigms, Reduced Labeled Data Modeling, Fraud Detection in Healthcare and Finance, CI/CD for ML in Insurance, Model Performance Monitoring, Auto Claims Settlement AI, Sentiment Analysis in Insurance, Online Parameter Tuning, Ensemble and Calibration Techniques, Cross-Domain Fraud Analytics, Enterprise-Focused AI Solutions.Abstract
Time to spot and eliminate fraud is critical in insurance application and claim processing. While generally, fraud is taken as domain-specific which makes it difficult to leverage already built models for other domains and hence invest in the building time and resources, this work explores a novel systematic modeling potpourri approach that can be utilized for building and integrating fraud detection models just not in Insurance but in much larger adversarial problematics. With this, we leverage model pipelines extensively, implement algorithms covering various algorithms, and identify novel techniques for pre-processing, post-processing, complete missing feature engineering, explainability, augmentation, and bulk data processing along with Sybil Detection specific to insurances. This not only reduces the overall turnaround time for model building by >97% by allowing data scientists to solely focus on data management and explaining results to business users but also provides a model performance boost of >5% by consuming the best of each algorithm pipeline without having to reinvent the wheel constantly and extensively.
This paper presents a comprehensive discussion of the Research and Development work undertaken at a data science organization. It deals with end-to-end solutions that adopt explainable AI paradigms with actual builds for addressing the challenges faced and comparing, contrasting, and evaluating the modeling decisions taken on insurance data for each problem discussed. It specifically emphasizes defining bulk Data Engineering Libraries, explainable Multi-Task Learning, reduced Labeled Data modeling paradigms as well as ensemble, calibration, online parameter tuning, and Continuous Integration – Continuous Deployment (CI/CD) training and prediction solutions for specific model problems like class imbalance address, bulk predictions, and Model Performance Monitoring. We primarily focus on Fraud Detection Domains, Auto Claims Settlements, Sentiment Analysis over advice, recovery, and remembrance from memories, cross-domain use cases of Fraud Detection in Healthcare and Mutual Funds, Email Classification, and Treasury functionalities to provide state-of-the-art models that are deployable, low maintenance, and focus more on solving enterprise problems than solutions.
