Automated Machine Learning in Insurance (2408.14331v1)

Abstract: Machine Learning (ML) has gained popularity in actuarial research and insurance industrial applications. However, the performance of most ML tasks heavily depends on data preprocessing, model selection, and hyperparameter optimization, which are considered to be intensive in terms of domain knowledge, experience, and manual labor. Automated Machine Learning (AutoML) aims to automatically complete the full life-cycle of ML tasks and provides state-of-the-art ML models without human intervention or supervision. This paper introduces an AutoML workflow that allows users without domain knowledge or prior experience to achieve robust and effortless ML deployment by writing only a few lines of code. This proposed AutoML is specifically tailored for the insurance application, with features like the balancing step in data preprocessing, ensemble pipelines, and customized loss functions. These features are designed to address the unique challenges of the insurance domain, including the imbalanced nature of common insurance datasets. The full code and documentation are available on the GitHub repository. (https://github.com/PanyiDong/InsurAutoML)

• The paper introduces an AutoML framework that automates key processes like data encoding, imputation, balancing, and feature selection for insurance applications.
• It employs a combined search space and distributed computing via Ray Tune to optimize model selection and hyperparameter tuning, outperforming traditional methods.
• The research demonstrates how ensemble techniques and tailored loss functions effectively address imbalanced datasets, streamlining actuarial analysis and risk management.

Overview of "Automated Machine Learning in Insurance"

This paper presents a comprehensive exploration of employing Automated Machine Learning (AutoML) techniques specifically tailored to the insurance industry, aiming to enhance the deployment and performance of ML models in actuarial science and insurance applications. The authors, Panyi Dong and Zhiyu Quan, recognize the challenge that traditional machine learning poses due to its dependence on domain expertise for data preparation, model selection, and hyperparameter tuning. AutoML aims to automate these processes, thereby lowering the barrier for non-experts to harness advanced ML models.

Core Proposition and Methodology

The research introduces an AutoML framework designed to address unique challenges in the insurance domain, particularly the issue of imbalanced datasets and the specific requirements such as loss functions tailored for insurance applications. The paper outlines the proposed AutoML pipeline, which incorporates several key preprocessing steps: data encoding, imputation, balancing techniques to adjust imbalances, scaling, and feature selection—all essential for the quality of the final model. The pipeline fully automates the model training process by simultaneously exploring a multitude of models and hyperparameters using an extended search space.

The authors utilize a conjunctive framework of CASH (Combined Algorithm Selection and Hyperparameter Optimization) to optimize the AutoML process. They leverage Ray Tune’s distributed computing framework to facilitate this optimization, thereby ensuring scalability and efficiency in model training. Various search algorithms, like HyperOpt and Optuna, are integrated to explore possible model and hyperparameter combinations. Moreover, the framework supports ensemble methods such as stacking, bagging, and boosting to further bolster model performance and mitigate data imbalance issues.

Experimental Evaluation

The efficacy of this AutoML solution is validated using several datasets, including the French Motor Third-Part Liability dataset, the Wisconsin Local Government Property Insurance Fund dataset, and the Australian Automobile Insurance dataset. These experiments demonstrate that the proposed AutoML framework is capable of producing highly accurate models, often outperforming traditional methods like Generalized Linear Models (GLM), thus exemplifying its potential as a benchmark tool for insurance-related ML tasks.

In these experiments, the AutoML approach revealed significant reductions in mean Poisson deviance and improvement in AUC scores compared to existing models. Notably, the introduction of ensemble methods within the AutoML pipeline contributed to the enhanced handling of imbalanced datasets, a common challenge in insurance data.

Implications and Future Directions

The research underscores the transformative capability of AutoML in democratizing access to sophisticated ML tools, making it feasible for practitioners in the insurance industry to deploy robust models without deep technical expertise. This not only streamlines operational workflows but also encourages the broader adoption of data-driven decision-making processes in the sector.

Theoretically, the application of AutoML in insurance sets a precedent for other domains with similar data challenges. Practically, insurers can leverage this tool to refine underwriting processes, optimize risk management, and enhance customer satisfaction through more personalized experiences.

Looking forward, the incorporation of more domain-specific loss functions and the expansion of the pipeline to include unsupervised learning could further enhance the applicability of AutoML solutions. As the tool is open-sourced, continuous updates and community contributions could drive further innovation and adaptation to emergent insurance challenges.

In conclusion, the AutoML framework as detailed in this paper provides a significant step toward efficient, user-friendly, and effective machine learning applications in the insurance domain, paving the way for more data-centric strategies within the industry.