Priya Patel
Machine learning algorithms are becoming increasingly important in the insurance industry in predicting claims and determining premiums. However, these algorithms often operate in secrecy, making it difficult for customers to understand the reasons behind their decisions. Machine learning (ML) explainability has emerged as a key concept in shedding light on these algorithms, enabling users to understand the rationale behind decisions. In insurance, this transparency is important for building trust and fairness, ensuring that customers understand the factors that determine their premiums. ML explainability also allows for human oversight, ensuring that ML complements human judgment and aligns with ethical and legal standards. Various approaches, such as LIME, SHAP, Feature Importance, Partial Dependence Plots (PDP), Counterfactual Explanations, and Global Surrogate Models, have been developed to provide understandable explanations for machine-driven decisions in insurance. Companies like Earnix are making ML explainability more accessible, providing features within their solutions that go beyond standard approaches. The future of ML in insurance will be defined by the clarity of explanations, with companies that demystify their algorithms leading the way. In essence, ML explainability is about ensuring that insurance prioritizes the human experience alongside technological advancement.
