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🤖 '''Overfitting''' is a modeling error that occurs when a [[Definition:Predictive model | predictive model]] learns not only the genuine patterns in its training data but also the noise, anomalies, and idiosyncrasies specific to that dataset — resulting in a model that performs impressively on historical data but poorly on new, unseen observations. In the insurance industry, where [[Definition:Actuarial | actuarial]], [[Definition:Underwriting | underwriting]], and [[Definition:Claims management | claims]] functions increasingly rely on [[Definition:Machine learning | machine learning]] and advanced analytics, overfitting poses a significant threat to the reliability of [[Definition:Risk classification | risk classification]], [[Definition:Pricing model | pricing models]], [[Definition:Fraud detection | fraud detection]] algorithms, and [[Definition:Loss reserving | reserving]] projections. A model that appears to predict losses with extraordinary precision during back-testing may fail dramatically when deployed against real-world portfolios.

⚙️ Overfitting typically arises when a model is excessively complex relative to the volume and diversity of available data — for instance, when a [[Definition:Generalized linear model (GLM) | GLM]] or neural network includes too many parameters, or when tree-based models are allowed to grow without pruning constraints. In insurance, the problem is amplified by the inherent characteristics of loss data: [[Definition:Catastrophe | catastrophe]] events are rare, [[Definition:Long-tail | long-tail]] claims take years to develop fully, and the mix of exposures shifts over time as products and markets evolve. An overfit pricing model might assign artificially precise [[Definition:Insurance premium | premium]] differences to small segments that performed unusually well or badly in a single observation period, only to see those distinctions evaporate in subsequent years. Practitioners guard against overfitting through techniques including cross-validation, regularization (such as Lasso and Ridge penalties), out-of-sample testing, and careful feature selection. [[Definition:Actuarial standards of practice | Actuarial standards]] in many jurisdictions require practitioners to demonstrate that their models generalize beyond the fitting data.

📉 The consequences of deploying an overfit model in production can be severe and far-reaching. An overfit underwriting model may lead an insurer to underprice a segment it mistakenly believes to be low-risk, resulting in [[Definition:Adverse selection | adverse selection]] and deteriorating [[Definition:Loss ratio | loss ratios]]. An overfit fraud-detection engine may generate excessive false positives, delaying legitimate [[Definition:Claim | claims]] and damaging [[Definition:Policyholder | policyholder]] relationships, or worse, miss genuine fraud patterns that were not present in the training set. Regulators and [[Definition:Rating agency | rating agencies]] have grown attentive to model governance: [[Definition:Solvency II | Solvency II's]] internal model approval process in Europe, the [[Definition:National Association of Insurance Commissioners (NAIC) | NAIC's]] principles-based reserving framework in the United States, and supervisory guidance from authorities in Singapore and Hong Kong all expect insurers to validate model robustness. Within the [[Definition:Insurtech | insurtech]] ecosystem, where firms compete on analytical sophistication, demonstrating that models are well-calibrated rather than overfit has become a mark of credibility with both [[Definition:Insurance carrier | carrier]] partners and investors.

'''Related concepts:'''
{{Div col|colwidth=20em}}
* [[Definition:Predictive model]]
* [[Definition:Machine learning]]
* [[Definition:Generalized linear model (GLM)]]
* [[Definition:Risk classification]]
* [[Definition:Model validation]]
* [[Definition:Adverse selection]]
{{Div col end}}

Definition:Overfitting - Revision history

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