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📊 '''Logistic regression''' is a statistical modeling technique widely used across the insurance industry to predict the probability of a binary outcome — such as whether a [[Definition:Claims | claim]] will be filed, whether a policyholder will lapse, or whether a submitted claim is [[Definition:Fraud | fraudulent]]. Unlike [[Definition:Linear regression | linear regression]], which estimates a continuous value, logistic regression maps its output through a sigmoid function to produce a probability bounded between zero and one, making it naturally suited to the yes-or-no classification problems that permeate insurance operations from [[Definition:Underwriting | underwriting]] to [[Definition:Claims management | claims management]].

⚙️ The model works by estimating the log-odds of the target event as a linear combination of predictor variables — age, claim history, coverage type, geographic zone, credit-based insurance score where permitted, and so on — then transforming those log-odds into a probability. Each coefficient in the model quantifies how a one-unit change in a predictor shifts the odds of the outcome, an interpretability advantage that has made logistic regression a mainstay of [[Definition:Actuarial science | actuarial]] practice and regulatory submissions. In [[Definition:Motor insurance | motor]] and [[Definition:Property insurance | property]] pricing, logistic regression often models claim frequency as a binary event at the policy level, complementing [[Definition:Generalized linear model (GLM) | GLM]]-based severity models. In life and health insurance, it underpins [[Definition:Medical underwriting | medical underwriting]] decision support, predicting the likelihood of adverse outcomes from applicant health data. Across different regulatory environments — U.S. state departments of insurance, the UK's [[Definition:Financial Conduct Authority (FCA) | FCA]], and supervisors in markets like Hong Kong and Australia — the transparency of logistic regression coefficients makes the model easier to defend in [[Definition:Rate filing | rate filings]] and fair-lending or anti-discrimination reviews than opaque [[Definition:Machine learning | machine learning]] alternatives.

🛡️ Despite the rise of more complex algorithms, logistic regression retains a central role for several practical reasons. Its outputs are directly interpretable as risk probabilities, which [[Definition:Underwriting | underwriters]] and [[Definition:Claims | claims]] professionals can act on without requiring a data science intermediary. It trains quickly on large insurance datasets, converges reliably, and is well understood by auditors and regulators. Many [[Definition:Insurtech | insurtech]] platforms use logistic regression as a baseline model against which more sophisticated approaches — random forests, gradient boosting, neural networks — are benchmarked. It also serves as the backbone of [[Definition:Inverse probability weighting | inverse probability weighting]] and [[Definition:Propensity score | propensity score]] estimation in causal analyses within the industry. For teams balancing predictive power with explainability and compliance requirements, logistic regression remains an indispensable tool rather than a legacy artifact.

'''Related concepts:'''
{{Div col|colwidth=20em}}
* [[Definition:Generalized linear model (GLM)]]
* [[Definition:Predictive analytics]]
* [[Definition:Inverse probability weighting]]
* [[Definition:Machine learning]]
* [[Definition:Risk classification]]
* [[Definition:Propensity score]]
{{Div col end}}

Definition:Logistic regression - Revision history

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