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📉 '''Interrupted time series analysis (ITSA)''' is a quasi-experimental statistical method that evaluates the impact of an intervention or event by comparing the trend of an outcome variable before and after a clearly defined interruption point. In insurance, ITSA is particularly well suited for assessing the effects of regulatory changes, new [[Definition:Underwriting | underwriting]] guidelines, [[Definition:Claims management | claims process]] reforms, or market-wide events on metrics like [[Definition:Loss ratio | loss ratios]], [[Definition:Claim | claims]] frequency, [[Definition:Premium | premium]] adequacy, or [[Definition:Lapse rate | lapse rates]]. Because many insurance interventions are implemented at a known point in time across an entire portfolio or jurisdiction — rather than being randomly assigned to individual policyholders — ITSA provides a natural and powerful analytical framework.

📊 The technique works by modeling the pre-intervention trend using a series of regularly spaced observations — monthly [[Definition:Claim | claims]] counts, quarterly [[Definition:Incurred loss | incurred losses]], or annual [[Definition:Combined ratio | combined ratios]] — and then testing whether the intervention produced an immediate level change, a change in the ongoing trend, or both. For example, when a European [[Definition:Regulator | regulator]] mandated gender-neutral [[Definition:Pricing | pricing]] under an EU directive, ITSA enabled [[Definition:Actuary | actuaries]] to isolate the policy's impact on [[Definition:Motor insurance | motor insurance]] loss patterns by comparing the pre-mandate trajectory with the post-mandate reality. Similarly, a [[Definition:Health insurance | health insurer]] in the United States could use ITSA to evaluate how the introduction of [[Definition:Prior authorization | prior authorization]] requirements affected specialty drug expenditures. Adding a control series — a comparable portfolio or jurisdiction unaffected by the intervention — strengthens [[Definition:Internal validity | internal validity]] by accounting for concurrent external trends, a design known as controlled interrupted time series.

🛠️ One of ITSA's greatest strengths is its transparency: the visual depiction of pre- and post-intervention trends makes the analysis accessible to non-technical stakeholders, from board members to [[Definition:Regulator | regulators]], without sacrificing statistical rigor. This makes it a valuable communication tool when insurers need to demonstrate — to supervisory authorities under [[Definition:Solvency II | Solvency II]], [[Definition:Risk-based capital (RBC) | RBC]], or [[Definition:C-ROSS | C-ROSS]] regimes — that a change in operations or market conditions has materially altered their risk profile. [[Definition:Reinsurer | Reinsurers]] examining cedant portfolios can also employ ITSA to evaluate whether shifts in [[Definition:Experience rating | experience]] are attributable to specific underwriting actions or simply reflect broader market cycles. The method does require a sufficient number of pre-intervention observations and careful attention to [[Definition:Seasonality | seasonality]] and autocorrelation, but when these conditions are met, ITSA delivers some of the most credible causal evidence available from observational insurance data.

'''Related concepts:'''
{{Div col|colwidth=20em}}
* [[Definition:Causal inference]]
* [[Definition:Internal validity]]
* [[Definition:Loss ratio]]
* [[Definition:Predictive model]]
* [[Definition:Experience rating]]
* [[Definition:Regulatory change]]
{{Div col end}}

Definition:Interrupted time series analysis (ITSA) - Revision history

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