Data engineering pipelines in insurance analytics and actuarial modeling

While actuaries apply mathematical and statistical approaches from risk theory to model and predict events in the insurance domain, data engineering is required to implement these models and predictions as pipelines that feed modern enterprise data strategies in insurance. In doing so, they produce services and products competent in supporting business and stakeholders’ decisions on company, accounts, entities, exposure, financial forecasting, policy, provision, pricing, product, quota, reporting, sheets, investments, claims handling, strategy, and loss, among others. Data engineering in insurance, then, is responsible for the advancement and implementation of models aimed at risk control as services and products that build upon reputation. Models and predictions in insurance are normally conceived and created upon historical data, or time series; the task of operationalizing these services and products is devised, thereby, on the creation and maintenance of data pipelines and systems capable of collecting, storing, processing, and updating the volumes of historical data that is needed to ensure reliable model and prediction precision. This task ensures that data works reliably on the scales that are demanded by insurance products – responsible for transferring risk from policyholder to insurer – and processes – guaranteeing that lapse of time between payment and transfer of compensation for an insured event is, on average, the minimum possible, given financial and investment market conditions (Cernaianu & Corbos, 2019; Esposito et al., 2021; Henckaerts et al., 2022).