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Publication
Personalização no setor de seguros: Segmentação de clientes, recomendação de produtos e previsão de retenção
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| 4.88 MB | Adobe PDF |
Advisor(s)
Abstract(s)
A crescente complexidade do setor segurador, aliada às exigências de clientes cada vez mais informados e diversificados, torna a personalização de produtos e a fidelização dois dos maiores desafios enfrentados pelas seguradoras. Este trabalho explora a aplicação de técnicas de Machine Learning para apoiar a tomada de decisão no setor, através da segmentação de clientes, da recomendação de produtos e da previsão da retenção.
A investigação iniciou-se com uma revisão sistemática da literatura, que permitiu mapear as abordagens mais relevantes e identificar lacunas na aplicação de métodos de personalização em seguros. Em seguida, foi desenvolvido um estudo experimental com base num conjunto de dados reais, abrangendo variáveis demográficas, contratuais e de sinistralidade. Foram aplicados algoritmos de clustering para segmentar clientes em grupos homogéneos, modelos supervisionados para identificar o produto mais provável para cada perfil e técnicas de previsão para estimar a probabilidade de retenção. A interpretabilidade dos modelos foi assegurada através do método SHAP, permitindo compreender o impacto relativo de cada variável nas previsões. Os resultados mostraram que a segmentação consegue identificar padrões claros entre perfis de clientes e os produtos contratados. A recomendação de produtos alcançou resultados promissores, destacando variáveis como idade, antiguidade e capitais segurados como determinantes para a escolha. Já a previsão da retenção revelou-se mais desafiante, com desempenhos modestos, mas evidenciou que fatores estruturais e contratuais têm maior influência na renovação de apólices do que a sinistralidade. Conclui-se que a integração de segmentação, recomendação e previsão constitui uma abordagem viável para apoiar a personalização no setor segurador, ainda que persistam desafios técnicos e de qualidade dos dados. Este estudo abre caminho para investigações futuras que explorem datasets mais ricos, modelos mais sofisticados e a validação em contextos reais de seguradoras.
The increasing complexity of the insurance sector, together with the demands of more informed and diversified customers, makes product personalization and customer retention two of the greatest challenges faced by insurers. This work explores the application of Machine Learning techniques to support decision-making in the sector through customer segmentation, product recommendation, and retention prediction. The research began with a systematic literature review that mapped the most relevant approaches and identified gaps in the application of personalization methods in insurance. An experimental study was then developed using a real-world dataset, covering demographic, contractual, and claims-related variables. Clustering algorithms were applied to segment customers into homogeneous groups, supervised models were used to identify the most suitable product for each profile, and predictive techniques were employed to estimate retention probability. Model interpretability was ensured through SHAP values, which provided insights into the relative importance of each variable in the predictions. The results showed that segmentation successfully identified clear patterns between customer profiles and contracted products. Product recommendation achieved promising results, highlighting variables such as age, seniority, and insured capital as decisive for product choice. Retention prediction proved more challenging, with modest performance, but it revealed that structural and contractual factors have greater influence on policy renewal than claims history. It is concluded that the integration of segmentation, recommendation, and prediction is a viable approach to support personalization in the insurance sector, although technical and data quality challenges remain. This study paves the way for future research that can leverage richer datasets, more sophisticated models, and validation in real-world insurance contexts.
The increasing complexity of the insurance sector, together with the demands of more informed and diversified customers, makes product personalization and customer retention two of the greatest challenges faced by insurers. This work explores the application of Machine Learning techniques to support decision-making in the sector through customer segmentation, product recommendation, and retention prediction. The research began with a systematic literature review that mapped the most relevant approaches and identified gaps in the application of personalization methods in insurance. An experimental study was then developed using a real-world dataset, covering demographic, contractual, and claims-related variables. Clustering algorithms were applied to segment customers into homogeneous groups, supervised models were used to identify the most suitable product for each profile, and predictive techniques were employed to estimate retention probability. Model interpretability was ensured through SHAP values, which provided insights into the relative importance of each variable in the predictions. The results showed that segmentation successfully identified clear patterns between customer profiles and contracted products. Product recommendation achieved promising results, highlighting variables such as age, seniority, and insured capital as decisive for product choice. Retention prediction proved more challenging, with modest performance, but it revealed that structural and contractual factors have greater influence on policy renewal than claims history. It is concluded that the integration of segmentation, recommendation, and prediction is a viable approach to support personalization in the insurance sector, although technical and data quality challenges remain. This study paves the way for future research that can leverage richer datasets, more sophisticated models, and validation in real-world insurance contexts.
Description
Keywords
Personalization Insurance Machine Learning Customer Segmentation Recommendation Retention Personalização Seguros Segmentação de clientes Recomendação Retenção