# Blockchain-Driven Generalization of Policy Management for Multiproduct Insurance Companies
## 3 Problem Statement
### The Insurance Market: Policy Management
These systems enable the configuration and management of various insurance products from a centralized platform. Among their key components are:  
* Creation of templates for each type of insurance (auto, life, health, home), including their own coverages, business conditions, and economic data. * Customized parameterization for each policy type, such as coverage limits, deductibles, and premiums. * Rule engine specific to each type of insurance, enabling tailored evaluation processes. * Centralized policy management using specific modules for each type of insurance. Here is an example to understand how these systems work. If an insurance company offers auto, life, and home insurance, they would use a PAS as follows:  
* Auto Insurance: A template with coverages such as collision, liability, and roadside assistance. Life Insurance: A template with options for term life, whole life, and universal life insurance. * Home Insurance: A template with coverages for fire, theft, and natural disasters. The complexity of these systems lies in data management, which requires efficient and well-designed modeling. Typically, a relational database model is used, with tables supporting each of the different types of insurance. Additionally, when dealing with multiple products simultaneously, all systems that are part of this type of architecture must be equipped to support this multiproduct model. Some of the main problems that may arise are:
* Duplication of Processes: In the management of insurance business operations, it is common to implement similar processes across multiple systems. This can lead to operational overload, increased implementation and maintenance costs, and reduced efficiency due to redundancy and the necessity for synchronization between systems. * Duplication of Data: When both the insurance company and other involved parties store information about customers, policies, or claims, duplications can occur across different systems. * Inconsistencies: If customer or policy data are updated in one system but not in another, inconsistencies can arise. These can also result from errors during data integration or transmission. For example, different versions of a policy might exist in various systems due to an update error. * Discrepancies in Risk Assessment: When the insurer and other entities operate independently, they may use different criteria or models for risk assessment. These differences are reflected in their technological architectures, leading to inconsistencies even when agreements are made to conduct operations or transfers. Examples include discrepancies in premium assignments, policy approvals, and claims resolution. * Inconsistent Appraisal Reports: Appraisal results can vary if the methodologies or data used by external entities differ from those of the insurer. * Fraud Risk: This is one of the primary challenges faced by insurance companies. The risk of fraud can arise in various ways due to the involvement of multiple actors and integration points. Some of the most common examples include:  
These examples highlight the challenges of a centralized data model that requires numerous interactions between different entities. This is why our initial approach involves the standardization of the policy model, making it possible to manage multiple products under a unified data model.