Insurance
- Improved performance of risk, pricing, and fraud detection models
- Protected sensitive personal data according to privacy regulations
- Improved accuracy and fairness in AI models
Data innovation.
Insurers are among the data innovators. This is no surprise since risk calculation is the core of their business. Customer data holds great potential for other use cases in marketing, customer experience, and fraud detection. Flexible, fast-moving start-ups are radically changing the insurance market. They are using new AI-based services.
Insurers will have to redesign their data and analytics processes. The development of data-centric AI and analytics solutions is urgent. Traditional insurers striving for data-driven and data-based operations still face many obstacles.
Challenges.
Insurers cannot use or share data due to privacy restrictions. Flaws in demographic diversity or fringe cases can cause imbalances in analyses and predictions. Collecting accurate data on a large scale is also time-consuming and costly.
Policyholders’ sensitive and personal information is heavily regulated. Statutory and regulative pressure makes it extremely difficult to collaborate effectively with other legal entities and countries or outside vendors. However, the inability to accurately evaluate risks and insurance claims leads to incorrect pricing and marketing decisions.
... Insurers are among the data innovators
... Insurers cannot use or share data due to privacy restrictions
Use cases
Accurate risk assessments and market selection
Afgerond
Comprehensive and reliable customer profiles
Insurers need access to comprehensive and reliable data sets. Without it, it is impossible to assess risk, accurately model possible outcomes, and select the best markets. With synthetic data that mimics real customer profiles and behaviors, insurers can still have a comprehensive, reliable dataset.
Improved fraud detection
Solid and accurate fraud models
Fraudulent activity and data breaches are a major concern for insurers. The lack of reliable data makes identifying and mitigating these threats difficult. However, this data is needed to create fraud models. Synthetic data offer a solution for this. It provides access to solid, accurate data sets that are up-to-date, representative, and compliant with laws and regulations such as the GDPR.
Claims analysis and targeting existing customers
Understanding customer behavior and preferences
Insurers want to streamline claims management processes and identify fraudulent claims without bias. They also want to identify new growth areas and opportunities. Claims analysis, however, contains sensitive data. The limitations faced by data scientists make it very difficult to build models.
Synthetic data analysis supports insurers in identifying factors that affect customer retention and reducing customers who cancel policies. Insurers can thus gain insight into customer behavior and preferences. They can use it to improve both targeting and customer service.
Marketing Models
Cross-selling and up-selling through deep insight into customer behaviors
Insurance is one of the low-interest products. This makes it hard to cross- and up-sell. With an in-depth understanding of customer behavior, this is possible. However, this requires access to a lot of data at a detailed level. For example, demographic data, customer contact, claims, responses to marketing campaigns, etc. Due to privacy laws, the necessary personal data is not available. Using synthetic data, an insurer can still gather the insights needed for cross- and upsell campaigns. As customers receive targeted information, it increases the Net Promoter Score (NPS) of these campaigns.
Insights into customer experience
Optimal balance between operational efficiency and customer satisfaction
Insurers strive for a balance between operational efficiency and customer satisfaction. High customer satisfaction requires investments such as a large call center, training call center staff, self-service, etc. Moreover, insurers aim to offer different services based on potential customer value. To gain a good understanding of the relationship between customer interactions and customer satisfaction, personal information is required. This data is not available from a privacy perspective. It can be done with synthetic data, resulting in valuable information and insights.
Customer data from intermediaries
Insights into customers through intermediaries
Intermediaries protect their data diligently. They do not want to run the risk of the head office approaching customers directly. By sharing synthetic data, the head office still gets all the information needed to perform numerous analyses. It is impossible to trace synthetic data back to individual customers. Therefore, it does not pose any risk to intermediaries.
HR analyses
Employee analyses for recruitment and selection
Synthetic data is beneficial to HR analyses. For example, to cope with labor market scarcity. With this type of data, it is possible to conduct employee analyses based on age, gender, distance from work, education, etc.