The insurance industry is known to utilize advanced analytics for underwriting. However, the challenge has always been accessing credible data sources that churn out meaningful insights. While Account Aggregator has a great potential use case for pre-policy approval processes like income verification, it can transform post-policy operations and customer relationships to a great extent.  

Let’s explore some of the non-obvious but extremely high-value applications of Account Aggregator (AA) and Customer Data Platform (CDP) that reshape portfolio management and customer insight.   

Behavioral micropatterns   

Policy monitoring has traditionally been mostly about macro trends because of limited data sources. AA can uncover subtle behavioral micro-patterns to predict portfolio health. For example, a policyholder who frequently downloads policy documents could file claims in the next few months. Keeping a close eye on customer service interactions can highlight seemingly unrelated behavior that signals potential claims and even increases risk awareness.  

Evolved risk mapping   

Risk is considered a relatively static measure post-underwriting when it is often not. Insurance companies can create risk evolution maps by integrating various data streams like IoT devices, social media sentiment, and even local weather patterns, along with authentic bank statements. These maps can indicate how a policyholder’s risks evolve in response to the changing circumstances, making room for dynamic portfolio rebalancing.  

Network effect in portfolio monitoring   

One thing insurance companies frequently overlook is that policies are not isolated units. They are interconnected through geographic proximities, business relationships, and shared risk factors. With a sophisticated CDP, insurance companies can run advanced network analysis to discover how risk cascades through these connections.   

One possible use case for AA CDP in this case is for commercial insurance. When one business in a supply chain experiences a claim, CDP can map out the probability of related claims across connected businesses, enabling proactive risk management across an entire network of portfolios.  

Predictive customer service model   

While most insurers could assume CDP can only be used for predicting claims, it can also be used in predicting customer service needs. By analyzing patterns in policy usage, life events, and external factors, insurers can build predictive models for when and why customers might need support – before they reach out. This can help insurers move from reactive customer service to proactive customer service.   

Cultural dimension   

Here's something rarely discussed: CDPs can map cultural and community-specific risk patterns that traditional underwriting misses. Different communities have unique risk perceptions and behaviors that affect how they interact with insurance products. By understanding these cultural dimensions, insurers can better tailor their portfolio management strategies.  

Urban insurance customers, typically digitally savvy professionals, prefer high-value term/health policies more than 1 Cr, make decisions around career milestones, and heavily use self-service digital channels with claims patterns linked to lifestyle diseases. Rural customers, conversely, align insurance decisions with harvest cycles, prefer savings-linked products with vernacular simplicity, rely heavily on trusted local agents for service, and show claim patterns influenced by weather events and festivals. Their policy modifications peak during harvest seasons while urban one's spike during job switches and tax periods. 

Non-binary customer retention   

Traditional retention models focus on binary outcomes, such as whether the customer will renew the policy or not. Will the customer be able to pay their premiums regularly and on time? Advanced analytics from a CDP can track customer portfolio health across multiple dimensions. Retention isn't just about preventing churn; it’s about understanding the quality and depth of customer relationships. Some customers might renew policies but show patterns indicating future dissatisfaction, while others might seem at risk but actually be deepening their engagement in ways traditional metrics miss.  

Privacy-aware analytics   

One fascinating aspect of using AA CDP in insurance is the Privacy Paradox. While customers are becoming increasingly privacy-conscious, they often value personalized service that requires deep data analysis. The solution lies in privacy-aware analytics that CDP can offer. Insurers can derive insights from behavioral patterns without accessing sensitive personal information.  

Adopt narrative analytics in insurance with FinBox AA CDP   

With FinBox AA CDP , insurance isn't just about better prediction but deeper understanding. We blend quantitative analysis with qualitative insights, creating narrative-based analytics. The most successful insurers will be those who use AA CDP not just as a technical tool but to understand the human behind the policies.   

Want to know how? We are here to help. Reach out to us here to learn more.