The key areas where AI is transforming, optimizing, and elevating marketing in the insurance industry.
As we’ve mentioned in previous articles, insurance is on the verge of a massive AI-driven shift. Despite the challenges stemming from the industry being heavily regulated, insurance carriers are actively experimenting with predictive models in many areas, from claim processing to underwriting, fraud detection, pricing, and, increasingly, customer service and marketing.
By using advanced analytics and Machine Learning (ML) capabilities, insurance companies can extract valuable insights from large datasets that can help separate their clientele into more granular and distinctive groups based upon behavior, needs, and preferences. And, this has huge implications for their marketing outcomes.
Having worked with both mid-sized and large insurance companies, we’ve thoroughly studied the industry’s overall marketing needs and pain points. Hence, based on the use case analysis, we’ve deduced that it makes the most sense to first focus on applying AI in these four areas:
- Creating a 360-degree customer view
- Personalized offerings
- Improving CX (Customer Experience)
- NPS (Net Promoter Score) and feedback analysis
Figure 1. Four insurance areas where AI can have a massive impact.
Why is it necessary for insurers to make use of AI?
Creating a 360-degree customer view
AI algorithms help organizations aggregate and integrate data from various sources so as to create a complete and accurate view of each customer. These sources typically include claims management systems, policy administration systems, CRMs (Customer Relationship Management), social media, etc. Within architectural considerations, Graph Neural Networks (GNNs) often emerge as an optimal choice for capturing intricate relationships within data sources.
GNNs create graphs in which data sources are the nodes and the edges are the relationships between them. For example, they can visualize a link between a CRM and a policy administration system to see if they share some customer data.
After the graph has been created, a message-passing algorithm is applied to move information between nodes, and after each iteration, the nodes update their state based on the messages from neighboring nodes. Post-graph creation, a message-passing algorithm is applied to transfer information between nodes. Iteratively, the vertices update themselves based on messages from adjacent nodes. After repeated interactions, the GNN converges, resulting in each node representing a single view of the customer. Once the system is ready, various marketing tasks could be performed with it, such as segmentation, personalized recommendations, etc.
Data cleaning and enrichment
Within this element, Named Entity Recognition, Part of Speech, Conference resolution, and Text models can be applied to both identify and correct errors, misspellings, and different inconsistencies within client data, as well as to extract information from unstructured sources such as reviews and social media posts in order to enrich existing datasets. Then, classification, regression, clustering, and anomaly detection algorithms can be used to detect incorrect policy numbers and claim information, and predict various customer risk factors.
Finally, GNN architectures can clean data by exploiting the relationships between clients (i.e., to identify and eliminate duplicate information), as well as propagate data across the customer graph, such as demographics. This means, for instance, that if the GNN sees a client’s age, it could determine the probable age of their family members, which could then be used to create more personalized campaigns.
Segmentation and profiling
GNNs can orchestrate client segmentation based on social circles, claims history, and policy purchase patterns. This analytical framework provides insights into purchasing decisions within groups of friends and families. But, they’re just one option.
A range of traditional ML models, including K-means, SVMs (Support Vector Machine), Decision Trees, Division, and Random Forests, can also be leveraged to find patterns in a dataset based on various risk factors such as age, driving history, and health history. These models can be important tools not only for creating targeted marketing campaigns, but also for generating personalized risk mitigation strategies for each client.
Yet, another approach is training a Reinforcement Learning (RL) agent to learn which clients are the most important to the company. This happens when an agent interacts with customer data and gets rewarded each time they identify valuable clients. Once trained, it can segment incoming data into appropriate sections based upon their lifetime value. With these insights, the carrier can focus more on directing their efforts toward valuable clients and creating impactful marketing campaigns.
Predictive analytics
AI-powered predictive models enable the anticipation of client behavior, assessing the probabilities of claims filing or churn. These insights are helpful in terms of developing proactive marketing campaigns and client retention strategies.
A variety of networks can be utilized to predict customer behavior and lifetime value, and then prioritize marketing campaigns correctly.
Traditional models:
- Logistic regression. Effective for binary classification tasks, such as predicting whether a customer will churn or not.
- Decision trees. Lend themselves both for classification and regression tasks, and are particularly well-suited for predictive analytics due to being easy to interpret and capable of learning complex patterns in data.
- Random forests. Average the predictions of the decision trees and, therefore, tend to be more accurate and less prone to overfitting than individual trees.
- Gradient boosting machines (GBMs). GBMs, which are ensembles of simple algorithms, often tend to be more accurate than random forests. Also, they can be used for a broader range of tasks, such as classification, regression, and ranking.
- Support vector machines (SVMs). SVMs are an optimal option when dealing with high-dimensional data, but they can also be used for classification and regression tasks.
Deep learning models:
- Simple neural networks (NNs). NNs are well-suited for predictive analytics and can learn complex patterns in data while having a simple architecture.
- Convolutional neural networks (CNNs). CNNs are used mostly for image and video classification, but can also be applied to Natural Language Processing (NLP) tasks such as text classification.
- Recurrent neural networks (RNNs). RNNs process sequential data, such as text and time series data.
- Long short-term memory (LSTM) networks. LSTM networks are a type of RNN that is optimized for learning long-range dependencies in sequential data.
- Graph Neural Networks (GNNs). GNNs, as we’ve mentioned, perform well in terms of modeling dependencies between data objects, such as clients. This also makes them very well-suited for predictive analytics as they can efficiently estimate probable client behavior.
Overall, our clients report the following benefits from using AI for a 360-degree view creation:
- Data consolidation
- Enhanced operational efficiency
- Improved customer satisfaction scores
- Increased Gross Written Premiums (GWP)
- Lower reporting costs
The business advantages of having an AI-enabled 360-degree customer view
Figure 2. 360-degree customer view.
Enhanced customer understanding
AI makes it easy to collect and analyze large amounts of information from various and diverse sources, enabling organizations to gain a deeper understanding of customer behaviors and preferences, along with better risk profiles. For instance, by analyzing customer interactions, the models can output patterns and trends of clients’ needs, expectations, and pain points.