How AI Insurance Recommendation Apps Personalizes Customer Experiences

Artificial Intelligence (AI) technology is extending its automation capabilities to businesses across various industries. Among other industries, the role of AI in insurance and banking is very impactful. AI stands at the frontline for its design and development of innovative apps that handle fraud detection, claim processing, underwriting, risk prediction, and more. On top, insurance companies are increasingly investing in AI apps to deliver personalized insurance recommendations and improve their customer experiences.

V-Soft Consulting helped a leading insurance company increase their customer retention rate by 65% as well as customer experience and satisfaction level by 45%. In this article, we will share our real-time experience in the design, development, and deployment of an AI-powered insurance recommendation app and the steps involved in this agile software development approach.

First, let’s look at how AI helps insurance service providers deliver personalized policy recommendations for their clients.

The Role of AI in Insurance for Personalization is Futuristic

AI, Machine Learning (ML), data analytics, and predictive analytics techniques empowers insurance recommendation apps to analyze and process the client’s historical policy data efficiently. Based on the insights derived from the app, the insurance service providers can advise personalized policy recommendations that align to the unique needs of customers.

AI in Insurance: A Step to Personalize Product Recommendations

Due to lack of centralized visibility in the customer management process, a leading insurance services company has experienced difficulties in processing customers’ data extensively. This led to inefficiencies in analyzing insights into customer needs and providing them with personalized policy recommendations. They faced challenges in creating potential sales opportunities and improving customer satisfaction and retention rates.

On top of that, the client’s existing recommendation systems were not tailored to individual customer preferences, which resulted in low conversion rates.

After understanding the client’s pain points in delivering personalized insurance policy recommendations to their customers, V-Soft decided to build an AI-driven insurance recommendation app.

The project’s goal was clear. Using ML, Generative AI, and predictive analytics capabilities, we developed an AI app that predicts potential insurance opportunities. From data collection to the development of a user-friendly interface using Streamlit, the project involved several phases. Look at the key phases involved in the development of this Generative AI solution.

Step-by-Step Process of AI Insurance Recommendation App Development

Data Collection

It involved collecting customer data, such as profiles and their previous policy details, to maintain structured logs into a database. We leveraged the Faker Library for generating synthetic data for testing and data analysis processes to ensure model efficiency.

Generally, synthetic data includes personal details (age, gender, marital status), previous insurance policy details (claim history, premium details, outstanding balance, etc.), and users' social behavior (time spent on insurance web portals and policy preferences). Later, then the synthetic dataset was accurately organized and stored in a CSV file to make it ready for processing.

Data Cleaning and Preprocessing

Data cleaning and preprocessing techniques play a crucial role in maximizing the quality of the data that is collected. In this phase, unnecessary or irrelevant information is removed to boost the accuracy of the database for model training. Look at the three core steps involved in this phase:

• Date Conversion: It converts the renewal date from text format to a number format that represents the current date.
• Categorical Encoding: It uses OneHotEncoder for multi-class features, such as marital status and claim type, and LabelEncoder for the target variable to convert categorical variables and features into binary format, making the data suitable for ML models and improving its accuracy.
• Feature Selection: It identifies and retains the most relevant data while removing irrelevant input data.

Model Selection

By leveraging ML techniques, the intelligent Generative AI solution can precisely extract significant features, such as customer visits to insurance websites and their preferences, deriving valuable insights into customers’ preferences and sales opportunities.

Our AI and Generative AI app development team implemented the RandomForestClassifier due to its robustness and versatility. During the model training, this method combines multiple decision trees and predicts the mean value of training data by comparing the root node of each decision tree with the attribute added. The following are key advantages of the RandomForestClassifier method.

• Complex Data Management: Manages both numerical and categorical data efficiently.
• Feature Importance: Provides insights into the significance of features in making predictions.
• Overfitting Prevention: Blocks the risk of overfitting by augmenting training data and eliminating the irrelevant features for model training.

Model Training and Validation

Insurance companies can quickly identify sales opportunities, advise product recommendations, and target potential customers. It involves the following steps.

• Feature and Variable Definition: Defines the features (X) and the target variable (y).
• Data Segmentation: Segments the source data into training and testing sets with an 80/20 ratio and accurately evaluates the model’s performance
• Model Training: Trains data in the RandomForestClassifier method to resolve regression issues
• Model Validation: Evaluates the performance of the model using a cross-validation approach across different subsets of the data for achieving data normalization and robustness

Model Evaluation

By evaluating the accuracy score, precision, and confusion matrix, we were able to provide additional insights into the performance of the ML model for analyzing the accuracy of the outcome it provides. However, the RandomForestClassifier has achieved maximum accuracy in generating reliable insurance policy recommendations.

• Accuracy Score: To measure the volume of accurately predicted instances out of the total instances.
• Confusion Matrix: Classifies true positives, true negatives, false positives, and false negatives, thereby helping to understand the model's performance across different classes.
• Precision, Recall, and F1 Score: Provides additional insights into the ML model's performance, especially in cases of imbalanced classes.

Model Deployment

We integrated the predictive model functionality to help the client determine the preferences and needs of existing or new policyholders while also providing personalized insurance recommendations.

Leveraging Joblib, the generative AI-powered insurance product recommendation app, helped our client save the status of the trained model and resume the progress on any supportive device. It provided quick loading and accurate predictions without retraining the model.

User Interface (UI)

Whether a mobile app or a web-based solution, an interactive and simple-to-use UI helps businesses improve user engagement while increasing satisfaction rates. We used Streamlit, an open-source framework, to build an intuitive user interface for our AI-based insurance recommendation app. We ensured improved accessibility for customers as well as insurance providers.

We also integrated personal and behavioral data analytics for even better predictions, optimization techniques for improved performance, and continuous model refinement by adding a user feedback loop.

Conclusion

We hope that you enjoyed reading our success story and the process we followed. This insurance recommendation app is an innovation for how AI, ML, and Generative AI, like revolutionary technologies, would help the insurance industry generate sales opportunities. Use of AI in insurance ensures rapid growth towards personalized insurance services and improved customer experiences. It offers a glimpse into a more streamlined and effective future for the insurance industry.

V-Soft Consulting has years of proven expertise in delivering reliable and result-driven AI services and solutions. Are you an insurance service provider looking to enhanced customer experience and satisfaction with our AI model?

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