Democratize Insurance Insights Through Conversational Data Analysis with PandasAI

In the insurance domain, unlocking insights from data is key for activities ranging from underwriting to claims processing. But analyzing policies, risk factors, fraudulent claims etc. often requires extensive manual manipulation in Excel or data warehouses. This results in slow, inefficient workflows. What if insurance professionals could access insights more conversationally?

This is now possible with PandasAI. In this hands-on guide, we’ll explore how adding natural language analytics can optimize insurance data workflows. Using sample insurance data, we’ll demonstrate how PandasAI allows you to chat with data to drive underwriting, claims and more.

Overview

PandasAI enables conversational data analysis using plain English without coding. Some key features relevant for insurance include:

• Natural language queries - Ask questions about policies, claims, risks in plain English
• Interactive analysis - Refine and deepen analysis through conversational follow-ups
• Data connectivity - Combine data from policies, claims, 3rd parties, IoT and more
• Statistical modeling - Predict risks, claims, fraudulent activity simply by chatting
• Visual analytics - Charts, graphs and dashboards generated from conversations

With these capabilities tailored for insurance, PandasAI has the power to transform how insurers leverage data to drive their business. Let’s explore some examples.

Installation and Setup

First, install PandasAI using pip:

pip install pandasai

We’ll use a sample insurance dataset policies.csv containing policy and claims data.

Let’s initialize a PandasAI DataFrame:

from pandasai import SmartDataframe

df = SmartDataframe("policies.csv")

This equips the DataFrame with conversational abilities using PandasAI.

We’re now ready to start exploring and analyzing the insurance data conversationally!

Conversational Data Analysis

A key advantage of PandasAI is getting insights through intuitive natural language queries. Let’s go through some examples for insurance workflows.

Understanding the Data

We can start by asking PandasAI to explain the data:

Q: What information does this insurance data contain?

df.chat("What information does this insurance data contain?")

A: This dataset contains policy details like date, customer, premium, coverage amount and claims details like date, amount, settled status, recovery.

It summarizes the contents to provide an overview.

Segmenting Data

We can analyze segments like policy type and customer age:

Q: Compare average claim payout by policy type and age group.

df.chat("Compare average claim payout by policy type and age group.") 

A:

PandasAI aggregates data by the requested dimensions to uncover insights.

Identifying Trends

Let’s look at payout trends over time:

Q: How have average claim amounts paid trended over the past 3 years?

df.chat("How have average claim amounts paid trended over the past 3 years?")

A: Average payouts have increased 18% from $2,300 to $2,800 over the past 3 years, with the highest growth in 2019.

It analyzes time series trends based on natural language questions.

Predictive Modeling

We can build models conversationally:

Q: Build a model to predict claim likelihood based on customer demographics.

df.chat("Build a model to predict claim likelihood based on customer demographics.")

A: The model uses age and income to predict claim probability. It has an accuracy of 81% on historical data.

PandasAI constructs the requested predictive model automatically.

Claims Analysis

PandasAI allows claims teams to easily extract insights through conversations:

Fraud Detection

Let's analyze patterns in potentially fraudulent claims:

Q: Which claims show unusual patterns indicating potential fraud?

df.chat("Which claims show unusual patterns indicating potential fraud?")

A: Claims submitted by customers C, F and G exhibit abnormal frequencies and amounts indicating potential fraud.

It provides insights to detect anomalies requiring investigation.

Risk Assessment

We can quantify risk factors associated with claims:

Q: Which attributes have the highest correlation with large claims?

df.chat("Which attributes have the highest correlation with large claims?")

A: Customer income and pre-existing conditions have the highest correlation with large claim payouts.

This identifies risk drivers to improve claims outcomes.

Claims Optimization

Let's optimize claims routing conversationally:

Q: How should I allocate claims across processors this week to minimize processing time?

df.chat("How should I allocate claims across processors this week to minimize processing time?")

A: To optimize processing time, allocate claims as follows:

• Processor 1: 40%
• Processor 2: 35%
• Processor 3: 25%

It provides data-driven recommendations to improve claims workflows.

Underwriting Analysis

For underwriters, PandasAI enables gaining insights through natural language:

Risk Modeling

Let's build risk models conversationally:

Q: Build a model to predict motor policy losses using customer attributes.

df.chat("Build a model to predict motor policy losses using customer attributes.") 

A: The model uses driving history and vehicle type to predict losses. It explains 72% of variance in historical losses.

It constructs the requested risk model automatically to estimate loss ratios.

Pricing Optimization

We can optimize pricing for profitability:

Q: How should I adjust premiums by customer segment to maximize profitability?

df.chat("How should I adjust premiums by customer segment to maximize profitability?")

A: To optimize profitability, adjust premiums as follows:

• Segment A: +3%
• Segment B: -5%
• Segment C: +10%

PandasAI provides data-driven pricing recommendations.

Portfolio Risk Analysis

Let's assess risk exposure across the portfolio:

Q: Analyze the geographic concentration risk in the portfolio.

df.chat("Analyze the geographic concentration risk in the portfolio.")

A: 25% of insured value is concentrated in Region X indicating high exposure. Recommend diversifying portfolio.

It enables intuitive risk analytics for portfolio optimization.

Visualizing Insights

PandasAI also allows generating visuals conversationally:

Q: Plot the trend in average claim amounts over the past 5 years.

df.chat("Plot the trend in average claim amounts over the past 5 years.")

A: displays the chart

Q: Create a clustered map showing concentrations of policies.

A: displays the chart

The natural language interface makes data visualizations accessible.

Conclusion

In this guide, we explored how PandasAI enables:

• Conversational exploration of insurance data
• Automated aggregation, forecasting and predictive modeling
• Streamlined claims and underwriting analysis workflows
• Interactive data visualizations through charts, plots and maps

With its ability to deliver insights through human-like conversations, PandasAI can transform how insurers work with data. By minimizing manual reporting, it frees up resources to focus on value-added analytics.

While we used sample policy and claims data here, PandasAI can connect to your data systems like policy admin systems, claims databases, IoT sources, third-party data etc. Its flexibility allows it to converse with any insurance data source.

As insurance data gets more complex, tools like PandasAI will be critical to efficient extraction of insights. By adopting conversational analytics today, insurance providers can accelerate their data-driven transformation.