Pandasai
PandasAI is an open source AI agent transforming data analysis for enterprise and insurance professionals. It provides natural language queries, interactive analysis, data connectivity, and predictive modeling, empowering users to make data-informed decisions.
What is Pandasai?
PandasAI is a cutting-edge open source AI agent designed to revolutionize the way data analysis is conducted. With PandasAI, users can easily ask natural language questions to their enterprise data and receive real-time insights. Trusted by professionals at top companies, PandasAI offers enhanced analytics, actionable insights, detailed reports, and visual data representation all in one place. Integrated with various data sources like SQL, NoSQL, CSV, and xls, PandasAI democratizes data analysis for better data-informed decision making.
When it comes to insurance data analysis, PandasAI shines as a powerful tool that can help insurance professionals unlock valuable insights. By leveraging natural language queries, interactive analysis, data connectivity, statistical modeling, visual analytics, and more, PandasAI enables insurers to optimize their workflows and drive underwriting, claims processing, and other key activities. With features like automated data structuring, data augmentation, website extraction, and predictive modeling, PandasAI empowers insurance professionals to make data-driven decisions.
By using PandasAI, insurance professionals can streamline their claims analysis, fraud detection, risk assessment, claims optimization, underwriting analysis, risk modeling, pricing optimization, and portfolio risk analysis. This open source AI agent allows insurers to gain insights through human-like conversations, automate aggregation and forecasting, and generate interactive data visualizations. PandasAI is the go-to tool for insurance providers looking to accelerate their data-driven transformation and extract valuable insights from complex insurance data.
Pandasai Features
Conversational Data Analysis
- PandasAI enables users to ask questions about their data in plain English, such as comparing average claim payouts by policy type and age group, analyzing trends in claim amounts over time, building predictive models, and extracting insights for underwriting and claims processing.
- The platform aggregates data from multiple sources, including policies, claims, 3rd parties, and IoT devices, to provide comprehensive analytics in one place. It also offers statistical modeling capabilities to predict risks, claims, and fraudulent activity through conversational interactions.
- Users can visualize their data through intuitive charts and dashboards generated from conversations, making it easier to interpret and communicate insights. PandasAI also supports natural language queries to explain the contents of the data, segment data based on user-defined dimensions, and identify trends or correlations within the dataset.
Enhanced Analytics
- PandasAI is integrated with different data sources, including SQL, NoSQL, CSV, and xls, making it easy for users to connect and manage their data within the platform. The tool also offers detailed reports generation to keep every team aligned and informed about the insights derived from the data.
- Users can benefit from actionable insights generated by PandasAI, which transforms raw data into actionable strategies to implement data-driven approaches in their business. The platform supports detailed analytics and data visualization, allowing users to create intuitive charts and representations to interpret their business data effectively.
Automated Data Structuring
- PandasAI automates the process of data structuring by extracting structured data from diverse sources, enabling users to enrich their datasets seamlessly. The platform also supports data augmentation, allowing users to forecast trends and gain actionable business insights by leveraging AI capabilities for data analysis.
- Additional functionalities of Automated Data Structuring include Website Extraction, which enables users to scrape websites and extract semi-structured data at scale. This feature broadens the scope of data sources that can be ingested and analyzed within PandasAI, enhancing the platform's capabilities for comprehensive data analysis.
How to Use Pandasai?
Step 1: Installation and Setup- Open your terminal or command prompt.
- Run the command: `pip install pandasai` to install the PandasAI library.
- Download the sample insurance dataset `policies.csv` to your local machine.
- Initialize a PandasAI DataFrame with the following Python code: `from pandasai import SmartDataframe` and `df = SmartDataframe('policies.csv')`.
- Open your Python environment (e.g., Jupyter Notebook, VS Code).
- After initializing the PandasAI DataFrame, use the chat feature by typing: `df.chat('What information does this insurance data contain?')`.
- Review the summary provided by PandasAI which includes details like policy date, customer info, premium, coverage amount, and claims details.
- In your Python environment, type the following query to analyze data segments: `df.chat('Compare average claim payout by policy type and age group.')`.
- PandasAI will provide the average claim payout categorized by policy type and age group.
- Review the generated insights to understand payout differences across various segments.
- To analyze payout trends, enter: `df.chat('How have average claim amounts paid trended over the past 3 years?')`.
- PandasAI will return trend data for the specified period, highlighting changes in average payouts.
- Use this information to understand historical trends and project future payouts.
- For predictive analysis, type: `df.chat('Build a model to predict claim likelihood based on customer demographics.')`.
- PandasAI will build and return a model that uses demographics data to predict the likelihood of claims.
- Review the model's accuracy and use it to make informed decisions on customer risk.
- To detect fraud, ask PandasAI: `df.chat('Which claims show unusual patterns indicating potential fraud?')`.
- Review the claims flagged by PandasAI for abnormal patterns.
- Use this analysis to investigate and mitigate potential fraudulent activities.
- Ask PandasAI to correlate attributes with large claims by typing: `df.chat('Which attributes have the highest correlation with large claims?')`.
- Review the attributes identified by PandasAI as having the highest correlation.
- Use these insights to enhance risk assessment frameworks and improve claim outcomes.
- Optimize claims processing by querying: `df.chat('How should I allocate claims across processors this week to minimize processing time?')`.
- PandasAI will provide an optimal allocation strategy for claims processors.
- Implement the recommended allocation to enhance processing efficiency.
- For underwriting analysis, use: `df.chat('Build a model to predict motor policy losses using customer attributes.')`.
- PandasAI will generate a loss prediction model based on customer attributes.
- Leverage this model to assess risk and determine appropriate policy pricing.
- Optimize premiums by typing: `df.chat('How should I adjust premiums by customer segment to maximize profitability?')`.
- Review PandasAI's premium adjustment recommendations for different customer segments.
- Implement these adjustments to enhance profitability.
- Analyze portfolio risk by querying: `df.chat('Analyze the geographic concentration risk in the portfolio.')`.
- Review the geographic risk insights provided by PandasAI, such as areas with high insured value concentration.
- Use these insights to balance and diversify your portfolio.
- Create data visualizations using natural language queries.
- For trends, type: `df.chat('Plot the trend in average claim amounts over the past 5 years.')`. PandasAI will display the requested chart.
- To visualize policy concentrations, use: `df.chat('Create a clustered map showing concentrations of policies.')`. PandasAI will generate the map.
Pandasai Pricing
-
Personal
everything you need to start your journey with conversational data analysis
free
Get Started25 queries/month
3 datasets connected
Debugging traces(history up to 1 day)
Discord community support
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Pro
everything in personal with higher rates and more datasets
€49/month per user
Get Started1000 queries/month
up to 10 datasets connected
Debugging traces(history up to 2 weeks)
Discord community support
Up to 3 users
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Enterprise
for larger teams needing more security, more customization, manage permissions and dedicate support
custom
Get StartedCustom LLM fine-tuning
Up to unlimited queries
Up to unlimited datasets
Debugging traces(history up to 3 months)
Dedicated support
Available on premise
Enforced cybersecurity
Permission management
Up to unlimited users
SSO
Pandasai Frequently Asked Questions
What is PandasAI?
What are some key features of PandasAI?
How can PandasAI help insurance professionals?
What are some examples of conversational data analysis with PandasAI in the insurance domain?
How does PandasAI transform how insurers work with data?
What are some new features in PandasAI v2.0?