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Custom Insurance Profitability Analytics Platform
  1. case
  2. Custom Insurance Profitability Analytics Platform

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Custom Insurance Profitability Analytics Platform

dataforest.ai
Insurance
Real estate
Technology

Challenges in Data Analysis and Profitability Prediction

The client struggles to analyze large volumes of tabular data to determine insurance profitability, predict profitable cases, and visualize actionable insights. Current systems lack real-time processing capabilities, flexible filtering, and predictive modeling for risk evaluation.

About the Client

A technology-driven insurance company leveraging patented analytical methods and unique data sources to evaluate property risks and streamline insurance purchasing processes.

Development Objectives

  • Build a real-time data analytics tool for insurance profitability evaluation
  • Develop a predictive model for identifying profitable insurance cases
  • Create interactive dashboards with customizable filters and visualizations
  • Process 10TB+ datasets efficiently with sub-2-second query performance
  • Optimize dashboard loading speed for seamless user experience

Core System Functionalities

  • Real-time data processing with dynamic dashboard updates
  • Predictive modeling using historical data (e.g., house type/age analysis)
  • Customizable filters for industry verticals, property types, and timeframes
  • Interactive visualizations of profit/loss metrics by category
  • Automated PDF report generation with key insights

Preferred Technologies Stack

Pandas
Dash
TensorFlow
PySpark
PostgreSQL

External System Integrations

  • PostgreSQL database
  • Slack notifications API

Non-Functional Requirements

  • Scalable architecture for 10TB+ datasets
  • Query response time <2 seconds
  • Dashboard page load time <1 second
  • Secure data handling (SOC 2 compliance)
  • Multi-threaded processing for large file uploads

Expected Business Impact

Enables data-driven underwriting decisions through real-time profitability insights, with potential revenue growth from optimized policy pricing. Predictive modeling improves risk assessment accuracy by 89%, while automated reporting reduces manual analysis time by 900+ hours/month.

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