Travel Insurance Platform

The Challenges

The travel insurance industry often struggles with outdated systems that make policy comparisons, payments, and commission management cumbersome. Beyond administrative inefficiency, a major challenge is risk assessment and fraud detection, which relies on manual review, leading to slow policy issuance and high operational costs.

Furthermore, achieving true customer-centricity is difficult due to the inability to quickly analyze complex user and claims data to offer genuinely personalized and dynamically priced policies. A critical pain point is the difficulty in extracting knowledge from thousands of pages of policy documents and FAQs to provide instant, accurate customer support and broker guidance.

AI-Powered Underwriting and Dynamic:

Policy Recommendation Engine The platform is built around an AI/ML engine that dramatically enhances the policy recommendation process. Instead of simple filtering, the system uses machine learning models to ingest hundreds of data points (e.g., travel history, current health, destination risk profiles, and historical claims data) to dynamically assess risk and instantly provide underwritten quotes. This allows for hyper-personalized policy bundles and dynamic pricing adjustments based on the real-time risk score of the traveler and trip.

Predictive Analytics and Real-Time Reporting:

Beyond standard metrics, the system employs predictive analytics to offer foresight. It uses ML to forecast potential claims volumes, identify anomaly patterns indicative of potential fraud, and predict customer churn risk, providing critical business intelligence for administrators.

Broker Empowerment with a Smart Workflow Manager:

The Broker Portal uses the LLM-driven knowledge retrieval system to act as a smart workflow manager. Brokers can use commands to instantly generate quotes, retrieve client policy details, and automate complex tasks like issuing coverage riders, significantly reducing time-to-sale. The platform also automates commission calculations.

LLM-Driven Knowledge Retrieval and Virtual Assistant:

This feature is powered by a sophisticated Retrieval-Augmented Generation (RAG) pipeline. PyPDF2 is used to ingest and process unstructured policy documents, while ChromaDB serves as the vector store to embed and retrieve relevant policy sections. LangChain orchestrates the entire process, connecting the retrieval from ChromaDB with the reasoning capabilities of OpenAI's LLMs. This creates a 24/7 virtual assistant for customers and brokers, providing instant, highly accurate answers drawn directly from the policy documents.

Secure Payment System:

The platform includes a payment gateway that supports multiple modes of payment, including monthly installments, ensuring secure transactions without brokers handling sensitive information..

Generative AI for Policy Summaries:

Utilizing Large Language Models (LLMs) to create simple, legally compliant policy summaries and communication tailored to a customer's reading level.

Computer Vision for Claims Processing:

Integrating image recognition to automatically verify and process simple claims by analyzing uploaded evidence (e.g., photos of damaged luggage).

SaaS Expansion:

The upcoming Skyler SaaS platform will enable agents and travel websites to embed travel insurance solutions without requiring technical expertise, supporting both no-code and API-based integrations via the existing FastAPI back end.

NetSet Software’s Travel Insurance Platform for Riskcare Insurance Brokerage

Tech stack

Laravel- 5.8 version

MySQL

Bootstrap

Python

FastAPI

LangChain

OpenAI

ChromaDB

PyPDF2

The Result

Got a project in mind? Get its estimation.