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Voice Recognition App for Insurance
Client: Leading insurance provider
QA
Expertise:
Tech Stack:
DevOps
UX/UI
PM
AI/ML
Frontend
Keras
TensorFlow
Hugging Face
AWS (S3, Sagemaker, Studio)
Scipy
PyAudio
Pandas
SpeechRecognition
Numpy
Business Analyst
Backend
The client is a leading insurance provider aiming to streamline their evaluation processes and enhance customer experiences through cutting-edge technology. Their focus is on improving efficiency while reducing the workload on their managerial teams.
About the Client:
The project involved developing a voice recognition application that allows users to obtain insurance estimates by simply stating key parameters of the insured object. The app leverages voice-to-text technology to process user inputs, identify the object, and provide an approximate insurance price. It acts as a bridge between automation and human expertise, requiring minimal intervention from managers for final validation.
About the Project

Challenges & Objectives

Challenges:
  1. Complex Voice Recognition Needs: The app needed to accurately recognize diverse user accents and terminology.
  2. Real-Time Interaction: Ensuring fast and reliable voice processing to maintain a seamless user experience.
  3. Accurate Object Matching: Displaying correct visual representations of objects based on user descriptions.
Objectives:
  1. Develop an intelligent voice recognition system to simplify the insurance evaluation process.
  2. Automate the price estimation workflow while ensuring data accuracy.
  3. Reduce the workload on insurance managers by enabling self-service functionality for users.
Implementation
01
Voice Recognition and Processing:
  • Integrated Python-based libraries like TensorFlow, Keras, and SpeechRecognition for accurate voice-to-text conversion.
  • Leveraged Hugging Face and NLTK for natural language understanding to interpret user inputs effectively.
02
Object Identification:
  • Designed algorithms to map spoken parameters (brand, production year) to a visual object database.
  • Used PyAudio for audio input processing and Scipy for signal analysis to enhance recognition accuracy.
03
Insurance Price Estimation:
  • Developed a backend system to calculate approximate insurance prices based on user-provided data.
  • Implemented AWS (S3, Sagemaker) to deploy the machine learning models securely and at scale.
04
User Confirmation Workflow:
  • Enabled a user-friendly interface to confirm object details and displayed results on-screen.
  • Integrated a notification system to alert managers for final cost validation when required.

Outcomes & Business Impact

  1. Faster Estimates: Reduced insurance evaluation time by 60%, offering users instant price feedback.
  2. Enhanced Accuracy: Achieved a 95% accuracy rate in matching voice inputs to database objects.
  3. Improved Manager Efficiency: Decreased manual workload by automating initial evaluations.
  4. Positive User Experience: Increased customer satisfaction with a seamless and intuitive process.
Business Impact:
  1. Cost Efficiency: Reduced operational costs by automating repetitive tasks.
  2. Increased Customer Retention: Advanced technology boosted customer loyalty and trust.
  3. Scalable Solution: Positioned the client as an industry innovator with a scalable AI-driven application.
  • +60% Faster Processes: Instant insurance estimates enhanced user experience.
  • 95% Recognition Accuracy: Ensured reliable object identification and pricing.
  • -40% Manager Workload: Automated repetitive tasks for efficiency gains.
  • +25% Customer Loyalty: Strengthened trust through cutting-edge technology.
Results & ROI
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