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Case Studies

AI-Driven Claims Processing

Discover how Synaptron’s AI-driven claims processing solution streamlined insurance workflows—automating validation, reducing fraud, accelerating settlements, and boosting customer satisfaction.
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Executive Summary

Inspection using AI

A top-tier insurance company engaged a reputed System Integrator (SI) partner to modernize its claims processing function. The SI brought in Synaptron as a strategic delivery arm to architect, build, and implement an AI-powered claims automation platform. Synaptron deployed a specialized team of solution architects, data scientists, and full-stack engineers to co-create the solution from ground up.

Delivered through our SI partner, the platform resulted in:

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50% reduction in claims processing turnaround time

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40% drop in manual intervention across retail policy types

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25% increase in fraud detection accuracy

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Rollout across 3 lines of business within 9 months

    This engagement highlights how Synaptron’s deep BFSI expertise and scalable resource model deliver tangible outcomes within large-scale transformation programs.

    Challenge

    Fragmented Crop Intelligence, Reactive Advisory, and Missed Yield Risks

    As one of India’s largest seed manufacturers, the client had a strong retail and research network, but lacked real-time agronomic visibility across distributed geographies.

    Key challenges:

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    Inconsistent Field Feedback Slowing R&D Decisions

    Inconsistent crop performance feedback from the field made R&D trials and product positioning decisions slow and imprecise

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    Delayed Pest and Disease Detection in High-Risk Zones

    Delayed detection of crop diseases and pest outbreaks, especially in high-risk regions, leading to avoidable farmer losses

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    Generic Advisories Ignoring Real-Time Crop Conditions

    Generic farmer advisories pushed via SMS or agents, without factoring in real-time crop health, location, or micro-climate

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    Manual Field Data Lacking Standardization and Geo-Context

    Manual data collection by field staff lacked geo-tagging, standardization, and diagnostic support

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    Satellite and Drone Data Underused without AI Insights

    Underutilization of satellite and drone data, due to lack of AI-based interpretation pipelines

    The client sought to deploy an AI-based solution stack, with minimal dependency on hardware rollout and high adaptability across crops and regions.

    Solution

    Agri AI Platform Engineered by Synaptron for Seed Trials, Disease Alerts, and Dynamic Advisory

    Synaptron delivered an end-to-end crop intelligence platform combining AI models, satellite data processing, and a lightweight field app. The platform was co-developed with the client’s digital transformation office and agronomy team.

    Key Modules Designed & Delivered:

    Disease Detection AI Capsule

    • Trained on over 25,000 annotated crop images across 7 crops (paddy, maize, cotton, soybean, chilli, bajra, and wheat)
    • Detected major diseases like leaf curl, blast, rust, and blight with over 93% precision using mobile camera input or drone image uploads

    Satellite & Weather Data Fusion for Yield Prediction

    • Integrated NDVI, rainfall, soil moisture, and growing degree day (GDD) indices from Sentinel & NASA MODIS data
    • AI models provided predictive yield forecasts at taluk/block level, calibrated with field validation from R&D plots

    Farmer Advisory Automation Engine

    • Smart backend matched real-time disease alerts, crop stage, and regional data to trigger personalized advisories (e.g., dosage recommendations, pest control schedule)
    • Integrated with multilingual IVR/SMS and WhatsApp APIs for last-mile deliver

      Field Force Mobile App

      • Enabled geo-tagged crop photo uploads, trial monitoring, and issue reporting with AI suggestions
      • Used for gathering on-ground validation of AI outputs and feedback loop training

      Outcome

      Precision Agri Intelligence Driving Faster Response, Higher Yield, and Farmer Trust

      The platform generated measurable agronomic and business impact:

      • 93% AI Accuracy in Field-Level Disease Detection, reducing dependency on lab confirmation or manual scouting
      • 15% Higher Yield Forecast Precision, enabling better demand planning and R&D investment decisions
      • 40% Faster Advisory Turnaround—automated alerts reduced average issue-to-solution time from 5 days to 2
      • Increased Brand Trust as farmers saw direct interventions from the company, reducing yield loss and improving loyalty
      • Improved Internal Efficiency, as field teams could now handle 2x more acreage per agronomist with the app and dashboard support

      Future

      Scaling to 1 Million Acres and New Crops

      Encouraged by impact, the client and Synaptron are scaling the platform in the next phase:

      • Expansion to 10+ crops including vegetables and horticulture
      • Integration with Retail CRM to drive input cross-sell based on forecast and disease model triggers
      • Blockchain Traceability Layer for export-focused supply chains
      • Voice-Enabled Advisory Bot to support low-literacy farmers
      • Public-Private Extension Collaboration to offer AI alerts to state agricultural departments under CSR programs