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LDiHK Analytics
InsurTech Venture Overview & MVP

Predicting Mental Illness Risk via Social Footprints

LDiHK is a pioneering InsurTech platform that bridges the gap between daily digital habits and mental wellness. By analyzing voluntary social media exports (YouTube, Instagram, TikTok, Spotify), we empower insurance providers to predict clinical risks, reward healthier habits, and deliver early preventative interventions.

The Market Opportunity

Strategic Fit: Hong Kong & Global Expansion

Mental health is a critical concern in high-density markets like Hong Kong. According to Gemini data and the Legislative Council Research Office, 13% of working adults, 24% of youth, and 12.5% of the elderly are diagnosed with mental disorders, with depression, bipolar disorder, and anxiety being the most prevalent.

Hong Kong is the ideal launchpad due to two unique advantages:

  • Hybrid Insurance System: The split between private and government-funded care rewards insurers who manage chronic risks early.
  • East-Meets-West Digital Intersection: Access to both Western (YouTube, Instagram, Spotify) and Eastern ecosystems enables training models for seamless future expansion into China.

The Ingestion Loop

01

GDPR Takeout Request

Users request their data packages directly from platforms under standard privacy regulations.

02

Ingest & Map Biomarkers

Our pipeline extracts usage hours, late-night circadian disruptions, and hourly fragmentation indices.

03

Risk Assessment & Rewards

Providers assess mental health wellness scores, providing premium offsets or early therapy recommendations.

The Stakeholder Ecosystem

Creating shared value for users, private insurers, healthcare experts, and society.

Insurance Providers

Gain competitive advantages in risk underwriting, lower long-term claims liabilities, and foster direct customer engagement through wellness incentive loops.

Policyholders / Users

Unlock premium offsets or voucher rewards by sharing GDPR data, access early clinical warnings, and receive personalized lifestyle recommendations.

Healthcare Experts

Receive high-fidelity clinical risk signals and historical user profiles to conduct optimized, preventative psychological interventions.

The Public & Society

Alleviates systemic pressure on public mental health systems, leading to a healthier, happier, and more productive workforce.

Venture Deployment Roadmap

Our three-phased roadmap to bring preventative mental wellness underwriting to life.

Phase 1: Ingestion & Training (Years 1-2)

Model Integration & Health Record Pairing

Insurance companies incentivize users to share their basic GDPR-compliant data takeouts. In combination with historical medical claims records, we train custom machine learning models to identify core digital behavior predictors of anxiety, depression, and stress.

Phase 2: Preventative Intervention

Voucher Incentives & Early Treatments

Using the early-warning models, insurance providers identify high-risk usage patterns in real-time. Insurers offer tailored vouchers and preventative clinical treatments, resolving wellness issues before they become severe, significantly lowering treatment liabilities.

Phase 3: Smart Underwriting

Dynamic Underwriting & Applicant Rewards

We transition to underwriting. New applicants who voluntarily verify their digital footprints receive custom insurance plans and discounts for displaying low-risk habits, creating a substantial competitive advantage for partner insurers.

The Hackathon Showcase

Our Hackathon MVP Goal

Due to time constraints, this MVP is not a fully-trained clinical model. Instead, it is an interactive platform built to demonstrate the exact data aggregation workflow and business value.

By allowing judges and attendees to inspect their own social history (YouTube, Instagram, TikTok, and Spotify) against standard medical wellness benchmarks, we showcase the granularity of the dataset we intend to gather.

4+ Platforms Modeled
100% Data Anonymized

What we are analyzing:

  • 1
    Screen Volume Index: Aggregated active screen hours across platforms per day. High volumes represent a primary indicator of mental fatigue.
  • 2
    Circadian Disruption Index: Focuses on late-night activity bounds (23:00 to 5:00) that delay sleep cycles and correlate directly with anxiety risk.
  • 3
    Task Fragmentation Index: Evaluates how frequently the user checks platforms throughout the day, measuring attention division.
Investor Relations

Review the Venture Deck

Download our pitchdeck to review the deployment roadmap, Netlify Astro static builds, AWS S3 infrastructure costing, and performance benchmarks.

PDF
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