DEV Community: RUKSHANA S CSE

DeliverGuard AI – Building Trust in Micro-Insurance for Gig Workers

RUKSHANA S CSE — Mon, 30 Mar 2026 18:09:30 +0000

DeliverGuard AI is a parametric micro-insurance platform designed for delivery partners of Zomato.

It protects workers from income loss caused by external disruptions such as rain, traffic, extreme heat, and environmental conditions.

The system uses AI monitoring, OCR verification, and fraud detection to ensure fair and automated payouts.

Problem Statement

Delivery workers depend on daily or weekly earnings, but external factors such as:

Heavy rain
Flood
Traffic congestion
Extreme heat
Environmental disturbances
Curfew / strike

can reduce or completely stop their ability to work.

Currently, there is no reliable system to compensate short-term income loss.

Deliverable Expectations and it's Solutions

Expectations	Solutions
Onboarding	OCR-based income extraction with simple user profiling
Risk Profiling	AI-powered analysis using weather, AQI, traffic, and behavioral data
Policy Creation	Weekly income-based pricing with dynamic risk evaluation
Claim Triggering	Automated detection of disruptions (rain, AQI, traffic)
Payout Processing	Secure and instant bank transfers
Analytics Dashboard	Real-time insights on claims, payouts, and risk trends
Fraud Detection	GPS, IP tracking, device fingerprinting, and behavior analysis

Phase 1 – Building a Secure & Reliable Foundation

In the first phase of our project, we focused heavily on trust, verification, and fraud prevention, which are critical for any insurance system.

Adversarial Defense & Anti-Spoofing

DeliverGuard AI implements a multi-layer fraud detection system that validates user activity using location, device, network, and behavioral signals. Each feature is designed to detect a specific type of fraud and contribute to a unified risk score.

Detection Layer

1. GPS Verification System

Problem:

Users can spoof GPS using fake location apps, making it appear they are working when they are not.

Solution:

Continuously validate location consistency instead of trusting a single GPS point.

How it Works:

Collect GPS coordinates periodically (every few seconds)
Store previous and current locations
Calculate distance between points
Detect abnormal jumps (e.g., 100 km in seconds)
Flag inconsistent movement

Tech Stack:

navigator.geolocation
Haversine formula

2. IP Address Verification

Problem:

User’s network location may not match their physical location.

Solution:

Cross-check IP-based location with GPS coordinates.

How it Works:

Extract IP address from request
Use IP geolocation API to get location
Compare IP location with GPS location
Calculate distance mismatch
Flag large inconsistencies

Tech Stack:

ipapi / ipinfo

3. VPN Detection Mechanism

Problem:

Users can hide their real location using VPN or proxy services.

Solution:

Detect anonymized IP addresses and unusual location switching.

How it Works:

Check IP against VPN/proxy database
Detect rapid country switching
Identify high-risk IP patterns
Mark suspicious sessions

Tech Stack:

ipqualityscore
proxycheck.io

4. Device & Emulator Detection

Problem:

Fraudsters create multiple fake accounts using emulators.

Solution:

Generate unique device fingerprint and detect emulators.

How it Works:

Collect device information (OS, browser, screen)
Generate hashed deviceId (SHA-256)
Detect emulator signatures
Track multiple accounts on same device

Tech Stack:

FingerprintJS
Crypto hashing

5. Movement & Speed Analysis

Problem:

Fake GPS creates unrealistic movement patterns.

Solution:

Analyze speed and movement consistency.

How it Works:

Calculate distance between GPS points
Compute speed = distance / time
Detect:
- No movement
- Unrealistic speed (>120 km/h)
- Sudden jumps
Flag suspicious behavior

Tech Stack:

Haversine formula

6. Route Validation System

Problem:

Fake routes do not follow real-world roads.

Solution:

Compare user path with actual map routes.

How it Works:

Track sequence of GPS points
Map points onto real road network
Detect invalid paths (through buildings/water)
Validate route realism

Tech Stack:

OpenStreetMap
Leaflet

7. Log-Based Monitoring System

Problem:

Fraud patterns cannot be identified from a single event.

Solution:

Maintain historical logs for analysis.

How it Works:

Store all tracking data (GPS, IP, device, timestamp)
Analyze repeated anomalies
Detect long-term suspicious patterns
Flag repeat offenders

Tech Stack:

MongoDB
Logging system

Intelligence Layer

8. Spatio-Temporal Correlation

Problem:

Fraudsters operate in coordinated groups.

Solution:

Analyze location and time relationships.

How it Works:

Compare multiple users’ activity
Identify same location + same time patterns
Detect clustering behavior
Flag coordinated activity

Tech Stack:

MongoDB aggregation

9. Shared IP & Device Detection

Problem:

One attacker controls multiple accounts.

Solution:

Detect shared device and IP usage.

How it Works:

Store deviceId and IP for each user
Group users with same identifiers
Detect abnormal sharing
Flag accounts for investigation

Tech Stack:

Backend grouping logic

10. Fraud Ring Detection

Problem:

Large-scale fraud networks operate together.

Solution:

Identify clusters of users with similar behavior.

How it Works:

Analyze user patterns (routes, timing, devices)
Detect repeated similarities across accounts
Build clusters of related users
Identify fraud networks

Tech Stack:

Graph-based analysis

11. Behavioral Pattern Analysis

Problem:

Fake users behave unnaturally compared to real users.

Solution:

Analyze behavioral patterns over time.

How it Works:

Track delivery frequency and timing
Identify unusual consistency
Detect robotic or scripted behavior
Flag anomalies

Tech Stack:

Statistical models

12. Multi-Signal Fusion Engine

Problem:

Single signal is unreliable.

Solution:

Combine all signals for stronger detection.

How it Works:

Collect signals (GPS, IP, device, behavior)
Assign weight to each signal
Combine into unified decision
Detect fraud based on multiple indicators

Tech Stack:

Rule-based engine

Risk Scoring System

13. Dynamic Risk Scoring

Problem:

Not all anomalies indicate fraud.

Solution:

Assign weighted risk scores.

How it Works:

Each anomaly adds risk points
Combine scores from all modules
Calculate final risk score
Classify user risk level

Tech Stack:

Rule-based scoring system

Final Insight

DeliverGuard AI combines detection, intelligence, and risk scoring layers to build a robust, real-time fraud prevention system that ensures security while minimizing false positives.

OCR-Based Income Verification

To ensure accurate and automated income verification, DeliverGuard AI uses OCR (Optical Character Recognition) powered by EasyOCR.

Why OCR is Used

Eliminates manual verification of income proof
Automatically extracts data from transaction screenshots
Speeds up onboarding and claim validation
Reduces human errors and improves efficiency

How It Works

The user uploads a screenshot of their transaction or earnings proof
The system uses EasyOCR to extract text from the image
Key details such as:
- Platform name (e.g., Zomato, Swiggy)
- Transaction amount
- Date and time
Extracted data is processed and structured in the system

Example:

Input:
INR 3139 credited via ZOMATO

Output:
Premium Rate : 8% (Standard Plan)
Insurance Premium : ₹251

Fraud Prevention Mechanism

OCR alone cannot verify whether an image is original or edited. Therefore, DeliverGuard AI combines OCR with multiple validation techniques:

Identifies inconsistent formatting in manipulated screenshots
Flags duplicate or reused images
Uses metadata analysis as an additional validation layer

Cross-Verification with Delivery Platforms

To enhance reliability, the system can cross-verify user income with Zomato delivery platforms:

The extracted income data is compared with actual earnings records
Ensures that the submitted screenshot matches real transaction history
Prevents fraud caused by edited or AI-generated screenshots
Acts as a strong validation layer beyond OCR

Since OCR only reads visible text, cross-verification ensures authenticity by validating the data from trusted sources.

Workflow

Technology Used

EasyOCR – for text extraction from images
Image preprocessing – to improve OCR accuracy
Backend validation logic – for data matching
Platform verification (Zomato integration) – for authenticity checks

System Workflow

Collect user data
Validate inputs
Analyze patterns
Assign risk score
Trigger actions

Phase 1 Outcome

By the end of Phase 1, we successfully built:

A robust fraud detection system
A secure onboarding and verification pipeline
A trust-first foundation for insurance processing

This allowed us to confidently move to the next stage.

Phase 2 – Expanding Intelligence & Insurance Logic

After establishing a strong foundation, we progressed to Phase 2, where we focused on making the system smarter and more user-centric.

Insurance Plans

Plan	Premium	Hour Threshold	Max Weekly Payout
Basic	5%	8 hrs	₹2000
Standard	8%	6 hrs	₹4000
Premium	10%	4 hrs	₹8000

Premium Calculation

Weekly Premium = Weekly Income × Plan %

Example (Weekly Income = ₹7000):

Basic → ₹350
Standard → ₹560
Premium → ₹700

Payout Calculation

Hourly Income

Weekly Income ÷ 42 (6 hours/day × 7 days)

Claim Types

Day Claim → 6 × Hourly Income
Hour Claim → Threshold × Hourly Income

Final Rule

Final Payout = min(calculated amount, plan limit)

Disruption Detection

The system uses real-time APIs:

Weather API → Rain / flood
AQI API → Pollution
Traffic API → Congestion

Trigger conditions:

Rainfall ≥ 50 mm/hr
AQI ≥ 300
Traffic ≥ defined threshold

Loyalty Rewards

Levels

Silver → 3 months
Gold → 6 months
Diamond → 1 year

Benefits

Fuel rewards
Premium discounts
Faster claim processing
Increased coverage

Final Thought

DeliverGuard AI is not just an insurance system — it is a smart protection layer for gig workers, ensuring they are supported even when they cannot work.

By combining verification, intelligence, and automation, we are building a system that is:

Fair
Secure
Reliable
Worker-centric

DeliverGuard AI – Building Trust in Micro-Insurance for Gig Workers

RUKSHANA S CSE — Mon, 30 Mar 2026 18:09:30 +0000

The system uses AI monitoring, OCR verification, and fraud detection to ensure fair and automated payouts.

Problem Statement

Delivery workers depend on daily or weekly earnings, but external factors such as:

Heavy rain
Flood
Traffic congestion
Extreme heat
Environmental disturbances
Curfew / strike

can reduce or completely stop their ability to work.

Currently, there is no reliable system to compensate short-term income loss.

Deliverable Expectations and it's Solutions

Expectations	Solutions
Onboarding	OCR-based income extraction with simple user profiling
Risk Profiling	AI-powered analysis using weather, AQI, traffic, and behavioral data
Policy Creation	Weekly income-based pricing with dynamic risk evaluation
Claim Triggering	Automated detection of disruptions (rain, AQI, traffic)
Payout Processing	Secure and instant bank transfers
Analytics Dashboard	Real-time insights on claims, payouts, and risk trends
Fraud Detection	GPS, IP tracking, device fingerprinting, and behavior analysis

Phase 1 – Building a Secure & Reliable Foundation

In the first phase of our project, we focused heavily on trust, verification, and fraud prevention, which are critical for any insurance system.

Adversarial Defense & Anti-Spoofing

Detection Layer

1. GPS Verification System

Problem:

Users can spoof GPS using fake location apps, making it appear they are working when they are not.

Solution:

Continuously validate location consistency instead of trusting a single GPS point.

How it Works:

Collect GPS coordinates periodically (every few seconds)
Store previous and current locations
Calculate distance between points
Detect abnormal jumps (e.g., 100 km in seconds)
Flag inconsistent movement

Tech Stack:

navigator.geolocation
Haversine formula

2. IP Address Verification

Problem:

User’s network location may not match their physical location.

Solution:

Cross-check IP-based location with GPS coordinates.

How it Works:

Extract IP address from request
Use IP geolocation API to get location
Compare IP location with GPS location
Calculate distance mismatch
Flag large inconsistencies

Tech Stack:

ipapi / ipinfo

3. VPN Detection Mechanism

Problem:

Users can hide their real location using VPN or proxy services.

Solution:

Detect anonymized IP addresses and unusual location switching.

How it Works:

Check IP against VPN/proxy database
Detect rapid country switching
Identify high-risk IP patterns
Mark suspicious sessions

Tech Stack:

ipqualityscore
proxycheck.io

4. Device & Emulator Detection

Problem:

Fraudsters create multiple fake accounts using emulators.

Solution:

Generate unique device fingerprint and detect emulators.

How it Works:

Collect device information (OS, browser, screen)
Generate hashed deviceId (SHA-256)
Detect emulator signatures
Track multiple accounts on same device

Tech Stack:

FingerprintJS
Crypto hashing

5. Movement & Speed Analysis

Problem:

Fake GPS creates unrealistic movement patterns.

Solution:

Analyze speed and movement consistency.

How it Works:

Calculate distance between GPS points
Compute speed = distance / time
Detect:
- No movement
- Unrealistic speed (>120 km/h)
- Sudden jumps
Flag suspicious behavior

Tech Stack:

Haversine formula

6. Route Validation System

Problem:

Fake routes do not follow real-world roads.

Solution:

Compare user path with actual map routes.

How it Works:

Track sequence of GPS points
Map points onto real road network
Detect invalid paths (through buildings/water)
Validate route realism

Tech Stack:

OpenStreetMap
Leaflet

7. Log-Based Monitoring System

Problem:

Fraud patterns cannot be identified from a single event.

Solution:

Maintain historical logs for analysis.

How it Works:

Store all tracking data (GPS, IP, device, timestamp)
Analyze repeated anomalies
Detect long-term suspicious patterns
Flag repeat offenders

Tech Stack:

MongoDB
Logging system

Intelligence Layer

8. Spatio-Temporal Correlation

Problem:

Fraudsters operate in coordinated groups.

Solution:

Analyze location and time relationships.

How it Works:

Compare multiple users’ activity
Identify same location + same time patterns
Detect clustering behavior
Flag coordinated activity

Tech Stack:

MongoDB aggregation

9. Shared IP & Device Detection

Problem:

One attacker controls multiple accounts.

Solution:

Detect shared device and IP usage.

How it Works:

Store deviceId and IP for each user
Group users with same identifiers
Detect abnormal sharing
Flag accounts for investigation

Tech Stack:

Backend grouping logic

10. Fraud Ring Detection

Problem:

Large-scale fraud networks operate together.

Solution:

Identify clusters of users with similar behavior.

How it Works:

Analyze user patterns (routes, timing, devices)
Detect repeated similarities across accounts
Build clusters of related users
Identify fraud networks

Tech Stack:

Graph-based analysis

11. Behavioral Pattern Analysis

Problem:

Fake users behave unnaturally compared to real users.

Solution:

Analyze behavioral patterns over time.

How it Works:

Track delivery frequency and timing
Identify unusual consistency
Detect robotic or scripted behavior
Flag anomalies

Tech Stack:

Statistical models

12. Multi-Signal Fusion Engine

Problem:

Single signal is unreliable.

Solution:

Combine all signals for stronger detection.

How it Works:

Collect signals (GPS, IP, device, behavior)
Assign weight to each signal
Combine into unified decision
Detect fraud based on multiple indicators

Tech Stack:

Rule-based engine

Risk Scoring System

13. Dynamic Risk Scoring

Problem:

Not all anomalies indicate fraud.

Solution:

Assign weighted risk scores.

How it Works:

Each anomaly adds risk points
Combine scores from all modules
Calculate final risk score
Classify user risk level

Tech Stack:

Rule-based scoring system

Final Insight

DeliverGuard AI combines detection, intelligence, and risk scoring layers to build a robust, real-time fraud prevention system that ensures security while minimizing false positives.

OCR-Based Income Verification

To ensure accurate and automated income verification, DeliverGuard AI uses OCR (Optical Character Recognition) powered by EasyOCR.

Why OCR is Used

Eliminates manual verification of income proof
Automatically extracts data from transaction screenshots
Speeds up onboarding and claim validation
Reduces human errors and improves efficiency

How It Works

The user uploads a screenshot of their transaction or earnings proof
The system uses EasyOCR to extract text from the image
Key details such as:
- Platform name (e.g., Zomato, Swiggy)
- Transaction amount
- Date and time
Extracted data is processed and structured in the system

Example:

Input:
INR 3139 credited via ZOMATO

Output:
Premium Rate : 8% (Standard Plan)
Insurance Premium : ₹251

Fraud Prevention Mechanism

OCR alone cannot verify whether an image is original or edited. Therefore, DeliverGuard AI combines OCR with multiple validation techniques:

Identifies inconsistent formatting in manipulated screenshots
Flags duplicate or reused images
Uses metadata analysis as an additional validation layer

Cross-Verification with Delivery Platforms

To enhance reliability, the system can cross-verify user income with Zomato delivery platforms:

The extracted income data is compared with actual earnings records
Ensures that the submitted screenshot matches real transaction history
Prevents fraud caused by edited or AI-generated screenshots
Acts as a strong validation layer beyond OCR

Since OCR only reads visible text, cross-verification ensures authenticity by validating the data from trusted sources.

Workflow

Technology Used

EasyOCR – for text extraction from images
Image preprocessing – to improve OCR accuracy
Backend validation logic – for data matching
Platform verification (Zomato integration) – for authenticity checks

System Workflow

Collect user data
Validate inputs
Analyze patterns
Assign risk score
Trigger actions

Phase 1 Outcome

By the end of Phase 1, we successfully built:

A robust fraud detection system
A secure onboarding and verification pipeline
A trust-first foundation for insurance processing

This allowed us to confidently move to the next stage.

Phase 2 – Expanding Intelligence & Insurance Logic

After establishing a strong foundation, we progressed to Phase 2, where we focused on making the system smarter and more user-centric.

Insurance Plans

Plan	Premium	Hour Threshold	Max Weekly Payout
Basic	5%	8 hrs	₹2000
Standard	8%	6 hrs	₹4000
Premium	10%	4 hrs	₹8000

Premium Calculation

Weekly Premium = Weekly Income × Plan %

Example (Weekly Income = ₹7000):

Basic → ₹350
Standard → ₹560
Premium → ₹700

Payout Calculation

Hourly Income

Weekly Income ÷ 42 (6 hours/day × 7 days)

Claim Types

Day Claim → 6 × Hourly Income
Hour Claim → Threshold × Hourly Income

Final Rule

Final Payout = min(calculated amount, plan limit)

Disruption Detection

The system uses real-time APIs:

Weather API → Rain / flood
AQI API → Pollution
Traffic API → Congestion

Trigger conditions:

Rainfall ≥ 50 mm/hr
AQI ≥ 300
Traffic ≥ defined threshold

Loyalty Rewards

Levels

Silver → 3 months
Gold → 6 months
Diamond → 1 year

Benefits

Fuel rewards
Premium discounts
Faster claim processing
Increased coverage

Final Thought

DeliverGuard AI is not just an insurance system — it is a smart protection layer for gig workers, ensuring they are supported even when they cannot work.

By combining verification, intelligence, and automation, we are building a system that is:

Fair
Secure
Reliable
Worker-centric

DeliverGuard AI – Building Trust in Micro-Insurance for Gig Workers

RUKSHANA S CSE — Mon, 30 Mar 2026 18:09:30 +0000

The system uses AI monitoring, OCR verification, and fraud detection to ensure fair and automated payouts.

Problem Statement

Delivery workers depend on daily or weekly earnings, but external factors such as:

Heavy rain
Flood
Traffic congestion
Extreme heat
Environmental disturbances
Curfew / strike

can reduce or completely stop their ability to work.

Currently, there is no reliable system to compensate short-term income loss.

Deliverable Expectations and it's Solutions

Expectations	Solutions
Onboarding	OCR-based income extraction with simple user profiling
Risk Profiling	AI-powered analysis using weather, AQI, traffic, and behavioral data
Policy Creation	Weekly income-based pricing with dynamic risk evaluation
Claim Triggering	Automated detection of disruptions (rain, AQI, traffic)
Payout Processing	Secure and instant bank transfers
Analytics Dashboard	Real-time insights on claims, payouts, and risk trends
Fraud Detection	GPS, IP tracking, device fingerprinting, and behavior analysis

Phase 1 – Building a Secure & Reliable Foundation

In the first phase of our project, we focused heavily on trust, verification, and fraud prevention, which are critical for any insurance system.

Adversarial Defense & Anti-Spoofing

Detection Layer

1. GPS Verification System

Problem:

Users can spoof GPS using fake location apps, making it appear they are working when they are not.

Solution:

Continuously validate location consistency instead of trusting a single GPS point.

How it Works:

Collect GPS coordinates periodically (every few seconds)
Store previous and current locations
Calculate distance between points
Detect abnormal jumps (e.g., 100 km in seconds)
Flag inconsistent movement

Tech Stack:

navigator.geolocation
Haversine formula

2. IP Address Verification

Problem:

User’s network location may not match their physical location.

Solution:

Cross-check IP-based location with GPS coordinates.

How it Works:

Extract IP address from request
Use IP geolocation API to get location
Compare IP location with GPS location
Calculate distance mismatch
Flag large inconsistencies

Tech Stack:

ipapi / ipinfo

3. VPN Detection Mechanism

Problem:

Users can hide their real location using VPN or proxy services.

Solution:

Detect anonymized IP addresses and unusual location switching.

How it Works:

Check IP against VPN/proxy database
Detect rapid country switching
Identify high-risk IP patterns
Mark suspicious sessions

Tech Stack:

ipqualityscore
proxycheck.io

4. Device & Emulator Detection

Problem:

Fraudsters create multiple fake accounts using emulators.

Solution:

Generate unique device fingerprint and detect emulators.

How it Works:

Collect device information (OS, browser, screen)
Generate hashed deviceId (SHA-256)
Detect emulator signatures
Track multiple accounts on same device

Tech Stack:

FingerprintJS
Crypto hashing

5. Movement & Speed Analysis

Problem:

Fake GPS creates unrealistic movement patterns.

Solution:

Analyze speed and movement consistency.

How it Works:

Calculate distance between GPS points
Compute speed = distance / time
Detect:
- No movement
- Unrealistic speed (>120 km/h)
- Sudden jumps
Flag suspicious behavior

Tech Stack:

Haversine formula

6. Route Validation System

Problem:

Fake routes do not follow real-world roads.

Solution:

Compare user path with actual map routes.

How it Works:

Track sequence of GPS points
Map points onto real road network
Detect invalid paths (through buildings/water)
Validate route realism

Tech Stack:

OpenStreetMap
Leaflet

7. Log-Based Monitoring System

Problem:

Fraud patterns cannot be identified from a single event.

Solution:

Maintain historical logs for analysis.

How it Works:

Store all tracking data (GPS, IP, device, timestamp)
Analyze repeated anomalies
Detect long-term suspicious patterns
Flag repeat offenders

Tech Stack:

MongoDB
Logging system

Intelligence Layer

8. Spatio-Temporal Correlation

Problem:

Fraudsters operate in coordinated groups.

Solution:

Analyze location and time relationships.

How it Works:

Compare multiple users’ activity
Identify same location + same time patterns
Detect clustering behavior
Flag coordinated activity

Tech Stack:

MongoDB aggregation

9. Shared IP & Device Detection

Problem:

One attacker controls multiple accounts.

Solution:

Detect shared device and IP usage.

How it Works:

Store deviceId and IP for each user
Group users with same identifiers
Detect abnormal sharing
Flag accounts for investigation

Tech Stack:

Backend grouping logic

10. Fraud Ring Detection

Problem:

Large-scale fraud networks operate together.

Solution:

Identify clusters of users with similar behavior.

How it Works:

Analyze user patterns (routes, timing, devices)
Detect repeated similarities across accounts
Build clusters of related users
Identify fraud networks

Tech Stack:

Graph-based analysis

11. Behavioral Pattern Analysis

Problem:

Fake users behave unnaturally compared to real users.

Solution:

Analyze behavioral patterns over time.

How it Works:

Track delivery frequency and timing
Identify unusual consistency
Detect robotic or scripted behavior
Flag anomalies

Tech Stack:

Statistical models

12. Multi-Signal Fusion Engine

Problem:

Single signal is unreliable.

Solution:

Combine all signals for stronger detection.

How it Works:

Collect signals (GPS, IP, device, behavior)
Assign weight to each signal
Combine into unified decision
Detect fraud based on multiple indicators

Tech Stack:

Rule-based engine

Risk Scoring System

13. Dynamic Risk Scoring

Problem:

Not all anomalies indicate fraud.

Solution:

Assign weighted risk scores.

How it Works:

Each anomaly adds risk points
Combine scores from all modules
Calculate final risk score
Classify user risk level

Tech Stack:

Rule-based scoring system

Final Insight

DeliverGuard AI combines detection, intelligence, and risk scoring layers to build a robust, real-time fraud prevention system that ensures security while minimizing false positives.

OCR-Based Income Verification

To ensure accurate and automated income verification, DeliverGuard AI uses OCR (Optical Character Recognition) powered by EasyOCR.

Why OCR is Used

Eliminates manual verification of income proof
Automatically extracts data from transaction screenshots
Speeds up onboarding and claim validation
Reduces human errors and improves efficiency

How It Works

The user uploads a screenshot of their transaction or earnings proof
The system uses EasyOCR to extract text from the image
Key details such as:
- Platform name (e.g., Zomato, Swiggy)
- Transaction amount
- Date and time
Extracted data is processed and structured in the system

Example:

Input:
INR 3139 credited via ZOMATO

Output:
Premium Rate : 8% (Standard Plan)
Insurance Premium : ₹251

Fraud Prevention Mechanism

OCR alone cannot verify whether an image is original or edited. Therefore, DeliverGuard AI combines OCR with multiple validation techniques:

Identifies inconsistent formatting in manipulated screenshots
Flags duplicate or reused images
Uses metadata analysis as an additional validation layer

Cross-Verification with Delivery Platforms

To enhance reliability, the system can cross-verify user income with Zomato delivery platforms:

The extracted income data is compared with actual earnings records
Ensures that the submitted screenshot matches real transaction history
Prevents fraud caused by edited or AI-generated screenshots
Acts as a strong validation layer beyond OCR

Since OCR only reads visible text, cross-verification ensures authenticity by validating the data from trusted sources.

Workflow

Technology Used

EasyOCR – for text extraction from images
Image preprocessing – to improve OCR accuracy
Backend validation logic – for data matching
Platform verification (Zomato integration) – for authenticity checks

System Workflow

Collect user data
Validate inputs
Analyze patterns
Assign risk score
Trigger actions

Phase 1 Outcome

By the end of Phase 1, we successfully built:

A robust fraud detection system
A secure onboarding and verification pipeline
A trust-first foundation for insurance processing

This allowed us to confidently move to the next stage.

Phase 2 – Expanding Intelligence & Insurance Logic

After establishing a strong foundation, we progressed to Phase 2, where we focused on making the system smarter and more user-centric.

Insurance Plans

Plan	Premium	Hour Threshold	Max Weekly Payout
Basic	5%	8 hrs	₹2000
Standard	8%	6 hrs	₹4000
Premium	10%	4 hrs	₹8000

Premium Calculation

Weekly Premium = Weekly Income × Plan %

Example (Weekly Income = ₹7000):

Basic → ₹350
Standard → ₹560
Premium → ₹700

Payout Calculation

Hourly Income

Weekly Income ÷ 42 (6 hours/day × 7 days)

Claim Types

Day Claim → 6 × Hourly Income
Hour Claim → Threshold × Hourly Income

Final Rule

Final Payout = min(calculated amount, plan limit)

Disruption Detection

The system uses real-time APIs:

Weather API → Rain / flood
AQI API → Pollution
Traffic API → Congestion

Trigger conditions:

Rainfall ≥ 50 mm/hr
AQI ≥ 300
Traffic ≥ defined threshold

Loyalty Rewards

Levels

Silver → 3 months
Gold → 6 months
Diamond → 1 year

Benefits

Fuel rewards
Premium discounts
Faster claim processing
Increased coverage

Final Thought

DeliverGuard AI is not just an insurance system — it is a smart protection layer for gig workers, ensuring they are supported even when they cannot work.

By combining verification, intelligence, and automation, we are building a system that is:

Fair
Secure
Reliable
Worker-centric

DeliverGuard AI – Building Trust in Micro-Insurance for Gig Workers

RUKSHANA S CSE — Mon, 30 Mar 2026 18:09:30 +0000

The system uses AI monitoring, OCR verification, and fraud detection to ensure fair and automated payouts.

Problem Statement

Delivery workers depend on daily or weekly earnings, but external factors such as:

Heavy rain
Flood
Traffic congestion
Extreme heat
Environmental disturbances
Curfew / strike

can reduce or completely stop their ability to work.

Currently, there is no reliable system to compensate short-term income loss.

Deliverable Expectations and it's Solutions

Expectations	Solutions
Onboarding	OCR-based income extraction with simple user profiling
Risk Profiling	AI-powered analysis using weather, AQI, traffic, and behavioral data
Policy Creation	Weekly income-based pricing with dynamic risk evaluation
Claim Triggering	Automated detection of disruptions (rain, AQI, traffic)
Payout Processing	Secure and instant bank transfers
Analytics Dashboard	Real-time insights on claims, payouts, and risk trends
Fraud Detection	GPS, IP tracking, device fingerprinting, and behavior analysis

Phase 1 – Building a Secure & Reliable Foundation

In the first phase of our project, we focused heavily on trust, verification, and fraud prevention, which are critical for any insurance system.

Adversarial Defense & Anti-Spoofing

Detection Layer

1. GPS Verification System

Problem:

Users can spoof GPS using fake location apps, making it appear they are working when they are not.

Solution:

Continuously validate location consistency instead of trusting a single GPS point.

How it Works:

Collect GPS coordinates periodically (every few seconds)
Store previous and current locations
Calculate distance between points
Detect abnormal jumps (e.g., 100 km in seconds)
Flag inconsistent movement

Tech Stack:

navigator.geolocation
Haversine formula

2. IP Address Verification

Problem:

User’s network location may not match their physical location.

Solution:

Cross-check IP-based location with GPS coordinates.

How it Works:

Extract IP address from request
Use IP geolocation API to get location
Compare IP location with GPS location
Calculate distance mismatch
Flag large inconsistencies

Tech Stack:

ipapi / ipinfo

3. VPN Detection Mechanism

Problem:

Users can hide their real location using VPN or proxy services.

Solution:

Detect anonymized IP addresses and unusual location switching.

How it Works:

Check IP against VPN/proxy database
Detect rapid country switching
Identify high-risk IP patterns
Mark suspicious sessions

Tech Stack:

ipqualityscore
proxycheck.io

4. Device & Emulator Detection

Problem:

Fraudsters create multiple fake accounts using emulators.

Solution:

Generate unique device fingerprint and detect emulators.

How it Works:

Collect device information (OS, browser, screen)
Generate hashed deviceId (SHA-256)
Detect emulator signatures
Track multiple accounts on same device

Tech Stack:

FingerprintJS
Crypto hashing

5. Movement & Speed Analysis

Problem:

Fake GPS creates unrealistic movement patterns.

Solution:

Analyze speed and movement consistency.

How it Works:

Calculate distance between GPS points
Compute speed = distance / time
Detect:
- No movement
- Unrealistic speed (>120 km/h)
- Sudden jumps
Flag suspicious behavior

Tech Stack:

Haversine formula

6. Route Validation System

Problem:

Fake routes do not follow real-world roads.

Solution:

Compare user path with actual map routes.

How it Works:

Track sequence of GPS points
Map points onto real road network
Detect invalid paths (through buildings/water)
Validate route realism

Tech Stack:

OpenStreetMap
Leaflet

7. Log-Based Monitoring System

Problem:

Fraud patterns cannot be identified from a single event.

Solution:

Maintain historical logs for analysis.

How it Works:

Store all tracking data (GPS, IP, device, timestamp)
Analyze repeated anomalies
Detect long-term suspicious patterns
Flag repeat offenders

Tech Stack:

MongoDB
Logging system

Intelligence Layer

8. Spatio-Temporal Correlation

Problem:

Fraudsters operate in coordinated groups.

Solution:

Analyze location and time relationships.

How it Works:

Compare multiple users’ activity
Identify same location + same time patterns
Detect clustering behavior
Flag coordinated activity

Tech Stack:

MongoDB aggregation

9. Shared IP & Device Detection

Problem:

One attacker controls multiple accounts.

Solution:

Detect shared device and IP usage.

How it Works:

Store deviceId and IP for each user
Group users with same identifiers
Detect abnormal sharing
Flag accounts for investigation

Tech Stack:

Backend grouping logic

10. Fraud Ring Detection

Problem:

Large-scale fraud networks operate together.

Solution:

Identify clusters of users with similar behavior.

How it Works:

Analyze user patterns (routes, timing, devices)
Detect repeated similarities across accounts
Build clusters of related users
Identify fraud networks

Tech Stack:

Graph-based analysis

11. Behavioral Pattern Analysis

Problem:

Fake users behave unnaturally compared to real users.

Solution:

Analyze behavioral patterns over time.

How it Works:

Track delivery frequency and timing
Identify unusual consistency
Detect robotic or scripted behavior
Flag anomalies

Tech Stack:

Statistical models

12. Multi-Signal Fusion Engine

Problem:

Single signal is unreliable.

Solution:

Combine all signals for stronger detection.

How it Works:

Collect signals (GPS, IP, device, behavior)
Assign weight to each signal
Combine into unified decision
Detect fraud based on multiple indicators

Tech Stack:

Rule-based engine

Risk Scoring System

13. Dynamic Risk Scoring

Problem:

Not all anomalies indicate fraud.

Solution:

Assign weighted risk scores.

How it Works:

Each anomaly adds risk points
Combine scores from all modules
Calculate final risk score
Classify user risk level

Tech Stack:

Rule-based scoring system

Final Insight

DeliverGuard AI combines detection, intelligence, and risk scoring layers to build a robust, real-time fraud prevention system that ensures security while minimizing false positives.

OCR-Based Income Verification

To ensure accurate and automated income verification, DeliverGuard AI uses OCR (Optical Character Recognition) powered by EasyOCR.

Why OCR is Used

Eliminates manual verification of income proof
Automatically extracts data from transaction screenshots
Speeds up onboarding and claim validation
Reduces human errors and improves efficiency

How It Works

The user uploads a screenshot of their transaction or earnings proof
The system uses EasyOCR to extract text from the image
Key details such as:
- Platform name (e.g., Zomato, Swiggy)
- Transaction amount
- Date and time
Extracted data is processed and structured in the system

Example:

Input:
INR 3139 credited via ZOMATO

Output:
Premium Rate : 8% (Standard Plan)
Insurance Premium : ₹251

Fraud Prevention Mechanism

OCR alone cannot verify whether an image is original or edited. Therefore, DeliverGuard AI combines OCR with multiple validation techniques:

Identifies inconsistent formatting in manipulated screenshots
Flags duplicate or reused images
Uses metadata analysis as an additional validation layer

Cross-Verification with Delivery Platforms

To enhance reliability, the system can cross-verify user income with Zomato delivery platforms:

The extracted income data is compared with actual earnings records
Ensures that the submitted screenshot matches real transaction history
Prevents fraud caused by edited or AI-generated screenshots
Acts as a strong validation layer beyond OCR

Since OCR only reads visible text, cross-verification ensures authenticity by validating the data from trusted sources.

Workflow

Technology Used

EasyOCR – for text extraction from images
Image preprocessing – to improve OCR accuracy
Backend validation logic – for data matching
Platform verification (Zomato integration) – for authenticity checks

System Workflow

Collect user data
Validate inputs
Analyze patterns
Assign risk score
Trigger actions

Phase 1 Outcome

By the end of Phase 1, we successfully built:

A robust fraud detection system
A secure onboarding and verification pipeline
A trust-first foundation for insurance processing

This allowed us to confidently move to the next stage.

Phase 2 – Expanding Intelligence & Insurance Logic

After establishing a strong foundation, we progressed to Phase 2, where we focused on making the system smarter and more user-centric.

Insurance Plans

Plan	Premium	Hour Threshold	Max Weekly Payout
Basic	5%	8 hrs	₹2000
Standard	8%	6 hrs	₹4000
Premium	10%	4 hrs	₹8000

Premium Calculation

Weekly Premium = Weekly Income × Plan %

Example (Weekly Income = ₹7000):

Basic → ₹350
Standard → ₹560
Premium → ₹700

Payout Calculation

Hourly Income

Weekly Income ÷ 42 (6 hours/day × 7 days)

Claim Types

Day Claim → 6 × Hourly Income
Hour Claim → Threshold × Hourly Income

Final Rule

Final Payout = min(calculated amount, plan limit)

Disruption Detection

The system uses real-time APIs:

Weather API → Rain / flood
AQI API → Pollution
Traffic API → Congestion

Trigger conditions:

Rainfall ≥ 50 mm/hr
AQI ≥ 300
Traffic ≥ defined threshold

Loyalty Rewards

Levels

Silver → 3 months
Gold → 6 months
Diamond → 1 year

Benefits

Fuel rewards
Premium discounts
Faster claim processing
Increased coverage

Final Thought

DeliverGuard AI is not just an insurance system — it is a smart protection layer for gig workers, ensuring they are supported even when they cannot work.

By combining verification, intelligence, and automation, we are building a system that is:

Fair
Secure
Reliable
Worker-centric

DeliverGuard AI – Building Trust in Micro-Insurance for Gig Workers

RUKSHANA S CSE — Mon, 30 Mar 2026 18:09:29 +0000

The system uses AI monitoring, OCR verification, and fraud detection to ensure fair and automated payouts.

Problem Statement

Delivery workers depend on daily or weekly earnings, but external factors such as:

Heavy rain
Flood
Traffic congestion
Extreme heat
Environmental disturbances
Curfew / strike

can reduce or completely stop their ability to work.

Currently, there is no reliable system to compensate short-term income loss.

Deliverable Expectations and it's Solutions

Expectations	Solutions
Onboarding	OCR-based income extraction with simple user profiling
Risk Profiling	AI-powered analysis using weather, AQI, traffic, and behavioral data
Policy Creation	Weekly income-based pricing with dynamic risk evaluation
Claim Triggering	Automated detection of disruptions (rain, AQI, traffic)
Payout Processing	Secure and instant bank transfers
Analytics Dashboard	Real-time insights on claims, payouts, and risk trends
Fraud Detection	GPS, IP tracking, device fingerprinting, and behavior analysis

Phase 1 – Building a Secure & Reliable Foundation

In the first phase of our project, we focused heavily on trust, verification, and fraud prevention, which are critical for any insurance system.

Adversarial Defense & Anti-Spoofing

Detection Layer

1. GPS Verification System

Problem:

Users can spoof GPS using fake location apps, making it appear they are working when they are not.

Solution:

Continuously validate location consistency instead of trusting a single GPS point.

How it Works:

Collect GPS coordinates periodically (every few seconds)
Store previous and current locations
Calculate distance between points
Detect abnormal jumps (e.g., 100 km in seconds)
Flag inconsistent movement

Tech Stack:

navigator.geolocation
Haversine formula

2. IP Address Verification

Problem:

User’s network location may not match their physical location.

Solution:

Cross-check IP-based location with GPS coordinates.

How it Works:

Extract IP address from request
Use IP geolocation API to get location
Compare IP location with GPS location
Calculate distance mismatch
Flag large inconsistencies

Tech Stack:

ipapi / ipinfo

3. VPN Detection Mechanism

Problem:

Users can hide their real location using VPN or proxy services.

Solution:

Detect anonymized IP addresses and unusual location switching.

How it Works:

Check IP against VPN/proxy database
Detect rapid country switching
Identify high-risk IP patterns
Mark suspicious sessions

Tech Stack:

ipqualityscore
proxycheck.io

4. Device & Emulator Detection

Problem:

Fraudsters create multiple fake accounts using emulators.

Solution:

Generate unique device fingerprint and detect emulators.

How it Works:

Collect device information (OS, browser, screen)
Generate hashed deviceId (SHA-256)
Detect emulator signatures
Track multiple accounts on same device

Tech Stack:

FingerprintJS
Crypto hashing

5. Movement & Speed Analysis

Problem:

Fake GPS creates unrealistic movement patterns.

Solution:

Analyze speed and movement consistency.

How it Works:

Calculate distance between GPS points
Compute speed = distance / time
Detect:
- No movement
- Unrealistic speed (>120 km/h)
- Sudden jumps
Flag suspicious behavior

Tech Stack:

Haversine formula

6. Route Validation System

Problem:

Fake routes do not follow real-world roads.

Solution:

Compare user path with actual map routes.

How it Works:

Track sequence of GPS points
Map points onto real road network
Detect invalid paths (through buildings/water)
Validate route realism

Tech Stack:

OpenStreetMap
Leaflet

7. Log-Based Monitoring System

Problem:

Fraud patterns cannot be identified from a single event.

Solution:

Maintain historical logs for analysis.

How it Works:

Store all tracking data (GPS, IP, device, timestamp)
Analyze repeated anomalies
Detect long-term suspicious patterns
Flag repeat offenders

Tech Stack:

MongoDB
Logging system

Intelligence Layer

8. Spatio-Temporal Correlation

Problem:

Fraudsters operate in coordinated groups.

Solution:

Analyze location and time relationships.

How it Works:

Compare multiple users’ activity
Identify same location + same time patterns
Detect clustering behavior
Flag coordinated activity

Tech Stack:

MongoDB aggregation

9. Shared IP & Device Detection

Problem:

One attacker controls multiple accounts.

Solution:

Detect shared device and IP usage.

How it Works:

Store deviceId and IP for each user
Group users with same identifiers
Detect abnormal sharing
Flag accounts for investigation

Tech Stack:

Backend grouping logic

10. Fraud Ring Detection

Problem:

Large-scale fraud networks operate together.

Solution:

Identify clusters of users with similar behavior.

How it Works:

Analyze user patterns (routes, timing, devices)
Detect repeated similarities across accounts
Build clusters of related users
Identify fraud networks

Tech Stack:

Graph-based analysis

11. Behavioral Pattern Analysis

Problem:

Fake users behave unnaturally compared to real users.

Solution:

Analyze behavioral patterns over time.

How it Works:

Track delivery frequency and timing
Identify unusual consistency
Detect robotic or scripted behavior
Flag anomalies

Tech Stack:

Statistical models

12. Multi-Signal Fusion Engine

Problem:

Single signal is unreliable.

Solution:

Combine all signals for stronger detection.

How it Works:

Collect signals (GPS, IP, device, behavior)
Assign weight to each signal
Combine into unified decision
Detect fraud based on multiple indicators

Tech Stack:

Rule-based engine

Risk Scoring System

13. Dynamic Risk Scoring

Problem:

Not all anomalies indicate fraud.

Solution:

Assign weighted risk scores.

How it Works:

Each anomaly adds risk points
Combine scores from all modules
Calculate final risk score
Classify user risk level

Tech Stack:

Rule-based scoring system

Final Insight

DeliverGuard AI combines detection, intelligence, and risk scoring layers to build a robust, real-time fraud prevention system that ensures security while minimizing false positives.

OCR-Based Income Verification

To ensure accurate and automated income verification, DeliverGuard AI uses OCR (Optical Character Recognition) powered by EasyOCR.

Why OCR is Used

Eliminates manual verification of income proof
Automatically extracts data from transaction screenshots
Speeds up onboarding and claim validation
Reduces human errors and improves efficiency

How It Works

The user uploads a screenshot of their transaction or earnings proof
The system uses EasyOCR to extract text from the image
Key details such as:
- Platform name (e.g., Zomato, Swiggy)
- Transaction amount
- Date and time
Extracted data is processed and structured in the system

Example:

Input:
INR 3139 credited via ZOMATO

Output:
Premium Rate : 8% (Standard Plan)
Insurance Premium : ₹251

Fraud Prevention Mechanism

OCR alone cannot verify whether an image is original or edited. Therefore, DeliverGuard AI combines OCR with multiple validation techniques:

Identifies inconsistent formatting in manipulated screenshots
Flags duplicate or reused images
Uses metadata analysis as an additional validation layer

Cross-Verification with Delivery Platforms

To enhance reliability, the system can cross-verify user income with Zomato delivery platforms:

The extracted income data is compared with actual earnings records
Ensures that the submitted screenshot matches real transaction history
Prevents fraud caused by edited or AI-generated screenshots
Acts as a strong validation layer beyond OCR

Since OCR only reads visible text, cross-verification ensures authenticity by validating the data from trusted sources.

Workflow

Technology Used

EasyOCR – for text extraction from images
Image preprocessing – to improve OCR accuracy
Backend validation logic – for data matching
Platform verification (Zomato integration) – for authenticity checks

System Workflow

Collect user data
Validate inputs
Analyze patterns
Assign risk score
Trigger actions

Phase 1 Outcome

By the end of Phase 1, we successfully built:

A robust fraud detection system
A secure onboarding and verification pipeline
A trust-first foundation for insurance processing

This allowed us to confidently move to the next stage.

Phase 2 – Expanding Intelligence & Insurance Logic

After establishing a strong foundation, we progressed to Phase 2, where we focused on making the system smarter and more user-centric.

Insurance Plans

Plan	Premium	Hour Threshold	Max Weekly Payout
Basic	5%	8 hrs	₹2000
Standard	8%	6 hrs	₹4000
Premium	10%	4 hrs	₹8000

Premium Calculation

Weekly Premium = Weekly Income × Plan %

Example (Weekly Income = ₹7000):

Basic → ₹350
Standard → ₹560
Premium → ₹700

Payout Calculation

Hourly Income

Weekly Income ÷ 42 (6 hours/day × 7 days)

Claim Types

Day Claim → 6 × Hourly Income
Hour Claim → Threshold × Hourly Income

Final Rule

Final Payout = min(calculated amount, plan limit)

Disruption Detection

The system uses real-time APIs:

Weather API → Rain / flood
AQI API → Pollution
Traffic API → Congestion

Trigger conditions:

Rainfall ≥ 50 mm/hr
AQI ≥ 300
Traffic ≥ defined threshold

Loyalty Rewards

Levels

Silver → 3 months
Gold → 6 months
Diamond → 1 year

Benefits

Fuel rewards
Premium discounts
Faster claim processing
Increased coverage

Final Thought

DeliverGuard AI is not just an insurance system — it is a smart protection layer for gig workers, ensuring they are supported even when they cannot work.

By combining verification, intelligence, and automation, we are building a system that is:

Fair
Secure
Reliable
Worker-centric

Protecting the Gig Workforce: A Smarter Approach to Micro-Insurance

RUKSHANA S CSE — Mon, 16 Mar 2026 18:20:52 +0000

DEVTrails 2026 | InsurTech | Social Impact

“Unexpected events reveal how fragile financial security can be.”

A Moment That Changes the Day

Picture a delivery partner working across the city. Suddenly, heavy rain starts pouring, roads begin to flood, and traffic slows down everywhere.

For most people, this might simply mean ordering food and waiting indoors.

But for the delivery worker on the road, the situation becomes much more serious.

They must decide whether to continue working in unsafe conditions or stop working and lose a portion of their daily income.

This situation represents a challenge faced by many gig workers every day.

The Challenge in the Gig Economy

The gig economy has grown rapidly, especially in sectors such as food delivery and logistics.

However, many workers in this sector do not have the same financial protections that traditional employees receive.

External disruptions such as:

extreme weather conditions
heavy traffic congestion
public disturbances
environmental hazards

can immediately reduce their ability to work and earn.

Even losing a few hours of work can significantly impact weekly income.

Rethinking Financial Protection

While exploring solutions during the DEVTrails hackathon, we started thinking about how technology could help address this issue.

Instead of creating another typical application, the goal was to design a system that could act as a financial safety layer for gig workers when unexpected disruptions occur.

The idea was simple: create a system that automatically detects disruptions and compensates workers fairly when their work is affected.

The Concept

The platform focuses on parametric micro-insurance for delivery partners.

Rather than requiring manual claims, the system monitors real-world conditions and determines when workers are unable to complete deliveries.

When disruptions are detected, compensation is calculated based on previous income patterns.

This approach helps ensure faster payouts and reduces the complexity of traditional insurance processes.

How the Platform Works

Worker Profile

Delivery partners first provide information such as their work zones, typical working hours, and weekly income.

This data helps estimate potential earnings and calculate fair compensation.

Insurance Plans

The platform offers multiple coverage plans designed around workers’ income levels.

Each plan adjusts parameters such as:

premium percentage
claim thresholds
maximum payout limits

while keeping the coverage events consistent across plans.

Monitoring External Conditions

The system continuously monitors environmental and urban factors that may affect deliveries.

Examples include weather conditions, traffic congestion, and other disruptions that can limit a worker’s ability to complete orders.

Automatic Claim Calculation

When a disruption affects working hours, the system calculates compensation using a transparent formula based on the worker’s recent income history.

This removes the need for complex claim submissions.

Fast Payout Processing

Once the disruption is validated, payouts are processed and transferred to the worker through the platform’s payment system.

This ensures workers receive support quickly during unexpected interruptions.

Encouraging Consistency

The system can also include reward mechanisms for workers who consistently participate in the program.

Workers who maintain regular contributions without filing claims over long periods can receive benefits such as improved coverage or reduced premiums.

Why This Approach Matters

The gig economy continues to expand, but financial protections for gig workers have not always evolved at the same pace.

By combining real-time monitoring, automated claim logic, and simplified insurance structures, technology can create more accessible protection systems for workers who rely on daily earnings.

Solutions like this demonstrate how digital tools can support workers and make financial protection more practical for modern work environments.

Looking Forward

Innovations in data analytics, automation, and digital platforms make it possible to rethink how insurance works for emerging economies.

Designing systems that protect workers from sudden disruptions is one step toward building a more resilient gig economy.