SafeMatch: An AI-Powered Tool for Safer Online Dating

diksha nasa
Nov 18, 2024
4 min read

Updated: Jan 11, 2025

The Problem: Growing Safety Concerns in Online Dating

Online dating has become a prevalent way to meet people, but it is not without its risks. The dangers range from romance scams to violent crimes, with reports documenting alarming statistics:

A 2023 study by Brigham Young University revealed that 14% of acquaintance rapes occurred during first-time meetings through dating apps, with over a third involving violent acts like strangulation.
Georgia State University research indicated that romance fraud losses soared to nearly $956 million in 2021, making it the third most costly cybercrime globally.

These statistics highlight the urgent need for dating platforms to prioritize user safety—especially for women and vulnerable communities disproportionately affected by harassment and fraud.

The Solution: AI-Driven Safety

Recognizing the growing safety concerns, I developed SafeMatch, an AI-powered tool designed to mitigate risks on dating platforms. Through leveraging AI technologies, SafeMatch detects fraudulent profiles, harmful language, and potential criminal behavior, helping users make informed decisions before engaging with others.

By using technologies like Google Vision API for image verification and Natural Language Processing (NLP) for sentiment analysis, SafeMatch offers a scalable solution to enhance user safety, providing early alerts and empowering users—especially women—to navigate online dating with greater confidence.

User Persona: Understanding the User

To ensure the product addressed real user concerns, I created a persona representing a typical user of SafeMatch: Janice, a 28-year-old frequent user of dating apps who is cautious about encountering fraudulent or dangerous profiles. Janice, like many other users, is looking for more control over her online dating experience, with a particular focus on avoiding scams and criminal behavior.

Janice's primary concerns include:

Catfishing: Encountering fake profiles that could lead to deception or fraud.
Harmful Behavior: Identifying manipulative or abusive language in profiles.
Safety: Knowing that the person she is speaking to is not involved in criminal activities.

SafeMatch was designed with users like Janice in mind, ensuring that it directly addresses these concerns in a user-friendly and actionable way.

Defining the MVP: Prioritizing What Matters

The development process began by identifying key pain points in the online dating experience, particularly the risks associated with fraudulent or dangerous profiles. With these pain points in mind, I focused on creating a product that would directly address user concerns through essential features.

Key features of the MVP were chosen based on their ability to deliver high user value while ensuring the product remained feasible within the initial development phase:

🙀 Catfish Detection: Using Google Vision API, SafeMatch cross-references profile images to detect fraudulent accounts.

🧐 Sentiment Analysis & Red Flag Detection: With NLP, SafeMatch analyzes profile descriptions to flag harmful language, such as hate speech or manipulative behavior.

🦹 Crime-Related Context Integration: SafeMatch leverages Gemini Pro to analyze the context of articles in which a person's image appears, identifying any potential criminal associations and providing users with peace of mind.

Development Roadmap: Execution and Iteration

Phase 1: MVP Development

The MVP was built with a focus on functionality and usability. I utilized React for the frontend and Flask for the backend, ensuring a user-friendly experience that clearly presented risk assessments. The tool was designed with a clear, hierarchical format that made it easy for users to understand the risk levels associated with profiles.

Phase 2: Iteration Based on Testing

Since user feedback was limited to internal testing, I focused on refining the tool by analyzing data and iterating on the algorithms. This allowed for improving detection accuracy, reducing false positives by 30%, and refining the user interface for better clarity.

Phase 3: Scaling and Future Expansion

Looking ahead, the plan includes integrating SafeMatch with major dating platforms through strategic partnerships. Additional crime data sources will be incorporated, and real-time alerts will notify users about high-risk profiles.

Technology Stack: Ensuring Scalability

Google Vision API: Real-time image analysis for detecting catfishing by cross-referencing profile images.
Natural Language Processing (NLP): Analyzes text in profiles to detect harmful language, hate speech, and manipulative behavior.
React: Provides a user-friendly interface that prioritizes clarity and ease of use.
Flask: Efficiently processes data on the backend through lightweight APIs.

Results and Metrics

85% Accuracy in Catfish Detection: By combining image and text analysis, SafeMatch successfully flagged fraudulent profiles with high accuracy.
30% Reduction in False Positives: Through fine-tuning, false positives were minimized, boosting user trust and improving overall accuracy.
40% Improvement in Frontend Usability: The clean, hierarchical design ensured users could easily navigate risk assessments and understand warnings.
1,000+ Profiles Processed: During internal testing, over 1,000 profiles were processed, providing valuable data for product refinement.

Overcoming Challenges

Developing SafeMatch presented several challenges, including:

Feature Scope: Prioritizing the core features without overcomplicating the MVP.
Minimizing False Positives: Ensuring that legitimate profiles weren't mistakenly flagged, while maintaining high detection rates.
Simplifying Risk Data: Presenting complex risk assessments in a format that was easy for users to understand.

Each challenge was met with targeted solutions, ensuring that the product met its goals while delivering a seamless user experience.