Human-in-the-loop trust and safety helps prevent romance scams and platform abuse
Guest: Alice Goguen-Hunsberger
Let’s break this down.
In this episode, I’m talking with Alice Goguen-Hunsberger, Vice President of Trust & Safety and Content Moderation at PartnerHero. Alice has spent years building trust and safety strategies for platforms that deal with massive volumes of user-generated content, including some of the most well-known online dating communities.
And if you’ve worked anywhere near fraud, trust and safety, or digital platforms, you already know the problem we’re talking about here.
AI moderation is powerful. It can scale quickly. It can detect patterns across enormous data sets. But when platforms rely on automation alone, things start to break down.
Because fraudsters, scammers, and bad actors tend to adapt faster than automated systems.
In this conversation, Alice and I dig into what human-in-the-loop trust and safety actually looks like in practice, especially in industries where communities, relationships, and identity are central to the product experience.
At first glance, content moderation might look like a customer support issue.
But when you dig in, it’s directly tied to fraud prevention, platform trust, and the safety of real people using these communities every day.
Here is what that human-in-the-loop trust and safety approach means in practice:
- combining AI content moderation with trained human reviewers
- recognizing early warning signals of romance scams and manipulation
- designing trust and safety frameworks that scale with community growth
- understanding how customer support failures can enable fraud
What you’ll hear in this episode:
- Why dating platforms and gaming communities face unique trust and safety risks
- How AI content moderation works and where it still falls short
- Why human review remains critical in high-risk moderation decisions
- How romance scam detection intersects with trust and safety operations
- The surprising connection between poor customer support and fraud risk
You should listen to this episode if you:
- lead trust and safety, fraud, or risk teams for online communities
- manage moderation systems for user-generated content platforms
- operate dating apps, gaming platforms, or digital communities
- want to understand how AI and human judgment work together in moderation
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Episode notes & key takeaways
This episode explores the operational realities of human-in-the-loop trust and safety, especially for platforms managing large communities built on user-generated content.
Alice brings deep experience from the online dating and community safety space, and the conversation highlights the risks that emerge when platforms scale faster than their trust and safety programs.
Because here’s the thing.
Fraud doesn’t just appear in financial systems. It often starts inside communities.
Why human-in-the-loop trust and safety matters for online platforms
AI-assisted moderation has dramatically improved the ability of platforms to detect abuse, scams, and harmful content at scale.
But automation alone rarely solves the problem.
The key thing to understand is that many fraud patterns involve context, behavioral nuance, and subtle manipulation tactics that automated systems struggle to interpret.
Human-in-the-loop trust and safety systems combine automated detection with trained reviewers who can evaluate edge cases and suspicious behavior that may not trigger obvious machine signals.
Operational considerations may include:
- escalation pathways for high-risk moderation decisions
- human review for suspected romance scam interactions
- cross-team collaboration between fraud and trust and safety teams
- behavioral monitoring across user-generated content environments
Romance scam detection in dating platforms and online communities
Dating apps are a prime environment for social engineering attacks.
Fraudsters build relationships, establish emotional trust, and then manipulate victims into sending money or personal information.
And that’s why trust and safety teams play such an important role in fraud prevention.
Romance scam detection often requires evaluating patterns that go beyond transactional data.
Operational indicators may include:
- behavioral signals tied to rapid relationship escalation
- suspicious messaging patterns or scripted interactions
- network analysis identifying scammer infrastructure
- cross-signal correlation between accounts targeting multiple victims
How bad customer support practices can increase fraud risk
One of the more interesting ideas we discuss in this episode connects customer support failures with fraud vulnerability.
When customers cannot reach support teams quickly or easily, they often turn to search engines, social media, or unofficial support channels.
And this is where fraudsters step in.
Scammers frequently impersonate banks, companies, or platform representatives, taking advantage of frustrated users who are trying to get help.
Operational considerations may include:
- stronger identity verification within customer support interactions
- monitoring impersonation scams targeting frustrated users
- cross-signal correlation between support complaints and fraud activity
- proactive communication strategies to prevent impersonation attacks
Startup trust and safety mistakes that enable abuse
Another theme Alice highlights is a common mistake made by many startups.
Assuming good intent.
Early-stage platforms often focus on growth and engagement without building the infrastructure needed to manage abuse, scams, or harmful behavior.
And that creates opportunities for attackers.
Operational indicators may include:
- delayed moderation controls during rapid platform growth
- lack of escalation processes for abuse reports
- insufficient fraud monitoring tied to community behavior
- weak governance around user-generated content environments
In simple terms, human-in-the-loop trust and safety isn’t just about moderation.
It’s about protecting communities.
Platforms that combine automation with experienced human judgment are far better positioned to detect fraud patterns, protect their users, and maintain long-term trust.
And honestly, that’s what good trust and safety strategy looks like.
