Charity Meets Tech: The Fraud-Fighting Platform Built For Trust

In this interview, Kate Lysykh, CEO of Helpster Charity, a global nonprofit redefining how we give, shares how her organisation is harnessing AI and digital infrastructure to detect fraud, build donor trust and deliver life-saving healthcare to underprivileged children and pregnant women across Africa and Asia.

From spotting scams in real time to empowering ethical giving through technology, Kate opens up about the challenges, breakthroughs and lessons that are shaping the future of charity.

 

Fraud By Beneficiaries Isn’t Something Most People Associate With Charities. How Did Helpster Charity First Discover This As A Challenge In Its Operations?

 

You’re absolutely right. When people think about fraud, they picture corporations hiding profits or wealthy individuals dodging taxes. But in our world, the reality is far less expected.

At Helpster, we work directly with hundreds of patients every month, so we’re deeply embedded in local communities. Early on, we realised something unusual: some people were pretending to be poor. We discovered cases where individuals paid to borrow someone else’s home just to appear eligible during a home visit. That was a wake-up call.

Unlike financial fraud, which you can avoid by simply not engaging, this kind of deception is harder to detect as it comes from the very people we’re trying to help. But we don’t rely on home visits alone. We’ve built a multi-layered system, both online and offline, that flags suspicious behaviour across every stakeholder involved, whether it’s a beneficiary, hospital or even a volunteer. This is how we protect donor funds and maintain integrity in everything we do.

 

Many Charities Face Fraud But Few Talk About It Publicly. Why Has Helpster Chosen To Be So Transparent, And How Has That Shaped Your Approach?

 

To be honest, I never fully trusted charities myself until we set up Helpster. In our early days, we got scammed too. But it was our money, not donor funds. That experience forced us to rethink everything. We realised that the same level of due diligence applied to banks and fintech needs to be applied to nonprofits and hospitals.

Now, we run rigorous KYC processes for every hospital we work with. We assess their reputations both online and on the ground. And when it comes to collaborating with other NGOs, we’ve made a firm decision to never transfer money. Instead, we sign MoUs and align on outcomes. That way, we know they’re mission-driven, not money-driven.

Transparency isn’t a nice-to-have for us. It’s a core value. If we want to rebuild public trust in the sector, we can’t shy away from difficult conversations. In fact, we’re now working on a guide to help donors verify charities themselves. Because if we want people to give generously, they need to feel confident about where their money is going.

Let’s Talk Technology. What Specific Systems Or Features Has Helpster Built To Detect And Prevent Fraud In Real Time?

 

Tech is definitely central to how we operate. We’ve integrated AI into our admin panel to assess every new case. It scores each application for fraud risk, urgency and poverty levels. It’s not perfect but it catches subtle red flags that a human might miss, especially when we’re processing hundreds of cases at once.

We’ve also developed an online medical validation system. Multiple volunteer doctors independently review each case to flag inconsistencies in diagnoses, treatment plans or costs. If we spot recurring issues at a hospital, be it inflated pricing or poor medical judgement, we simply stop referring patients there. We always ensure we have alternative facilities in the area.

This combination of AI and peer medical review means our platform doesn’t just filter fraud; it raises the overall standard of care and accountability.

 

Fraud Detection Is One Part Of The Puzzle. How Does Your Model Protect Donors While Still Ensuring Compassion For People In Desperate Need?

 

We’ve designed a unified backend that powers both our fundraising platform and case management. This means that the same system that receives a patient’s application is the one that displays their story to a donor. And once data is entered, it can’t be altered.

Every completed case includes proof of hospital treatment, discharge reports and photo evidence. And if a donor prefers to pay the hospital directly, we make that possible. We’ve done it many times. At the end of the day, it’s not about where the money goes. It’s about how many lives we can save together.

We also publish detailed annual reports with data on admissions, outcomes and spending. For us, tech isn’t just a tool for transparency. It’s a bridge between trust and impact.

 

What Have Been The Biggest Lessons Or Surprises In Designing A Fraud-Resistant Giving Platform In Regions With Complex Healthcare Challenges?

 

One of the biggest surprises? Rumours. In some communities, word of mouth holds more power than documents or data. When we flagged certain individuals or hospitals for misconduct, we suddenly found ourselves the target of defamation, accused of being the fraudsters ourselves. It’s the unfortunate downside of doing anti-fraud work publicly. People don’t always take it well when they’re caught.

Another challenge is corruption. In some areas, religious or community leaders expect payment in exchange for validating a case. That’s disheartening but we stay firm on our principles.

And then there’s the tech barrier. Internet access is limited, devices are scarce and electricity is unreliable. Something as simple as uploading a medical document can take hours. We’re constantly adjusting our systems to work within these constraints but there’s no quick fix as it’s the economic reality on the ground.

 

What Advice Would You Give To Other Social Impact Leaders Trying To Build Trust And Accountability Into Their Platforms From Day One?

 

Start by treating every beneficiary and donor like an ambassador. If they believe in your mission and have a positive experience, they’ll become your loudest advocates.

Be relentless about documentation. Publish annual reports, case updates, impact data even when no one asks for it. That kind of radical transparency builds trust over time. Leverage AI wherever you can. Not just to catch fraud but to ease the operational load and reduce human error. But remember, tech is only as strong as the values that shape it.

Finally, understand that trust doesn’t come from what you say about your organisation. It comes from what others say about you. So build a system that empowers your community to speak up on your behalf.