Every Fraud Case
Tells a Human Story

Kavilashini, you are a fraud investigator — and you are quite young. This is an unusual career choice, particularly for a woman in Malaysia. What was the attraction?

I am a very curious person. That is where everything starts for me. The deep interest begins with curiosity — first, it was simply: what happened? And then it developed into something deeper: why are people doing this?

Because there is always a reason. Nobody commits fraud in a vacuum. And when you spend your days reviewing cases, you realise very quickly that each case has a human story behind it. The numbers on the screen, the flagged accounts, the suspicious GPS patterns — they all trace back to a person who made a decision. And understanding why they made that decision is actually the most interesting part of the work.

And from day one in this work, what was it actually like?

Every day, I am reviewing cases. And every single case tells me something different. The reasons people commit fraud — sometimes they seem trivial to us from the outside. But to the person doing it, it was significant. It was something they felt they had to do. That perspective — holding the human side of what looks like a dry data case — is actually what keeps the work meaningful for me.

Take us through the fundamentals. You mentioned the fraud triangle — what is it, and why does it matter?

The fraud triangle is the foundational model of fraud. Three components — pressure, opportunity, justification. They work together in sequence.

It begins with pressure. The person has a motive — a financial need, a target they want to reach, a problem they cannot solve through legitimate means. That pressure creates the intent. Once the intent exists, they look for opportunity: the gap in the system, the moment where the controls are weak, the way in which they can act without being caught. And then, once the fraud is done, they find justification — a story they tell themselves about why what they did was actually reasonable, or necessary, or not really their fault.

That third part — justification — is psychologically fascinating. Because almost universally, when we confront someone with what they have done, the first response is denial and justification. They will not immediately admit it. They will explain, contextualise, find reasons. And it is only when they understand that the evidence is definitive that the admission comes. But even then, the justification persists alongside the admission: yes, I did it — but here is why.

Walk us through how fake demand and fake supply actually works in a logistics platform. Because I think most people have used these apps but have no idea this is happening.

In a logistics platform, the two primary parties are the user — who places the order — and the driver — who fulfils it. Fake demand and fake supply happens when the same person controls both accounts. They use the first account as the user to create a fake order. Then they switch to the driver account and accept that order. The acceptance happens within seconds, which is already a flag — in real usage, order matching takes longer. Then the driver stays static. They do not go to the pickup location. They do not go to the delivery location. After a few minutes, they swipe complete. The system records a fulfilled delivery. Money moves to the driver wallet. The order was entirely fabricated.

And for this, they are willing to commit fraud for amounts that are sometimes less than ten ringgit. That is what surprised me most when I started. The scale of risk — account ban, police report, a criminal record — taken for a gain that is essentially nothing significant.

Would these people not be smarter about how they do it?

This is the key insight — fraud is not primarily about intelligence. It is about psychology. They genuinely believe they will not be caught. They think the system cannot see what they are doing. But we can. Every device has a fingerprint. We can link accounts by the device that was used to log in, by phone number, by bank account number. We can check GPS logs to see whether the driver actually moved to the pickup and drop-off locations. We can review order history going back five or six years and identify patterns — the same user appearing with the same driver repeatedly, orders completed in seconds, static GPS throughout a supposedly active delivery.

The fraud leaves traces everywhere. The fraudster just does not know how many traces are being read.

And when you detect it, what is the process?

The automated system flags the account. Then fraud investigators come in for manual review. We look at three things: the genuineness of the order history, whether there are linkages between the user and driver accounts, and whether the GPS evidence shows the driver actually completed the physical journey. If we find fraud, we assess severity. If it rises to the level of a crime, we lodge a police report. For lesser offences, we partially ban the account first — the driver cannot see or accept orders, but they do not initially know why. They contact support. We then educate them about what was detected. If they acknowledge it and it is a first offence, they may get a second chance. Serious offences get permanent bans.

You mentioned reactive enforcement as a problem. Explain that for us.

Reactive enforcement means the response to detected fraud is: we will silence your account for two days, then you can operate again. And the problem is obvious. Two days off is not a deterrent. For most people committing this kind of fraud, two days is essentially a short holiday. They return after two days and do the same thing. Nothing has changed. The behaviour has not been addressed. The system has not been tightened. The person has not faced real consequence.

The enforcement response needs to be proportionate to severity and — critically — it needs to be proactive rather than reactive. That means tightening the detection systems so fraud is caught earlier, before significant gain has been made. It means applying meaningful consequences for first offences rather than treating them as minor. And it means learning from each detected case to close the gap that made that fraud possible in the first place. Most systems are still too reactive. The investigation catches what the system missed. But the real goal is to make the miss less likely to begin with.

You also lecture. What do you see in the current generation of students compared to when you were studying?

They are significantly more curious. When I was a student, lecturers talked and we listened. The current generation asks questions — they want to connect what they are learning to what is actually happening in the world. I adapted to that quite quickly. I always relate the theory to current situations, give real examples. And their response is engaged: yes, I saw this happen, and does it relate to this concept? They are sharper. They push you to stay current yourself.

And where are you taking your investigation career next?

FinTech and banking fraud. That is where I want to go next. What drives me is still that fundamental curiosity — why do people do this? In logistics fraud, the answer is usually money, in relatively small amounts. But in FinTech — hacking, scams, digital banking fraud — I want to understand what makes people do this in that context. Some of it is money at larger scale. Some of it, I have learned, is actually about power, or about proving capability. People commit some of these crimes just to show they can. The psychology is different, and that is exactly what pulls me toward it.

My final question — if you had one wish to make the planet a better place, what would it be?

We cannot prevent fraud completely. I know that. But my wish would be to reduce it significantly. Because in every fraud case, there is a gain and there is a loss. One person gets something. Another person — or many people — lose something. And I always feel for the person who is losing. They trusted the system. They trusted that what they paid for would be delivered. And someone took advantage of that trust.

If I could reduce that — reduce the number of people who lose to fraud, reduce the amount that trust is violated every day — that would be what I would wish for. Fewer people losing. Fewer people going through that experience of being cheated.

Thank you so much, Kavilashini. It has been a real education — and we will have you back for part two when we go into FinTech crime. That is a wrap on Episode Eight of The Centered Edge.