Episode 1: Fighting Courier Fraud in Cash-on-Delivery Markets

Cash on delivery (COD) is still the dominant payment method in many emerging markets—but with it comes operational complexity and high fraud risk. 

In this episode, we chat with Faisal Ahmad Jafri, who’s worked for platforms like Glovo, Foodpanda, and Alibaba, and has led teams across Africa and Asia. He breaks down the challenges of courier collusion, proof of delivery loopholes, and the trade-offs between fraud prevention and platform growth.

Key discussion topics include:

• How courier fraud in COD markets differs from fraud in digital-first markets
• Why detection is so difficult when proof of delivery is weak
• The importance of collaboration across fraud, ops, and support teams
• Signals and behaviors that reveal courier fraud
• Field-tested interventions that worked (and those that didn’t)
• How fraud strategies need to adapt when expanding into cash-heavy markets
• Emerging fraud models with QR codes and mobile wallets

Show notes:

• Connect with Faisal: https://www.linkedin.com/in/faisalahmadj/ 
• Connect with David: https://www.linkedin.com/in/david-nesbitt/ 

About Fraud on the Go Podcast

Fraud on the Go is the podcast for fraud fighters on the frontlines of the gig economy. Through candid conversations with experts, we explore how delivery, ride sharing, and marketplace platforms are fighting fraud and risk at scale, and what it takes to stay ahead.

Episode Transcript

David: Welcome to Fraud on the Go. I'm your host, David Nesbitt, and today we're going to be tackling courier fraud in cash-on-delivery markets.

My guest is Faisal Ahmad Jafri. He is a seasoned global operations leader who has led marketplace ops teams across Africa and Asia. He’s led key leadership roles at Glovo and Foodpanda and currently serves as the Director of PMO and Strategic Planning at Alibaba Group. 

Faisal has deep expertise in last-mile logistics, courier performance, and fraud prevention in cash-first markets. Together on this podcast episode, we're going to unpack what makes courier fraud so hard to detect in cash-on-delivery markets and how platforms can fight back.

Faisal, thanks for joining me today.

Faisal: Thank you for having me.

David: Absolutely. So to start us off, could you just share a bit about some of the markets that you’ve worked in where cash is still dominant and how you got involved in courier fraud specifically?

Faisal: Thank you—happy to tell you about that. So basically, I’ve worked across cash-heavy markets in Asia and Africa with platforms like Glovo, Foodpanda, and right now with Alibaba, as you said.

In comparison to digital markets like North America and Europe, in many of the Asian and African places, cash still drives the majority of orders—even in the urban hubs as well. And the fraud patterns are often entirely different as opposed to Europe or America.

What kind of pulled me into the courier fraud was very simple—because that's where the money leads. So it’s not fake users or stolen cards. It's actually real people and real orders and cash that quietly disappears at the doorstep of the customer. And because it's human and behavioral—and it's not just digital—you need ops, you need anti-fraud teams, and you need on-ground field insights working together to fight it. And this was the challenge that fascinated me and it continues to do so.

David: That’s great. So let’s jump right into the questions. Our goal today is to get some interesting insights for folks that are going to be listening in—things they might apply on their own platforms. 

So first question for you—I want to first talk about the nature of the risk, the problem. How does courier fraud in cash-on-delivery markets differ from fraud in digital payment environments?

Faisal: Actually, in digital environments, fraud often focuses on account takeovers, fake users, bots, etc. But in cash-on-delivery markets, which is known as COD, the fraud is actually pretty operational. It lives in the last-mile part of the delivery.

So you are dealing with real users, but the fraud sits in how the money is moving and the hands that are moving the money. Couriers can cancel real orders at the last moment. They can falsely mark deliveries that aren't delivered—they have delivered the order but would say they have not delivered. And they have the potential to collude with customers to pocket the cash.

Essentially, it is the analog nature of the COD that makes the tracking and accountability far harder than the digital or the card-based orders.

David: That’s interesting—you mentioned the problem’s operational. Interesting to dig into that more as we continue through these questions.

My next question: What makes courier-side fraud particularly difficult to detect when cash is involved? So talk to me about the detection challenge.

Faisal: Okay, the real reason is that the proof of delivery is actually weak. So essentially, it is relying on the courier—or someone who is delivering the order, the driver, etc.—to mark a particular button on the driver’s app that the order has been delivered.

So essentially what it means is that there is no digital trace once the money is exchanged. And because of this sort of loophole or weakness, the couriers often exploit the blind spots that exist in the app or in the process—particularly when it comes to manual cancellations, when someone has to trigger the call to cancel an order.

They also exploit vague reasons when it comes to the customer side of things. Also, what we see is that there is a lot of contextual manipulation, which means they would do staged deliveries, fake attempts, route detours, and also time tricks accordingly, which makes the overall data look legitimate—however, there is ongoing fraud happening.

So unlike card payments, there is no bank trail to audit as well, which complicates the issue even further.

David: Yeah, that’s interesting. The lack of digital signature—I mean, it’s very different. We’re talking about platforms that are purely operating in a digital world. All the signals that they're able to have throughout the entire process—they’ll be very different when you’re transitioning at some point to an off-the-platform money-exchanges-hands part of the process.

So yeah, in that kind of complex process, where you've got that additional complexity introduced by going off the platform to do something—what kind of collaboration is important to have between fraud, ops, customer, and driver support to manage this kind of cash abuse?

Faisal: So I’m glad that you asked this question because this is a very critical question. And the answer is that you need a very closely knitted feedback loop between these three functions that you mentioned.

What happens is that the anti-fraud teams detect patterns and create blacklists, graylists, and whitelists. Then the on-ground ops redesigns processes to close certain loopholes that existed. Then the driver or courier support team brings real-time insights from the ground.

And when these three teams sync, they catch fraud early and identify potential false positives that might be blocking couriers and drivers—and therefore ensure that no party is getting impacted too much.

On the contrast, if these departments are working in silos, the abuse will scale quickly—and there will be no check on it as well.

David: Yeah, so that makes sense. I’m sure you've probably seen examples of both—where things are moving well together and when they're not together.

Are there any signs that would show you that it's not working properly and needs to be adjusted, in terms of those teams working across silos with each other?

Faisal: One of the things that we did when I was part of one of the organizations is that we created a model called the “true cost of experience.” So an example is that if you tighten fraud controls a lot—but at the same time, if that means that a lot of couriers are disabled from the platform or certain customers that are potentially genuine are not able to place orders—it means that while you are tightening the noose on fraud, on the other hand, you are creating blockers to the growth of the couriers on the platform or to the growth of the customers on the platform, which is actually counterintuitive to what you want to do.

David: Yeah, it’s kind of the classic balancing act you’re always trying to play between keeping your platform clean, free of abuse, but also making sure you’re not tightening the belt on good users and making the experience worse for them, whether it’s a courier, consumer, or diner, etc.

So that’s great. Thanks for the insight into the problem.

Let’s move next to talking a bit more deeply about detection policy and onboarding—how you address this challenge, get ahead of it. And let’s start with a question about signals.

What kind of signals or behaviors are helpful for catching courier fraud in cash-on-delivery flows?

Faisal: Okay. I think at a very high level, I would talk about high-value order cancellations—they are a big signal. Route manipulation by couriers. Frequent delivery to the same clusters or the same customer locations. Then late-night activity outside the zones and catch.

Also, for example, one of the things we noticed when I was part of one of the food tech companies was that certain couriers were very habitual of canceling their last orders on their shift. Basically, they were taking the free meal at the end of the shift.

So these are some of the signals, as you mentioned. It’s also very, very important that location behavior is measured and overlaid on these areas where you’re seeing wastage of cash happening, so that you can detect and identify outliers.

David: When you're detecting signals that show a warning sign of cash-on-delivery fraud, would you ever surface those flags to couriers to try to shape their behavior? Is that productive to do that, have you found in your experience?

Faisal: I think what you need to do is monitor it for a certain period of time. You cannot make quick decisions. If you see that the anomaly is kind of confirmed—and you also try to get some on-ground insight as I said earlier—and if those corroborate, then I would definitely take action.

David: Thanks, that’s great insight. I’m curious to hear about region-specific policy changes—any specific examples you could share?

And also, it'd be interesting to hear both any policy changes that were specific to a region, as well as how did you test and evaluate those changes once you made them?

Faisal: Okay, so whenever you implement any change, you have some end result in mind. So as long as you are achieving the end result or the success metrics, I think that is the criteria to ascertain whether the changes were successful or not.

With regards to specific policy changes in a region or in a market, some of the changes that I implemented were:

• We introduced proof of delivery for COD for certain types of orders.
• We introduced dynamic cash limits based on courier profiles.
• We also did randomized audits.
• And we implemented a chunk of customers that were called at the end of the day, post-delivery, on a daily basis. It was a random list of customers that we were calling.

What happens in terms of A/B testing is that when you do A/B testing with control zones to evaluate effectiveness, you get the real picture of what is working and what is not working.

In all the three tech companies that I have been part of, calling customers, for example, has done wonders because it gives you quick insights into what is happening on the ground. Customers tell you. So trust me on that.

And anyone who's sort of listening to us—if you are curious and you are maybe uncertain what's happening, why these potential cancellations or cash mismatches are happening—just give a call to a random customer. Five, ten customers. And they will exactly tell you what was happening.

Also, I want to mention that tiered trust models—when it comes to be it your customers or your partners, like your sellers and your vendors, or even couriers—if you kind of develop those trust models, they will drive some of the best results for you.

David: So when you're calling a customer to check in, what are you asking them? Like, what's that conversation? How does it look like?

Faisal: It is a very, very usual conversation. So, for example:

“Hey, I'm Faisal. I'm calling from Incognia, and you ordered this thing from us. Was the order delivered to you? What happened? Was it okay? Did the courier come to your doorstep? Was the order canceled? Did the courier say anything to you? Did he ask you to not accept the order, accept the order?”

So just ask these questions just like as if you are trying to get post-delivery feedback from them. And within those answers that the customers will give to you, you will unearth some of the fraud—if it was kind of an identified fraud delivery which came up in your checks, etc.

David: That’s cool. I’ve actually never heard that. I’ve heard a lot of fraud prevention topics within this industry—I’ve never heard of that kind of specific approach of calling customers directly to get an insight. I think it’s really interesting. It’s like a manual intervention that could get you some really helpful insight.

Faisal: It works. It works. It’s like a small focus group, you know, where you actually talk to the real customer, get the insights, and design your policies around that. And as I mentioned, I have done this in three different companies, and it works wonders.

David: That’s great. I love the concept of a focus group like that.

David: Okay, great. Let’s move on to talking a little bit about maybe getting ahead of the problem a little bit. Should platforms—do you think, in your opinion—should they take a different approach to onboarding, education, or compliance in markets where cash is the norm, or cash is being used regularly?

Faisal: I think absolutely. The approach to onboarding, education, and compliance in cash-first markets versus card markets is going to be pretty different.

We need to have structured onboarding with mock deliveries, fraud scenario trainings, and specific compliance enforcement as well. A copy-pasting card market flow wouldn’t work in cash-first environments.

I have seen in certain cash-dominated countries, there are certain geographies within those countries that are more prone to cash fraud—as opposed to certain geographies which are less prone to cash fraud. So you need to have certain customized solutions when it comes to compliance, particularly.

David: That’s great. We’re getting into some really practical insights. I love that. I think this would be really useful for everyone listening.

And I want to move next into talking about specifically collusion—and kind of the iteration process as well of when you're rolling out controls, policies, and you're kind of changing those over time. What does that look like?

So let’s start by talking about the collusion piece first. What have you seen in terms of collusion between consumers and couriers in cash transactions?

Faisal: One of the things that I would like to mention is that the collusion between consumers and couriers is pretty real. It is really, really happening on ground—especially in family or friend networks, it kind of happens predominantly.

What I’ve seen is that couriers repeatedly serve the same customers or the same sellers—or vendors, for that matter. Customers reject deliveries to get refunds while keeping the product. And in that sort of fraud activity, the couriers also get a share.

So there’s a customer who would, say, be ordering a grocery order—a carton of milk, for example. And they would make a fraudulent claim that “I was not delivered the item I was supposed to get,” and they will get a refund. And from the refund that the customer got, they will give a cut to the courier, and the remaining will be kept by the customer.

So the problem of consumer and courier collusion is very real when it comes to cash products.

Because I want to stress further on that in digital markets, there is little interaction many times between the consumer and the courier. Because you would place the order somewhere, at the drop-off location, provide the POD, and leave.

But when it comes to consumer and courier relationships in a cash-first market, oftentimes a courier is at your doorstep, a consumer would come, they would then collect a particular item, and then go inside, bring in the cash, etc.

So therefore, the overall interaction time that is there between the consumer and the courier increases—thereby increasing the chances of them having conversation. And if they both have kind of devil’s minds, then they will interact and lead to collusion for fraud.

David: Yeah, it makes sense. I mean, the truth of the window of interaction, like you said—I mean, in the United States, there’ve been times where it’s almost weird if I see the delivery driver. I think people try to almost avoid it because it’s like, yeah, they’re going to leave it at my door.

If I open the door, neither of us is going to know what to do. It’s like, well, just have them leave it outside the door. Everything’s taken care of through the app. Versus yeah, if you’re actually having to interact and exchange money—hand over the actual delivery in person—the window for that kind of interaction to turn into some of that collusion is, you're right, a lot bigger.

Faisal: I’ll give you another example, and then I’ll put the numbers exactly.

So in general, the time that a courier spends at a customer for a prepaid order versus for a postpaid order—there’s about two minutes difference. That is a study that I have done, and across many companies that I’ve worked for, always a cash-on-delivery order would mean that the courier is spending at least two to three minutes more at the customer location, as opposed to a prepaid order.

So, you know, this is the real difference—the additional two to three minutes that the courier is at the customer location. Within that window, if they develop a friendship, they can do that.

David: Yeah, that’s fascinating to have an actual number on it. Two to three minutes. I mean, operationally, it's interesting. We're not talking about that even, but how much of a difference that introduces in terms of timing and supply.

Faisal: It does. But then also for fraud specifically, we're talking about a huge difference.

David: Yeah.

Faisal: Yeah, and collusion is something that we're hearing about globally a lot. So I think it's interesting that in cash-on-delivery markets, it's particularly prevalent. And then even just in general on all markets, I feel like we're hearing a lot about collusion. And it feels like this topic is picking up speed.

Almost like a year ago, if we talked about this, or two years ago, there'd be fewer people maybe following along closely, or it wouldn't be as popular of a topic. But now it seems like this is more prevalent on the minds of fraud and risk leaders in this space, as it maybe is just becoming more prevalent—perhaps as they crack down on other types of fraud.

Faisal: I'll add on this. The bigger reason is that when—so let's say there's a restaurant partner that wants to do fraud, and the reason why he asks a particular courier to join him is that a courier is able to give him more insights about the business model and the policies a particular platform has implemented.

So when the two minds kind of join together, they are able to come up with more sort of detection—a model which—they're able to design a fraud mechanism or a modus operandi or fraud which has a less likelihood of getting detected as opposed to someone who is just working in isolation. So that's the reason also, yeah.

So you know, take what you know, let's combine it with what I know, we've got a fuller picture now of what's happening on the platform, and also like what kind of controls there are.

David: So, okay, we've talked about—clearly collusion is a problem and a prevalent problem, and there's a big opportunity for it in a cash-on-delivery system.

What interventions have you seen that have reduced or disrupted collusion behavior?

Faisal: Yeah, so I think some of the major things that I've seen are that we need to ensure that there is a mechanism whereby a courier is assigned not to just one customer or two customers again and again. So you need to rotate customers and courier assignment and the relationship between them.

You need to make randomized positive recalls, as I said.

You need to do shadow routing for flagged accounts as well.

What is essentially needed is that you combine subtle monitoring with visible deterrence.

In this way, you can disrupt collusion behavior.

David: Got it. Okay. You know, let's talk about kind of once you've put in place some of those controls. I'm curious—have you tested any controls in cash markets that you ended up either rolling back—like we found out this wasn't successful, this wasn't the right approach—or expanding—you know, we doubled down on this because we saw it was working well. Curious to hear about any examples you have.

Faisal: Yes. So, you know, everything that you do in life, some of the things work and some of the things do not work. So we have had a fair share of both—our sort of failures and rolling back things, and also rolling out and scaling some fraud control techniques as well.

So I'll start with the proof of delivery, which was a success basically. The example that I'm sharing is basically—we expanded the photo proof of delivery for COD orders. It was initially piloted in a few zones which we thought were high risk, and it proved to be highly effective.

The couriers over there were likely to falsify deliveries. When they knew that a photo was basically being timestamped and it was being required, we were kind of putting frictions to their fraudulent attempt. And therefore, because it was adding friction, and the couriers knew that they can't game with us anymore, it worked.

And we eventually rolled it out across multiple other higher-risk regions with fraud rates—or you know, in food tech, usually the KPI is called CPO. So the fraud CPO dropping more significantly—that was a success story.

On the flip side, if I talk about something that we had to roll back, it was basically related to blanket COD restrictions that blocked certain areas or certain user segments entirely. The overall intention was to basically reduce fraud.

But what actually happened was that it triggered a spike in customer complaints on the contact center. And also, let's say if you're being too tight on the courier side of the things, it made couriers less available. And the supply being less available in peak ordering time of orders—like say, dinner hours—which led to legitimate cancellations and also support tickets inbounds increasing: "That one order was canceled and then I need a refund."

There was a lot of noise and commotion. And also, you know, as I said earlier, that if you are blocking customers or if you're blocking couriers too much, then you are actually kind of tightening the growth. And every food tech or every ride-hailing company right now wants growth.

So if you're going to sort of stifle growth, then you are actually setting yourself up for disaster.

So eventually, what it was doing was creating more friction than the fraud prevention that it was supposed to bring.

Some of the key lessons that we got from all of this process is that you need to be highly, highly precise and accurate—rather than having a sort of a restrictive mindset of being always afraid of fraud and, you know, "fraud is happening, let's just block everything."

It cannot work like that.

What you need to have is a structure where you bring in targeted interventions. You develop courier trust tiers.

This is like a tier-one courier. This is like a tier-two courier. So a tier-one courier is a long-tenured courier or a long-tenured driver on the platform. He is an exceptional courier—doesn't do any cancellations, etc., and always, you know, would deliver orders on time or would complete his rides on time, etc. So you know, those are tier one.

So you can create certain tiers, and then based on that you can create cash limits for those couriers.

And you also need real-time monitoring for certain anomalies that are occurring. So you need to have a sort of flagging mechanism. Because what happens is that the real-time monitoring will yield much better results than having broad and blanket bans.

David: Yeah, related to the broad blanket bans, I think I've heard people say before, like, you could have zero fraud—but it means you're going to have zero business overall on the platform. That's the only way to get there. So you can tighten things all the way.

Faisal: I'll tell you—whenever, like in the past, when I have been directly involved in controlling fraud and designing interventions, what we have done—even if we are coming up with a rule—we would first run it into a shadow mode and see how much sort of GMV are we blocking. The incoming GMV.

So if we would set a criteria that, okay, this rule is not blocking a significant amount of GMV, which means that we are not curtailing on the growth. If anything that is curtailing on the growth but kind of stopping fraud, I think it is counterintuitive.

David: Yeah, that makes sense. You know, whatever area of fraud and risk you're working in, that’s something you always have to keep in mind is like, what’s the bigger business context here? What’s our goal?

It's like with, you know, banning drivers for bad behavior—always keeping in mind the supply issue. Like, well, if we ban all of our drivers, or if we ban half of them or a third of them, what kind of problems is that going to create? Do we have enough supply to actually, you know, serve our customers?

Faisal: I'll give you a real example. So Christmas, for example, over the year-end and the year-end period—that's a time of high demand, and that's a time of less supply as well. Be it on the food tech front or the ride-hailing front, because the people would want to go with their families on vacations, etc.

And that is the time where the ops team—the rider and/or the driver support team—and the anti-fraud team, they need to come up with a model which is not sort of cannibalizing growth, but also is not too relaxed on fraud.

And I have been part of these scenarios whereby—be it Christmas, New Year's Eve, or Eid for that matter, or Chinese New Year for that matter—you have to come up with customized solutions, because you have a real crunch of supply, and at the same time your demand is huge.

David: Yeah, that makes sense. Okay, let's keep moving. We're going to talk in this next section about kind of scaling this strategy—some of the strategies we've been talking about for dealing with fraud and cash-on-delivery.

So one question—maybe think about it like, let’s say that I'm a platform in North America who's going to be expanding to a cash-centric economy, like Latin America or something in Asia. How do fraud strategies need to adapt between those cash-centric economies and digital-first markets?

What do I need to be thinking about changing in my strategy as I make that expansion to a new market?

Faisal: Yeah, so basically, very good question. And I think what we need to understand is that markets like North America or Europe, where most of the orders are based by credit cards—in the digital-first economy, the fraud is pretty synthetic.

So it's done by bots. It's done by scripts. They would hack your credit card, and then you might see a barrage of chargebacks coming, etc.

But when it comes to cash markets like LATAM, or Asia, or Africa for that matter—cash markets are basically very behavioral and operational. So the fraud is latent in the behavior and the operational processes of the market.

While the digital markets would focus on identity and authorizations, etc.—preventing account takeovers—the cash market would need route tracking, ensuring that the order was really delivered through PODs and other interventions, and making foolproof field processes.

David: Not slipping my mind in terms of the scale. So I'm curious—with that particular example, you know, interesting to hear about that. And definitely, you know, the scale across three markets is interesting. Do you have any sense of how many individuals were involved in that ring?

Faisal: Yeah, I think it would be a drop-off—at least 40 to 50 odd people, easily.

David: And I don't know if you can share this specifically, but what kind of—like, were they organizing through Telegram? What kind of things would they do through a platform like that?

Faisal: So basically, what they were doing is that they were sharing the modus operandi of how to seek fraudulent refunds or how to make cancellations—what to say to the particular contact center when they call you, etc. And they were telling all the other couriers what you should do.

So let's assume it—or believe it—in this way: there were four to five sort of... 

David: The mastermind?

Faisal: Yeah, the mastermind. Exactly—that was the word I was looking for, thank you for that.

So they were the masterminds, and then the remaining—so let's say if it's a group of 50—five people were masterminds and the remaining 45 people were actually executing it on their instructions.

David: That makes sense. Yeah, it sounds like some other schemes that we've heard. You know, it's either they're actually turning it into a full service where you just go on and you order something and they're doing everything in the background—or maybe sometimes they're doing both: selling the guides, like, "here's how we do it."

Faisal: Yeah, exactly. A couple of revenue streams there.

David: Okay, great. Yeah. Interesting to hear that example, for sure. And eye-opening to think about that many individuals networking together against your platform. That’s definitely a super tough challenge to fight.

I've got one more question for you. As new payment mechanisms are getting added in cash markets—think of things like QR codes or new mobile wallets—what emerging fraud models do you anticipate? And how can platforms get ready in advance for that?

Faisal: Good question. I think what one should expect is that there's going to be a lot of cross-channel fraud. I've seen that.

I would love to give this example and explain that, but it would kind of diverge some confidential information, so I would refrain from doing that. But I think expect cross-channel fraud is going to increase a lot.

You're going to have fake reversals. Off-platform payments are going to increase more. Account hopping will happen—from one mobile wallet to another mobile wallet, etc. That’s going to happen.

What is my advice to platforms? Prepare via real-time wallet API checks. Let's have a unified fraud scoring mechanism—just like you have your credit score in the U.S., a unified credit score—you can also think of developing a unified fraud scoring mechanism.

And let's build something tied to location—so location-type confirmations are going to help in controlling these new frauds related to new payment mechanisms.

David: Thanks, Faisal. So I've got one more—actually kind of bonus—question for you.

As we think about the whole conversation and the whole challenge we just talked through—cash-on-delivery markets—if you could make one change, redesign one part of the cash-on-delivery experience to reduce courier fraud specifically, where would you start?

Faisal: Great question. I think this is a question that I have always thought of. I'm glad that you asked this.

I think I would start with the proof of delivery thing. I think the button that is there on the courier app or on the rider’s app—where they mark the order as delivered or the ride as completed, right? You get the sense of it.

Usually, in the app—the driver’s app—it is just a button which can be easily manipulated. It can be easily clicked, especially in the cash flows.

If it was possible, I would kind of redesign the proof-of-delivery button to be tamper-proof, timestamped, location-verified—and potentially should have, ideally, a silent SDK in the courier's app that kind of captures all this information. You know, just like you capture metadata when you're taking a picture or something like that—getting as much information as you can when the courier is actually pressing the delivery button.

I think that one shift is going to make a lot of difference.

Secondly, tying cash collection to verified delivery data—I think that would also close a massive blind spot that exists right now on the ride-hailing and food tech side of things.

I believe it's not about adding friction, as I said earlier. Because if you add too much friction, it’s going to basically stifle your growth—and I would not want that.

But what I'm actually sort of vouching for is to add certainty—that an order was really delivered, and it was at the right location, delivered in the right circumstances.

And, you know, when the couriers would know that the system can easily check and validate what's happening—and that when and where the delivery happened or not happened—the courier would get kind of a bit worried. Scared, if that's the right word to use. And the fraud would drop very, very swiftly.

David: Well, we'll see. Maybe something will be integrated like that in the future, and we’ll see things get cleaned up on the cash-on-delivery fraud side.

But for now—Faisal, thanks a ton for having the conversation with me. We've been interacting for a while now on different topics, we've gotten to collaborate a couple times now, and so I really appreciate all the practical insight you shared.

I feel like we covered a lot of really nitty-gritty details that could really help people dealing with some of the problems out there. And it’s clear that you've lived these challenges—this is something you've really thought through, brainstormed on, and considered how it could improve.

So really appreciate you joining me for this episode.

Faisal: Thank you for having me. And I'd just love to tell our viewers and listeners how we have navigated fraud in complex markets. And if they can learn from us and sort of prevent fraud, I would be very happy to know.

David: That’s great. If anyone listening wants to reach out to you, what's a good way for them to connect with you?

Faisal: Well, they can reach out to me via LinkedIn, and they can also send me an email. My email address is also on my LinkedIn, but I can also voice it over: it's faisalahmadj@gmail.com.

David: That’s great. Thanks, Faisal.

And yeah—to our listeners, encourage you to connect with Faisal on LinkedIn at least, and reach out to him if you have more questions or want to talk through this.

And don't forget to subscribe to Fraud on the Go, leave a review, share it with other colleagues. Our goal is that this is a really useful resource for the industry.

In this episode particularly—maybe send it to someone who's navigating fraud in complex cash-on-delivery type of markets.

We’ll see you next time. Thanks for joining.