Fraud Technology Podcast

Episode 17: Leveraging Technology and Data in Fraud Prevention

• Ravi Madavaram • Season 1 • Episode 17

Share episode

In this podcast episode, Chris Danese, a fraud prevention expert, reflects on the shift from instinctive, face-to-face fraud detection in law enforcement to data-driven strategies in a call center environment. The discussion covers the evolution of "bust-out" fraud, where consumers exploit full credit lines, and how the industry has adapted over the years. The expert shares experiences in developing payment monitoring systems and the importance of staying current with new technologies, such as open banking and generative AI, to remain effective in the field. The conversation highlights the need for personalized fraud prevention strategies tailored to individual customers, moving away from broad, one-size-fits-all approaches. The episode also explores the intellectual challenge and excitement of using data to detect and combat fraud, emphasizing the shift from instinct to analytics.

Ravi Madavaram:

Hi, welcome back listeners to the fraud technology podcast. And today we have somebody with almost two decades of experience working in different domains within the financial services and the last 10 years working in the fraud space. And that is Chris Dennis. Currently he's at MasterCard, but he has a ton of experience coming from different companies as well. So welcome Chris to the podcast.

Chris Danese:

Thank you so much. Great to be here.

Ravi Madavaram:

Yeah. Nice to have you here as well. So first I would love to understand how. You are, you're shaped up into and finally going into fraud. How did that journey happen?

Chris Danese:

Yeah. So my journey starts back in college. So I went to college, graduated in criminal justice with an interest or passion of understanding, you know, criminal behavior. I moved into law enforcement, spent seven years in law enforcement. So I somewhat solidified my background there just. Understanding the mechanisms of honestly, how to, how to talk to people in the way that you're helping to draw out information about suspicious activity. I moved into banking after, uh, about nine years and my role started as many do earlier in their careers. I started in a call center and in the call center, I was taking calls, talking to customers about authorization activity. And did you authorize a charge? And this is you. And verifying that you are who you say you are. You know, I was very fortunate. I progressed in my career into management roles. And then from a management perspective, I had the opportunity to oversee analytics, specialty operations, which included identity theft, account takeover, any money movement, like ACH wires, bill pay P2P. And then I was fortunate. I was speaking at a conference and at that conference, at the conclusion of it, one of the, uh, Hosts had approached me and asked if I'd be interested in an opportunity working for a fraud technology company. And again, I'd always been on the issuer side, but I thought it was a good fit for me to try to expand my experience. I was a little reluctant cause I didn't really have sales background. So I was a little bit fearful, a little bit tentative, but I was super grateful. fortunate and glad that I did it because it opened up so many new doors to new people and different fraud experiences that were far beyond just what issuers experience. Then ironically, about three and a half years later, former coworker of mine was working where I am today and had called and reached out to me and said, Hey, listen, they're looking for some people. Would you be interested? So again, by building that network of folks and staying close to individuals in the industry, That go to different companies. You know, that's one of the ways that you can find your career takes you is depending upon the relationship you've built over time. I'm so glad that I did. I came over to MasterCard and, uh, I've been really enjoying the type of work that we'll talk about a little bit today, but really enjoying the work that I do and how we help our, our customers and clients.

Ravi Madavaram:

Awesome. Awesome. So, I mean, I'm, wow. I was fascinated with being in a conference that actually you're part of. I'm assuming you were part of a panel discussion when the host asked you to. That's really. a happenstance that you were at that conference and then you, you're transitioning to fraud after that conversation.

Chris Danese:

Yes. And your description of happenstance is far, far more accurate than mine. So yes, it just happened to be a coincidence that I was at the right place at the right time.

Ravi Madavaram:

Correct. Correct. So, uh, you also talked about your experience in the law enforcement and you also studied the, that part of, um, that as well. Right. So we'd love to understand a little bit One, obviously, how was your transformation from being a, from the government side or the law enforcement side to working in the private sector? I'm sure that was a big, big jump. I'm sure some of them do, but I'm sure that's not a typical career path for a lot of law enforcement people as well. So we'd love to understand that transition happened and how was it personally for you as well?

Chris Danese:

From my experience, folks that retire from law enforcement do look to get into some form of, whether it's AML type of work, any money laundering, or some form of investigative work for financial institutions or insurance companies. That seems to be the career path. I just did the same thing. I just didn't wait till I retired. So I felt it was a good time for me to try something different. There were some, um, Personal reasons for making the change and I just happened to be again sitting in. I was earning my masters at the time and my masters is in human resources and business relations and one of the folks that was in my class. I was working on a project with and she happened to be an HR manager at the bank that I went to. And we became friendly and working on the projects together. And, uh, you know, I got to know a little bit more about the private sector and it intrigued me. So I said to her, uh, at one point during the project, I said, well, I want to take the hire somebody like me. And, uh, she said, no, start with an application. And I said, well, that's good, good suggestion. So, uh, again, just a coincidence of a meeting the right people at the right time. If I wasn't in taking those classes and if I wasn't in there, I would not have probably ever even thought about doing something different. It's just kind of what I knew. But then as I got exposure to different career paths and different companies and sort of, there's a very big difference between how the private sector treats employees and the public sector or government sector treats employees. So, uh, I was looking for a better quality of life. And that was sort of what, where it took me.

Ravi Madavaram:

Wow. So this is the second instance where I'm seeing a coincidence actually helps you make a huge career transform and that's not a small one either, right? Because I think what you're probably not talking about is the people that you interacted with had a huge amount of respect for you. And so when that happenstance or the coincidence happened, it flew right away. Um, so I'm sure that underlying work ethic and respect from your colleagues has helped you, but I cannot not notice the fact that you were open to new things and new directions in your life, and you've taken whatever opportunities

Chris Danese:

I think one of the things that helps with that is, in law enforcement, every day I was taking risks. And, when you make a career change, you're taking a big risk. And a lot of people aren't comfortable with that. They don't like the unknown, so, they're interested, they're intrigued, they're looking forward to something different, but when it comes time to actually making the step, There's often some people just stay paralyzed in the ability to say, I don't know what the unknown is, so I'm just going to stay in what's comfortable to me. And that's not where I operate well. So I'm always looking for something different, something to challenge me and this space of fraud prevention and fraud detection. And I'm amazed at the Some of the fraud schemes that we see and I'm like, man, these people are good and they're smart and you just got to stay, if not ahead of them, you got to at least stay up with them.

Ravi Madavaram:

Yeah, I mean, again, I totally agree with that because I think one personality trait that I'm seeing here is, as you rightly put out as well, that a lot of people are uncomfortable with. Some change or an opportunity presenting it itself to them and they're not necessarily either comfortable or not excited by it then, but you seem to be flowing with it more naturally. Like, like water. I mean there was a code that was about be like water, you need to flow, like water, take the least assistance and just go with it. Right? So you seem to like be that kind of person who just takes what is an option in front of you. And not be afraid of it and just go with it. And that seems to be a personality of you and which also fits perfectly. I mean, you also talked just about it, which with fraud as well, that because fraud is changing so much that you also need to keep moving with the fraudsters along with the respect for who is doing the fraud. So I would love to know more about how your fraud journey has been. What are the kinds of things that you've done? And, uh, probably a little bit about what keeps you excited about fraud. We'd love to know more about your journey in fraud itself.

Chris Danese:

Sure. I look back at, you know, I thought we were pretty sophisticated when we were looking at signatures on checks and trying to compare them to other signatures and nobody writes checks anymore. So my journey starts back a long, long time ago when it was a very manual process. You didn't have some of the technology and the capabilities that exist today. A lot of the modeling, the techniques that are used today, we didn't have, it was instinct, you know, you looked at something and you had to determine whether or not it felt like the type of behavior that this consumer would be doing, and if not, you instinctively had to decide whether or not you wanted to stop that check, block that card, and some of that still happens today, but much more of it is based off of scores and models, And matrixes as opposed to, I just don't like the way that looks. So, you know, the instinct piece is still an important part of it, but you can today leverage a lot of technology and capabilities to help guide that decision. And I think that's. What I've enjoyed is contributing to some of those advancements over time. Um, so, you know, if you can always look at it and say, there's gotta be a better way, there has to be a faster way, a more efficient, a better, a more costly, a cost effective way to do something, create efficiency with better results. That's really what my. Career has been so whether it was the early days of, you know, as I said, looking at signatures on checks to, you know, coming up with ways to identify identity theft and synthetic fraud, building models and scores, looking at money movements coming in and out of institutions for fraud or purposes. Each step that I took, I wanted it to be something uniquely different than the last. So, sometimes I see folks that, uh, kind of stay in a lane, and they enjoy a career that's, you know, focused on, let's say, account opening fraud. You know, I've done that. I have really enjoyed and seeing every, every aspect of, you know, the importance of what upstream does to downstream and vice versa, a decision that's made over here impacts, you know, the performance of something else over there and being able to balance that across the entire life cycle to create what the customer wants is a seamless, uh, experience with. Able to use their card or their account freely without disruption is, you know, the total view that I think that my experience has allowed me to help clients get that.

Ravi Madavaram:

I understand. So you also mentioned that you start in the call center space of the financial services, right? So can I also understand what are the different kind of roles that you've done? And you also mentioned quickly about, you know, people stay in their lane, but you trying to learn more and more things, right? So I would love to know what are the kind of rules that you've done over

Chris Danese:

the years as well. Sure. I think from a call center perspective, one of the hardest things was coming from law enforcement. You know, I had a face to face interaction with people. So you're able to read suspicious activity by body language and other things. Moving into a call center, that was a very different experience just to being able to detect suspicion or, or, uh, nefarious behavior when I can't see you. So it was a new, it was a new skill set that I had to learn. But the types of experiences that I've had, one of the probably first. Really unique or specialized role was working bust outs and bust out fraud simply is, you know, consumers that are taking advantage of full credit lines and greater. And, you know, you're going back 20 plus years ago. So these were terms that weren't actually called that 20 years ago. It's evolved and it's become more formalized. But, you know, we were looking at that type of behavior and working closely with the bureaus to understand. Look, there are some patterns that we're seeing that you are participating in the. In the development of some of these accounts and some of these behaviors, how can we work with you better? So again, that's not, uh, you know, a lot of folks do that in the industry today, but it was new at the time and we didn't, uh, understand how do we treat these customers. So first it was, do we close them down completely? Do we reduce their lines? Do we manage them and monitor their activity differently and only restrict them to certain purchases? That then led into, well, you know, we have to be, pay a closer attention to payments. So then I became very familiar with that. Payment activities for again, one of the ways you bust out on an account is making multiple payments from multiple sources for dollar amounts greater than the balance owed and on. I got very familiar with understanding the types of techniques they were using, so that helped me become a payments expert, which led me towards analytics and from an analytics perspective, helped me understand how to write strategies and rules. And then again, a lot of that was card related. So I wanted to learn the money side. So then that's when I decided again, to raise my hand, to take on a new opportunity. And, you know, again, I'm going back before online banking was a thing. So when online banking became a thing, and we now are having people, instead of coming in and requesting a wire in person or an ACH in person and a branch now wanting to do it through an online channel, we didn't have. Anybody monitoring any of these things, right? So I said we need somebody to monitor this stuff So let's figure out how to put a team together And understand what are the risks that we should be looking for and build strategies around them so that we can Monitor money coming in and coming out So again a lot of the things because of my maturity I experienced before they were even a concept So there was no online banking When I started my career, you know, there wasn't mobile banking. Now the payment space today is wildly different. There was no such thing as Zelle and a P2P. So each time something new came about. So I'll give you an example today. Like I'm really interested in open banking. I'm really interested in generative AI relative to model building. So whatever the new thing is. tend to kind of gravitate towards. And I think that helps, you know, people like me and people in their careers stay relevant. If you kind of stay in a lane, like if I stayed in the Czech environment, I'd be long gone, right? So, you know, it's from a career perspective. I think it's important to just know what you're doing. What's the next generation of things of importance? I'll give you another example of I think one time passcodes are quite frankly going to be a thing of the past. And it's interesting because I work with clients that are trying to get SMS messaging and one time passcode set up for the first time. Like it's already outdated.

Ravi Madavaram:

Yeah.

Chris Danese:

So that's the influence that we're able to put it for forward for our clients to say, you know, there is a better mousetrap. What you're doing is. New to you, but it's not new to fraudsters. So what else can we offer? Whether it's, you know, biometrics through, you know, a one time pass goes through biometrics as opposed to a new numeric value. Again, that's far more valuable, but it's not widely adopted yet. So we have to help clients understand and how to use it and why that's a better option for them.

Ravi Madavaram:

Understand. So you talked about, uh, gene AI and few other technologies. Right. I also understand, I mean, you, you mentioned that you are not a technology execution. I'm, I'm assuming, but I'm assuming you are using technology. Mm-Hmm. as a user all the time. And when you talk about technologies like gene ai, are you looking at it from learning the technology or are you looking at it how a. Forrester could leverage this technology to come into the system. Are you taking the Forrester's perspective, or are you taking your own perspective to learn this thing?

Chris Danese:

I love that question, and I'll tell you why. Because one of the things that I hear constantly is, you would be a great criminal. Like, and I don't know that I'm, I don't know that I'm proud of that, but a lot of people say, man, you think really, Differently. Yeah. So, and I'm not alone in that, right? There's lots of folks that have kind of that, you have to have the dual minded personality of well, if I really wanted to get away with it, how would I do it? Right? So, but from a gen AI perspective, I don't know what the future looks like. I don't know that, you know, Because again, I'm not, I'm not a processor or business owner of that, but what I'm most interested in is, you know, today when a financial institution is writing rules or strategies, right? Those rules and strategies apply to a large swath of customers. And not all customers behave the same, right? If you have a customer that travels frequently versus one that shops online frequently versus one, you know. What I'd like to do is see far more. Segments within strategies so that you can be in a, I'll say authorization or fraud rule group of people that perform and behave like you and I can be in a group of customers so that you can have the best experience that's personalized. With your card, and I know, you know, lots of marketing will say, you know, it's personalized, but at the end of the day, I know when you're writing all strategies, they apply to thousands and thousands of people, as opposed to how do you really personalize the experience based off of the customers, not only spend patterns with one bang, but spend patterns overall. Okay,

Ravi Madavaram:

so you're probably alluding to a concept that I was familiarized a long time ago in analytics, which was a segment of one concept. We talk about segments, right? So cluster your customers and do analysis, right? So you're probably alluding to segment of one that each individual person has to have it. I mean, not has to have, but more like if you try to segment over three people, you're going to lose something.

Chris Danese:

Yeah,

Ravi Madavaram:

right. Exactly. And you're going to end up having having fun. So is that what you're talking about?

Chris Danese:

It is essentially trying to figure out how to give each individual consumer the experience that they want, right? And one of the things that's really, I guess, different from, and again, I, I work globally with clients, but, uh, from a North America perspective, you know, you look at customers have on average, I think it's about six and a half to seven cards. So some customers use a card primarily for gas and groceries. Other than I use my other card for travel and I use my other card for this. Right. So that's why I think the. The importance of how do you create the experience that the consumer wants for the card that they have for that particular transaction? Which you know goes back to your segment of one You know, I don't know what that looks like, but i'd like to be involved in shaping that somehow

Ravi Madavaram:

This was a concept that, uh, came about when analytics and data science was a thing. Not here, not yet. Yeah. But when analytics were, at least marketing was talking about, you know, uh, instead of doing segments, we should do a segment of one. I don't think anybody has ever gone to that, but there was a concept like that that was being discussed at some

Chris Danese:

point. I haven't seen it successful. I've heard, I've heard folks talk about and seen examples of segmentation, specialized segmentation, but it's not the true concept of what I think the future could look like. Yeah.

Ravi Madavaram:

Yeah. Got it, got it. So one thing that you touched upon, and I'm probably changing tracks here, is previously fraud fighting or fraud prevention or strategies were more instinctive, that you followed your instinct, right? And now it's more looking at scores and models and stuff like that, right? One, one aspect. So, so there are two aspects, quite a few things that differ in that, right? Going by instinct is fun. I mean, like, like, like being a pilot of a fighter jet was the commercial pilot, right? Because the commercial pilot has to go through all the instrumentation, has to do all the checks and stuff. So it's probably boring as well. I don't know if I'm describing it correctly, right? So what to understand, because doing it instinctively, Things instinctively can be fun as a personal, as an employee experience, but going through scores and like looking and making decisions based on rules that are set. can be boring, right? So how do you keep it interesting for at least the fraud analysts or fraud fighters that are out there?

Chris Danese:

Well, I don't know if I fully agree with the assessment and the reason for this, it can be exciting. It's just a different kind of excitement. But when you're looking at large data sets and you're like, You know, there's a pattern here, you know that there's something that they're doing that's causing x And i'm gonna figure it out and i'm gonna put something in place to make their lives uncomfortable It's a different type of excitement It's not the maybe the adrenaline rush, but it is more of a chess type of match of you're doing something The data's going to tell me what it is, and then I have to figure out, okay, now what do I do with it? So now I know what it is, but I, now I have to figure out a way to prevent it from occurring, you know, and how many good customers are going to pick it up, bad customers. But I would say some of the biggest fraud events that I've been involved with, some of them massive, millions and millions of dollars. If it wasn't for data, You know, you could look at the date and I could look at the date and we both come back with different things. So I'm thinking of an event that happened a year ago, right? And there were multiple people looking at it. And quite frankly, it's like internal competition. I'm like, I figured it out, you know, and we are looking at the same thing I am. But, uh, you know, I figured it out and this is what they're doing. And now let's work together. If we agree that that's what's occurring, let's work together and figure out how to stop it. So it's just a different type of excitement. It's not fighter pilot exciting, but it is, it is a challenge. And that challenge is you got to, how quickly can you figure it out? Because as long as it takes you weeks. A lot of money is getting lost, you know, so it's an adrenaline rush. It's just a different one.

Ravi Madavaram:

Yeah. So instinct, you're probably going one by one, one on one person, right? Here, you're probably looking at problem solving and hunting for that pattern that is alluding, basically. It's you're, you're looking for it. And that's, there's a sense of, excitement in actually looking for the pattern itself, right? The different tools, different data, different, uh, sort of thing, but you still, there's an sense of excitement.

Chris Danese:

But you have to have instinct to look at the data too. You have to have some idea. What's the end game? Why are they doing what they're doing? Is this the final result? Or is this leading up to something bigger? And a lot of times these are precursors to a bigger attack. So, you know, Yeah,

Ravi Madavaram:

cool. So you also touched upon one of the questions that I wanted to go through was, Time. So you talked about, you know, if you take time to assess what the pattern was, by the time you lose a lot of million. On one hand, you also talked about Zelle and Venmo and things like that. So one is, you're talking about real time payments and fraud and time being a critical measure. And also time being a customer experience part as well, because people are expecting real time. Previously, you know, you take, you took 48 hours, so you had time. to essentially go back. So how do you manage the increasing expectations of customers without the time that is needed for you to even catch the pattern?

Chris Danese:

I think I would answer that in a way that's not necessarily time, truly time sensitive, right? So for me, the onboarding becomes the important part of that. If you onboard the right people and you're confident that the individual is who they say they are, and you know, their history, that they don't have a history of malicious activity per se, then you're working with a trusted individual. So the, Transactional monitoring becomes, I don't want to say less important because it's equally important, but where I see a lot of the risks in that real time space is the rush to, you didn't verify the device, you didn't authenticate the individual properly, there's risks within this space. consumer that were evident and you failed to identify or at least address them if you identified them and address them properly. But once they're on the platform itself, then I think a lot of the traditional fraud monitoring, whether, you know, the same techniques exist, whether it's in real time or if it's, you know, a 24 hour ECH, the rules are similar because, you know, why is Chris paying Robbie? Whether it's In a second or in 24 hours, the question is still the same as why is that transfer? Why is that money movement occurring? But if you don't know that the recipient is who they say they are and the sender isn't who they send they are then you know Your opportunities to recover and prevent that become far less So I think it's more about the onboarding and the identity verification and then of course there are different Strategies that you use because there's different data elements in that authorization or that transaction type But the method of writing, you know, modeling or writing rules You It doesn't change a great deal, whether it's a, like I said, a real time payment or a 24 hour. The whole difference is your recovery opportunity is far less, you know. So if you have good recovery and you have good identity verification and device identification and funding sources, that's where I think you can strengthen your program. So the time

Ravi Madavaram:

sensitivity is for the recovery part, but not necessarily on the detection or recovery. Decision or disposition part. Is my understanding accurate here?

Chris Danese:

Yeah, I mean if the funds are sent And your, your recovery window is pretty small. So it goes back to is Chris, Chris, is this something that Chris is authorizing is, and as the recipient who they say they are, and outside of that, I would say a lot of the techniques that are used. And they're certainly wildly different depending upon the platform. But I don't know that there's any special sauce, I guess, or special rules that are, you know, far greater than the other. It's you use the same modeling techniques. You're looking for the same types of suspicious activity in the transaction. The difference is you just don't have the opportunity to recover it once you hit

Ravi Madavaram:

go. So if the techniques of detection are similar, you on the few other questions, few questions ago, you talked about trying to detect the patterns, right? Wouldn't detecting a new pattern come up to a new rule or a new way of detecting it? Um, I, I don't know if I'm clear with the question. So one, on one side, you're saying that, Hey, we are using similar, not we are using, which the rules are pretty much similar, right? But there is different patterns happening and there is a, there's a, uh, Uh, there is an energy in finding that pattern itself. So, would a new pattern not create a new rule?

Chris Danese:

Exactly. So, maybe from a verbiage perspective, the techniques are similar, but the rules should be changing. Constantly, right? It's not a set it and forget it. You're constantly recalibrating rules on an hourly basis. You should be monitoring activity depending upon the size of, but whether it's in real time, you're monitoring or at least daily, weekly, but it's never a set it and forget it. So what I'm saying is, yeah, the rules should change, but the techniques of which you apply those rules are similar because again, you only have so many data elements to work with. So Your logic is based off of the elements that are available and the settings of which you determine one parameter is greater or less of a risk than the other. You know, those techniques are standard. I don't say standard, but those that are more of a moving target.

Ravi Madavaram:

Got it. So you also talked about strategies to fight fraud. Now, I want to understand how a strategy and a pattern or a rule or a technique, what are these elements? How do they work? all fit together. I'm assuming when you're talking about patterns, you're talking about detecting the mode of operation of a particular type of fraud and then figuring out how to How to put controls in it to stop it. So we'd love to understand what is the entire Ecosystem and a step by step understanding of that strategy.

Chris Danese:

Yeah, and I think that's from an ecosystem perspective That's really the way you need to look at it. It's not transactional monitoring, right? It's the what are the strategies that are in place today? To on board a new consumer and that could be the flow itself. In what order is the application being completed? Or what are you verifying versus what are you collecting? So an example, some institutions. They collect information, but they don't verify it. So if you're collecting a phone number and an email address, and you're only verifying the name and the physical address and the date of birth, but then you're going to use the phone number or email for one time pass codes down the road. Well, you're using an unverified email address. Phone number and email to do authentication, which is not authentication. So, you know, those are the types of strategies say what information are you going to collect? What's the channel it came in? Is it a face to face, a paper, a mail in? Is it an online application? So, you know, whatever that component is there that channel is Dictates the information that should be one collected and two verified. And then the question is, if you're verifying, is it being verified against an independent third party source or you're verifying it around something that's not reliable? And oftentimes we see authentication being authenticated against something that's not a reliable source. So what's an example of that? And I'll see people do our, our clients, you know, doing Google searches and looking on the Internet for your LinkedIn profile. Okay, well. Yeah. I don't know that I would say that that's a independent, verified third party source, right? So there's a profile out there, but, uh, and you know, so that would be some examples of that. The others is, which unfortunately is going away, but in some instances where institutions, you know, one of the rules might be, well, if we can't verify you, we're going to request documentation. Okay. So let's, right. So that documentation could be a scanned image where your government ID is being authenticated through technology, or some would say, well, our strategy is just send us a copy of it and someone will visually inspect it. And, you know, to me, that's not authenticated. It's a scanned copy of a picture. So that's the difference between what you're collecting versus what you're verifying and authenticating. And that's, that's strategy, right? So which customers are going to go out that yellow path that are going to require some form of step up? And that's all driven by, you know, your application strategy. Are you going to send them in the green path straight to approval, instant issuance, or are you going to send them a yellow path that are required to update and provide additional information? Are you going to send them the red path because you don't believe they are who they say they are and you're not going to request additional information? So, you know, that's from the account opening perspective. Then you get into, to, okay, the consumer has been approved. Well, then you need pin strategies. You need plastic strategies. So strategies exist there as well, right? So how are we going to get this consumer pin? How are they going to change the pin? How are we going to get the plastic in their possession? They want an expedited card request. What do those strategies look like? So, you know, we could continue down the account life cycle into the transactional monitoring, then non monetary updates. So strategies around, I want to change my phone number. I want to reset my password. I want to change my login. I want to add another user to an account. All of those are strategies. So I think in the broad terms, one of the things that. People that don't have a wide range of experience could think of, you know, fraud strategies as transactional monitoring. And that is not what a career in fraud strategies can look like. Fraud strategies can be account opening to, like I said, PIN management, to transactional monitoring, to money movements, to ATM strategies, to non monetary activity on an account, to Linkage between multiple accounts and looking at the relationship point of view. So, I think what I'm just trying to share with you is strategies and the data used to build strategies. is wildly different depending upon where you are in the life cycle. And that, and that's from a career perspective, gives you lots of opportunity. So there's lots of career paths you can take as an example. And then, you know, you look at your chargeback and recovery, there's all types of strategies around disputes and chargeback and recovery rates, et cetera. So again, more opportunities to create strategies on what to do. What you're going to essentially take as a, as a load out right off and you know, what are you going to challenge and how far are you going to challenge it?

Ravi Madavaram:

Got it. So essentially every process that a customer has as an interaction with the company can have a fraud. For prevention element, uh, to it and it can happen. So transaction monitoring is only one part after you're on boarded and you're doing transactions. But you are saying that every process, every interaction that you have with the customer can have a fraud element to it. And I'm assuming that you would probably not put fraud. For people everywhere, you would probably assess, probably analyze the data and say, okay, this part of the process is seems where the problem is. And so we should focus here. And if you plug that hole, you have some other hole and we go there and I'm assuming you keep doing that all the time.

Chris Danese:

Yeah. And you can, you know, then the other opportunity is to create different experiences for the, for the consumer intentionally. Right. So what level of friction? So you might want to try to. Log in and look at a query on your account and, you know, say, no, you got to call in. So you're not going to be able to log in. You're going to get a failed authentication, and then you're going to get a number to call in. And then based upon the number that you call, whether you called the number on the card or the number that we told you to call, we'll dictate, you know, what treatment you get. So even within just. The experience you want to give the consumer to authenticate them by channel is a strategy. Mm. You call into the IVR, you might get a live agent and uh, because you don't have a choice, you're gonna get a live agent.'cause they need additional information on you and I, you know, I might go through and get, you know, automated responses, be able to satisfy my inquiry. Understand that's all

Ravi Madavaram:

strategy driven. Yeah. Got it. Got it. You also touched upon verified and, uh, unverified. Mm-Hmm. data. Right. And in the last 10 years, obviously we have seen. A proliferation of availability of data, right? I would love to understand a little bit about how do you prioritize because there's just so much data as well. Sometimes it can be noise. Sometimes you also pointed out, like, for example, LinkedIn profile. It is data. But it's unverified. It can't be fake data as well that I went and created the profiles just for that so that you can see it right. So how do you prioritize more data? What do you rely on? What do you not rely on as more and more data is available?

Chris Danese:

I think I could answer that from, you know, what excites me is more from a technology perspective and seeing more and more. companies get into fraud orchestration, right? So the term orchestration. So, you know, in my earlier days working for a bank, I'd have 20, 30, 40 different vendors doing different things, right? And the reliance of data coming from multiple sources, especially if it's competing or conflicting data, you know, you have to decide, well, which one is going to be the source of truth, which one is going to be the secondary, you know, Makes it very complicated, but what I am really enjoying is working with some of the companies that are, you know, a one stop authentication hub, right? So, you know, whether it's appropriate or not to name some of those particular vendors, but, you know, I as a financial institution have a relationship with And your company's sole job is to, if I want to verify a phone number, an email and address, whatever it is that I'm looking for, they have the relationships with the third parties. They're doing the modeling, they're doing the scoring, they're doing the Contracts and all of the things that like I can get out of that business and just focus on give me a response based on the risk level that I'm comfortable with and the response that they provide to me dictates. So more and more. I think customers will get out of the trying to manage all the data and manage all the relationships and relying upon orchestrate companies that orchestrate authentication technologies as a single stop a single API. That's where I really hope. The industry goes.

Ravi Madavaram:

It's

Chris Danese:

expensive. And you know, that's one of the barriers to entry is the cost of it. But there's so much value in doing that. Got it.

Ravi Madavaram:

Uh, so one final question. I'm also talking with the time as well. Is you clearly alluded about, you know, orchestration, how that can help be helpful, right? So you also have been in the industry for some time. So where do you think the next set of challenges or next set of problems around fraud would be in the next few? Uh, yes, I know Gen AI probably is going to be a catalyst to this, but do you have a sense of where we are heading towards?

Chris Danese:

So I think the authentication that was historically common practice is going to be and should be obsolete soon, right? More and more consumers are getting comfortable with, you And the acceptance of biometrics as a form of authentication, right? Whether it's facial fingerprint on your device, right? So I think biometrics is going to be a different type of data. Right? Best way to say it, a different type of data. So some of the traditional techniques, I think, will become obsolete. Again, trying to stay ahead of the curve and allowing authentication to happen seamlessly without the consumer even knowing they've been authenticated. Right. So you see it as standalones, as voice biometrics and call centers, you kind of see different ways of which, you know, I've enrolled myself into authentication, you know, by providing my face or something else, but one, there's going to be a lot of privacy and, you know, a lot of regulation around what that looks like, should be as well. But I do think that authentication can become far less frictionless. But I also think that you're going to have to have different types of experts than you have today because analyzing and writing a strategy on I am authenticating you based off of Biometrics versus I'm authenticating you on historical data. It's gonna be different and exciting too, but Hmm. It could be different. So, you know, biometrics as a service, for example, is just more and more companies that we're seeing coming out that Mm-Hmm, you know, you're not sending a one-time passcode anymore. You're not sending a numeric value to authenticate, you are receiving a link or a text message, and you are responding to that text via biometric response. So, got it. Mm-Hmm. that's very different type of experience and authentication and data than we've seen historically.

Ravi Madavaram:

That's wonderful, Chris. And a few things that I take away from talking to you is one, as a person, I think I really loved the ability for you to navigate those opportunities. I really think that is a great asset that you possess. And I would love if some of the listeners could Take inspiration from something like that, because I think opportunity presenting and then going out of comfort zone is not necessarily natural for a lot of people. I do love that aspect about you, along with the expertise that you're bringing about, um, fraud, um, itself. So thank you so much for your insights today. And also, I'm happy to talk to you and know you as well.

Chris Danese:

I'm very, very glad that you had reached out and, you know, offered to have this conversation. I've enjoyed it as well. And, uh, you know, I, I just want to thank you for giving me the time and, you know, the opportunity to share my story.

Ravi Madavaram:

Thank you. Thank you, Chris. And, uh, hope to see you soon and again here as well.