thanks for the opportunity to talk about, the library, Yeah, I in, disturb payments. Basically, it's a great opportunity here. so I want to continue. I want to, take this time and then thank you everybody, who is having, the session and then who is attending the session and then hope, after the session, you guys get to know more about why we need AI in disturb payment security and this current world. So I wanna I'll basically. Go through some of the slides I have. so yeah, let's get started. So the global, digital payment market is rapidly basically expanding and throughout the world. And we are projecting this, payments will exceed 15 trillion dollars by 2027. That's a huge number. Growth is basically followed by increasing the number of factors, something like, contactless transaction, evaluate and black chain based system. So this kind of stuff. And then, however, this is like a lot of, the pros comes with the cons and something like that. There are some, cyber threats, online frauds that are happening. And then, that, that's a huge number that we are talking right now. That number may reach by 48 billion annually by 2025. So this presentation explore basically, the artificial intelligence is transforming to still a. Payment security. So why we need, artificial intelligence. we'll basically dive in, into the realtime fraud detection, predictive risk analysis, and adapt to threat migration. We'll showcase how a power module, out perform in, traditional methods and discuss. Emerging things like quantum secure inscription and adversarial AI ism. So paving the way for several digital financial ecosystems. I'll move on to the next slide. so the AI fraud detection, there are like a different type of fraud detection that we could do. something like, I can explain something like deep learning and all of my new So deep learning, when it comes to deep learning, it's a neural network, and a deep learning algorithm. It's a kind of revolution, I would say, and then, which basically detects the fraud by analyzing millions of transactions that happens in real time. And achieve the And 80 percent of improvement over traditional root based system. So these sophisticated models adapt and, learn few new fraud pattern continuously. and then when it comes to anomaly detection, advanced mechanism, advanced machine learning algorithm process, complex transaction patterns. Use behavioral and constrictional data to identify suspicious activities and then with more than 98 percent of accuracy, and this precise dramatically reduce the false alter while catching the genuine threats. and then let's move on to the productive analytics. That's a, and then there's a final 1 by analyzing the history of fraud patterns, and imaging the threats. Basically, so AI powered protective models enable financial institutions to prevent fraud before it occurs. Basically, when something is happening, something is fraud happening. Basically, it, AI basically detects this, and then the fraud is then saving the estimated savings. using AI, it's 12 billion annually in potential losses. yeah, this is huge, 12 billion annually to save using the AI and why traditional security system rely on the rich rules that, fraud ensures can study, explore, but AI power solution continuously evolve to counter new threats. we can detect the things before it happens and then we can save a lot of money. So basically, we doing, deep learning algorithm Excel and, detecting the suitable patterns across the billions of data points and enabling real time fraud prevention that adopts imaging the attack vectors. So the intelligence and learning approach not only minimize the financial losses, but also enhance the customer. Trust by providing, frictional, it's secure transaction, basically. yeah, move on to the next one. So let's talk about the real time transaction monitoring. So there are like three things we could do, reduce a fraud incident and then biometric integration and then frictional experience, right? So basically when it comes to the reduce the fraud incidents, using the advanced, AI monitoring system, and then we can achieve basically reduction of 30 percent of fraud incidents and then saving the financial incidents over 2 billion annual across the globe networks. That's a huge number, 2 billion and it's across the, global networks. And then the second. Thing that we could do biometric integration, multi factor biometric, authentication combined, facial recognition and then fingerprint scanning and then behavior analysis created and what's well, implemented security still with a 99. 9 accuracy. that's very close to 100. So 99. 9 accuracy where. We can basically detect and then, we can use a biometric integration. that's what we can do. And then, frictional explore experience, smart, bio biometric verification, complete authentication in under 0. 3 seconds, maintaining robust security while delivering the seamless payment experience. That rate is, the card. I'm gonna buy 25%. That's a huge number there. And then the economy and then where transactions occur a million, milliseconds, the real time monitoring serves of the different payment security or algorithm process over 100, 000 data points per second, where it flags lot of suspicious pattern, which allows. We're allowing the, legitimate payments to go through, and then we can cut off the, theran, the kind of fraud payments we can cut off, and then we can save a lot of money. yeah. And then, yeah, this is, we can use these kind of realtime transaction monitoring over traditional method to detect the fraud transaction. And let's move on to the next one. So the AI enable. blacks and security. So basically you can do the A. I. In black chain security. You know how, it's very big deal with black chain security. So the enhanced privacy, you can do the, enhancing the privacy and then, federating the learning and then secure transaction. So basically there are, enhancing privacy, I powered zero knowledge proves enables secure verification of transactions. Without revealing sensitivity details and protecting the user privacy while maintaining the transparency. So basically, in nowadays, the privacy is most interesting and then most thing that people think about sharing that data. by enhancing the privacy using the AI and AI, which saves, a lot of stuff, and then it gives us. And hence, the zero knowledge proof enables security verification. and then federated learning by training the AI models basically across the distributed networks while keeping data locally and, federated lending enables financial institution to collaborate securely without compromising the customer data. So basically. You, whether compromising the customer data, you can basically, go ahead and use the AI models and then, teach them or, guide them, train the AI models and then get the, max, maximum out of it for the customer that's using the federated lending and then secure transaction AI algorithm continuously monitor the blockchain transactions. And detecting anomalies and potential threats in real time while maintaining the immutable audit trails. So the secure transaction, basically using the AI, in the real time, you can detect the frauds just like that. And then you can save a lot of time, money for the customer and for the institutions. yeah. And then the, the. Convergency of blockchain and then AI created a powerful foundation for next generation payment security. this is the future. So we're basically AI and enhanced blockchain native security feature by adding the intelligent threats. And detection and then privacy presuming mechanism, through advanced techno techniques like zero knowledge proofs and then federating the learning and then financial institution can now share insight and valid transactions while. Maintaining strict data privacy standard and leading 60% of detection in security breaches and full, with global privacy. Yeah, that, that's what we can do with blockchain. AI, let's move on to the next one next topic here. And this is basically a case study case studies in the industry leaders by industry leaders. so chargeback, reduction, leading, bank have achieved a 40 percent reduction in the chargeback prior and then, and, savings over a hundred million annually through AI powered transaction validation. Use AI validation, and then which basically saves 100 million annually, in the banking sector, basically, or you take any kind of sector and then improve the speed. So everybody want to work very speed. Everyone want to get the results, in a time manner. So global payments processes plus a report 20 percent of 25 percent improvement in transactions. That's a big number. 25 percent is basically. You are basically improving the secured payment methods by 25% and processing over hundred thousand transactions per second with, with the enhanced accuracy. So basically, you're doing with using the, the, whatever industry re selling the hundred hundred thousand transaction per second, enhance security. Basically, this is a power to system have reduce a false positives. By 60 percent while blocking 99. 9 percent of fraudulent transactions. it improves the 60 percent of the false positives, where, so that, something we usually get false positives nowadays with using the traditional method. significant improvement in customer satisfaction. And then these partner improvements. Implementation by, these are all pioneers implementation by major financial institution demonstrate the, transformative power of AI payment security, but combining mission learning with the traditional security measures and then. These organizations have not only strengthened their fraud detection at the same time, the, they enhance the customer experience and then get the, faster transactions and then get the payments done very easily and they said, this is all like a blueprint border financial industry adoption strategy and then let's move on to the next slide. the reduction in charge backfired. If you look at the graph bank here. Fitech B and payment process C, where AI power, fraud detection system have demonstrated the remarkable success in combating chargeback fraud across the financial sector. You can see here and then leading the industry, a bank achieved an immense 42 percent reduction in fraud rental. Chartbacks through implementing advanced mechanic machine learning algorithm. So you implement the machine learning algorithm in the payment level. And then basically, it will basically deduct the 42 percent of the fraud, parental transactions or chartbacks. And then the FinTech B, if you look at the FinTech B, a solution delivered 30 percent of. Decrease in the fraud under claims while payment process see until then system drove a substantial of 40 percent improvement. So that's a big number 40 percent improvement that gives that shows that, what is the reduction in charge back for and then these results not only represent significant cost saving. Potentially, millions of dollars annually, basically. but also shows how AI driven security solutions are, basically. all the AI driven security solutions, how it is helping the payment system to basically improve the, institutions, financial, and then, of course, the customer service to and the customer experience. if you look at the advancement in transaction speed, this is fascinating stuff. AI powered, payment system. Our revolution transaction process across the final sector, leading the transaction, the lead, leading the transformation is company X with a remarkable 27 percent reduction in the processing time and enable near institution payments, instance payments for millions of customers and company Z follows flows to 25 percent improvement while the company Y, okay. Achieves a 23 percent boost. So if you look at the numbers, these are good numbers, which has the customers, which had the institutions, they're all using the algorithm and, optimization, they're. Payment and then, customer experience. And then this is all the significant improvement, translate directly enhance customer satisfaction. So reduce the transaction amountment and then stronger competitive and advantage advantageous in digital payment landscape. yeah, let's move on to the next slide. So the next generation security, we were, we are talking about the next generation security where basically what coming so quantum security encryption implementing the post quantum cryptography algorithm and then latency. based inscriptions and to safeguard the financial data against, future quantum computing threats. this is all in all quantum security encryption. We can do that. And then we can basically, you know, we can basically, safeguard the financial data. And, the adversarial AI defense, deploying the sophisticated, neural networks and defensive algorithm to detect and, neutralize AI powder attack targeting payment system, basically. So if you improve the adversarial AI defense based and the deploying the sophisticated neural networks. and defensive algorithm to detect the neutralized AI power attack. Any AI power attack we can basically cut down, and then, target, which basically target the payment system, which is very important. And then enabling the AI, that is called, XAI, and then integrated advanced model, interpretation, technique, and, transparency frameworks, to ensure AI. discussions. all the decisions are traceable and then complete with a regulatory standard. So the next generation AI security using this three topics. And then we can basically advance the security of each payment and get the things done easily. And then basically this basically provides and then avoid the threats. to the future, next generation, or any payment stuff, that's coming to the, any institution, any, especially financial institutions. together, this innovation from a robust foundation for the future of payment security. yeah. the interesting topic right now, why we need, we spoke about AI and then, cyber attacks, and then, but the important point, why we need to use AI instead of the tradition way that is our traditional method that we are using right now. Very interesting thing, right? So yeah, using the ai, the of traditional matter offers several advantages that can signifi significantly, improve the performance efficiency, the scalability. So yeah, these are the things that, you can improve. And then here are the, and some of the, few things I'll go over right now, and then so that, you guys understand, why we need ai, Instead of the traditional method that we have right now in any, financial institute or any transactions, any payment transaction that we are using right now. So the first thing is automation of repetitive tasks. AI can handle repetitive and then mandate task much more efficiently than human, which basically you can automate the task and then you can get the results. Better than what we are doing by traditional method, which is done by humans right now, this allows the employees to focus on, high level discussions, and then making creativity and improving overall productivity. whenever you're using the AI, which basically does the automation on the repetitive task, and then which will helps the all the employees. Who is working in the institution, which helps the productivity and then taking a good decisions and then making, the all productivity good, improving the productivity basically. and then the 2nd thing we can do is speed and accuracy using the eye. A system can process a large amount of data quickly and accurately, unlike a traditional method, which is very useful for, a lot of, manual stuff that we are doing or traditional stuff that we are doing. And then this analysis, sometimes human may, face the error, but, in AI, you can handle tasks with a, greater precision and real time, which is, like I mentioned in the previous slide is You know, it's just like a false positive, you can basically have a lot of false positive using the AI and then using, this, and then you can speed up the transaction and the scalability, this is the biggest thing right now. It is scalable is the biggest thing in any institution. a business grow at traditional methods may struggle to keep up with increasing volume of data and demand. And, yeah, if you look at the numbers right now in any in the world. The payments, the digital payments are increasing a lot, and then the scalability for institution is the biggest thing. So if you look at the demand is very high day by day. So A can scale to handle vast amounts of data and complex tasks without compromising the performance. So making it ideal for large organization or, those with a rapid growth, like I mentioned. If an institution is increasing their rapid growth, you can basically, make sure that you're using the AI and then it will basically help you finding the things for you. And then, that's a good thing. And then, of course, cost efficiency, while there might be upfront cost implementing the AI. But over the time, it can reduce the operational cost by automating the task and then, reducing the errors and, streamlining the workflow. This can lead long term, savings and then better allocation of resources. data driven insight, AI can, analyze large, data sets and then uncover the patterns. Or friends that would be difficult or, what I'm going to say, time consuming for the human to spot, sometimes, we, we do a lot of errors with a human error that happens everywhere. it's, it's sometimes it's time consuming as well when we do some kind of error. So with AI. we can save a lot of time and then, this leads to more information, informed decision making and then improved for forecast and ability to take, proactive actions or steps that can, that can give a lot of time to the organization and then good customer experience and personalization AI enable highly me. Personalized experience by analyzing individual user behavior, for example, AI driven recommendation, engines like, those used by Netflix, Amazon right now provide the user a tailored solution, which difficult to achieve with the traditional approach. So when you're basically watching a movie and then when you're doing something in Netflix, Amazon, you'll get the solution, which. Right now, it's, compared to if you use a I, what we are doing in a traditional method, you'll see a lot of improvement in that. yeah, that's about why we need to use the instead of traditional way that we are using right now. Let's move on to the next slide. Though, yeah, quantum security encryption. so as a quantum computing advanced at the unprecedented pace, you look at the, world right now, everybody, and then most of the people are fascinated about the quantum, computing, what it does. What and why we need quantum computing. I'm also very fascinated about quantum computing and then so it presents a critical challenge to the digital payment industry. So today encryption methods, which they've got a billions of financial transaction could be rendered absolute and which powerful quantum computer become a reality. And this looming thread has sparked the Development of quantum security encryption. So the revolutionary approach to protecting sensitivity financial data. So leading financial institutions are already exploring the breakthrough solutions, latency based cryptography, which. Create the mathematical problem and that even quantum computer can't easily solve the other promising approaches including code base and multivariate Cryptography, so there are a lot of issues in using cryptography right now Which basically even you know using the cryptography sometime we fail, but Yeah, a lot of interest in going with crypto right now, each offering unique difference mechanism and then against quantum attacks. So by proactively implementing these quantum resistance method, bank and payment, provide can future proof their security, infrastructure and maintain the trust. Of their customers, in a increasingly complex technology, technical landscape. yeah, let's move on to the next slide. adversarial AI defense, in digital, payment, landscape, criminal, the cyber criminals are increasing, increasingly using adversary AI. To our smart security system, these, sophisticated attacks, work by, manipulating the transaction data. For example, let's say, just in the, adjusting purchase pattern or transaction timestamp in a way that appear normal to AI fraud detection system, but actually fraud and also think of it. As a creating a blind spot in a vision and allowing a transaction to slip through the notice. So financial institutions are fighting back with a robust advice. a different strategy in the AI. Now, financial companies or financial institution thinking about how to use AI through addressing training. Basically, you're training the AI and then you're giving the, you're supporting with a lot of data as systems exposed, simulated attacks and building the resistance, vaccines, strengthen the immunity. yeah, this is how an adversarial AI defense should work. And then, that helps a lot of institutions. So the key takeaways, from this, from this meeting or from this, stuff, what I'm trying to do is AI is essential, basically, and then it's a continuous adoption. So we're getting into AI world. we're starting which the AI is. helping in all the way and every sector, but keep the customer data, secure and then keep the payment, keep the digital mechanism secure. We need AI, which basically AI basically solve a lot of problems, especially the payment, payment securities enabling 99. 9 accurate and a percent of accurate fraud detection. And that's basically. It's not, it's a huge number, 99. 9, fraud detection is very huge number. And then it happens in, hours to milliseconds right now. So using the AI, you can get that accuracy in milliseconds and continuous adoption. Of course, the, any organization, anything investing emergent technology, quantum resistance and encryption and adversarial AI defense to stay ahead of evolving cyber threats. First of the collaboration, cross industry partnership, threat intelligence, sharing the networks are vital for detecting the emerging attacks, pattern and developing the standardized security protocol. yeah, so the integration of AI payment security as more optional, optimal, optional to the emeritus. Basically, we have to use it. It's essential right now. What we are seeing the AI is getting in all sector. we have to take this and we have to adapt the technology and then we have to use it for a good reason and then make sure that it is helping the And then it's basically, we are talking about the transaction right now, the payment transaction. It has so much. And then by taking those, concrete steps, the audits and, regular security, the organization can build a foundation for secure and resilient payment system. For future for tomorrow. Yeah. that's conclude my session on the, how are we going to use AI? Why are you, why it is so useful? and then, why we need AI, and then how it is helps to secure the data, the customer, and then giving the basically more productivity for the employee.