Good morning, good afternoon, everyone. My name is, and I'm excited to talk to you today about a transformative shift happening in financial services. Quantum AI driven personalization. This isn't just about making things a little better. It is about fundamentally changing how financial institutions interact with their customers. Yes. Life states, quantum enhanced AI is truly revolutionizing financial services through hyper-personalization. And leveraging advanced pattern recognition With quantum neural networks and secure data processing, leading institutions are already deploying real-time architectures that adapts to individual customer journeys and creating significant market advantages. The goal is to pushing your organization at the forefront of this technology revolution. Why is personalization so critical? Right now? It's moved beyond being market differentiator to strategic ethnicity. Now let's come to competitive advantage. Personalization has transformed into critical S ethnicity, redefining the competitive landscape of modern financial services. We are seeing that advanced machine learning algorithms specifically. XG Boost and neural networks enable real-time station engines that outperforms traditional segmentation by staggering of 65%. Now customer expectations. Today's digitally savvy customers don't want just a response to their needs. They demand intelligent financial experiences that proactively anticipate their financial goals and behaviors. This requires agile architecture, and we are seeing a PA driven microservices enabling contextual interactions across 15 plus touch points with sub 200 milliseconds response times. Now, let's come to business growth. The impact on business growth is clear and quantifiable. The data-driven personalization strategies consistently yield significant improvements. The 30% of higher retention rates a 40%. Increase in customer lifetime value and 25% more effective cross-selling conversations. In fact, re arm investment analysis shows 3.2 times re arm personalization technology investments within just 18 months. Let's come to the technical framework achieving this request. Robust technical foundation, we are talking about a scalable data lake architecture of five petabyte or more federated ML model deployment and real time event process capabilities handling over 50,000 transactions per second. This all needs to be done while supporting GDPR and CCPA complaints through federated privacy, preserving algorithms. Now let's look at the cornerstone of this hyper-personalization Customer. Data platforms are CDPs. This is the foundational layer. Actionable insights. The CDP transforms complex customer data into actionable insights. This means personalized recommendations that drive measurable engagement and conversion outcomes through mission learning, algorithms, predictive analysis, and real time station engines, unified customer view, A CDP is crucial for creating comprehensive 360 degree spective of your customer. This view synthesizes. Financial behaviors, preferences, and lifetime stage for strategic vision making. Utilizing N Entity Revolution techniques, persistent identifies, and cross channel attribute models. Data aggregation. It seemingly integrates structured and unstructured data from multiple digital and physical touchpoints to build rich customer profiles. This is achieved through API driven architectures, robust A TL process, and secure data and organization frameworks. Now let's come to complex architecture. Crucially. A strong CDP incorporates a complex architecture. This means implementing privacy by design principles with granular consent management, automated data governance workflows, and encrypted protocols. This shows regulatory complaints when enabling ethical data utilization. Think of the CVP as a central nervous system for all customer data enabling everything else we will discuss. Now, let's dive into the quantum aspect of this revolution and understand why it is a game changer compared to traditional ai. Traditional AI limitations traditional has served as well, but it has inherent limitations. It has bonded computational capacity with sequential processing. This results in single dimensional data analysis that often requires multiple iterations. Its performance ceiling is restricted by classical binary architecture and algorithmic complexity. Scaling linearly are at a rate that grows steadily but increasingly fast with problem size. This leads to limited optimization capabilities, especially in high dimensional feature spaces and it's resource intensive. Request extensive hardware infrastructure. Let's look into the quantum enhanced AI advantages. Quantum enhanced AI breaks these barriers it offer never seen before. Computational parallelism with ex exponential scaling capabilities. This means it can perform many calculations simultaneously, unlike classical computers. It for multidimensional pattern recognition across vast parameter spaces, handling complex data sets with. Is we leverage super position, enable simultaneously data processing analysis. A special quantum link where particles are connected and influence each other instantly. Facilitates complex correlation detection across financial data sets for optimization, a quantum computing method that finds the best possible solution. To complex optimization problems. By gradually reaching a stable state provides nearly instantaneously optimization of complex portfolio models. And finally, training time is significantly reduced through quantum accelerated gradient decent algorithms. The key here is the ability to handle vast amount of data and complex relationships at the speeds and scales are impossible for classical ai. So how does this all come together in your real time scenario data collection? It starts with capturing comprehensive customer interactions across digital and physical touch points in real time. To build dynamic behavioral profiles, we utilize encrypted a p streams and edge computing nodes with subfamily second latency to process over 10,000 data points per customer session. Quantum processing. Next, we leverage quantum enhanced algorithms to identify complex patterns and correlations that traditional computing would miss our process too slowly. This implements a 1 28 cubit tensor network architecture capital of analyzing multidimensional financial data across 500 plus variables simultaneously. Personalized delivery. Finally, these analytical insights are transformed into hyper-personalized financial recommendations delivered seamlessly within milliseconds of customer engagement. This deploys neuros symbolic reasoning engines with the 99.8% station levels and confidence levels, and adapt to mission learning models that refine with each interaction. Beyond just analysis, generative AI is playing a huge role in personalization personalized content. Generative AI can dynamically generate tailor financial education, materials and product recommendations. These evolve with each customer's financial journey and literacy levels. This utilizes transformed based neural networks with over one 75 billion parameters to analyze thousands of customer data points for precise content customization. Let's come to the A Power Chat bots. This enables sophisticated conversational experiences that seamlessly adapt to individual communication preferences while providing contextually relevant financial guidance. We implement retrieval argumented generation frameworks to combine real time data with T financial knowledge basis predictive offerings. Generat to a enhanced by quantum capabilities, proactively identifying emerging customer needs through quantum enhanced pattern recognition, enabling proactive service delivery that builds lasting loyalty. It leverages recurrent neural networks with attention mechanisms to detect slight or not obvious financial behavior shifts with 93% accuracy. Synthetic data generation. A crucial application is creating privacy, preserving financial data sets that maintain statistical properties of real data while completely eliminating PIA concerns. This implies differential privacy algorithms with Epsilon guarantees to enable robust model training while maintaining regulatory complaints across different legal or governance areas. Implementing the sophisticated system comes with significant data engineering challenges. It's not just about the quantum tech, it's about the infrastructure, low latency. We need to process financial data in milliseconds for real time customer experiences. This requires memory map database with non-blocking IO, strategic edge computing to reduce network latency and predictive caching file. Query optimization. High volumes. Managing petabytes of customer data without performance degradation is a paramount. Solutions include auto scaling, microservices and Kubernetes distributed using multiple types of data storage and realtime analytics via streaming processing. Security implementing quantum research encryption for financial data protection is no longer optional. This includes adopting post quantum cryptographic algorithms using zero knowledge proofs for identity verifications and homo graphic encryption. For security competition competence, we must balance regulatory requirements with the data accessibility needs. This involves graph based metadata for automated data lineage, dynamic attribute, basically access control and immutable. Cryptographic cardiac trials, balancing performance, security, and compliance requests, highly specialized expertise to support these advanced AI applications. Let's break down the specific quantum innovations driving this quantum algorithms. These are revolutionary computational methods that exponentially rate financial pattern recognition and deliver unprecedented accuracy in complex risk assessment scenarios. Quantum Machine learning. This involves state of art to predictive models, leveraging quantum super permission to simultaneously analyze thousands of customer behavior variables for hyper-personalized insights. Quantum cryptography, this is about the next generation security protocols. Utilizing quantum and properties, it creates theoretically unbreakable encryption that safeguards send to financial transactions and customer data. So what's the roadmap for an organization looking to adopt this assessment? Start with comprehensive analysis of your existing data architecture. Quantify your current personalization capabilities against industry benchmarks to understand your starting point integration. Next, deploy the customer data platform infrastructure and establish secure a PA connections with quantum enhanced processing systems. Testing, execute control pilot programs with strategically selected customer segments. This allow you to validate personalization, efficacy, and re refine algorithms in your control environment. Scaling. Finally, systematically expand quantum core personalization, capabilties across all products, offerings, and omnichannel customer touch points. The results speak for themselves when these systems are implemented Effectively, quantum AI implementation demonstrates measurable, written off investment across key performance indicators. 40% engagement increase. Quantum powered personalization tools are driving a 40% higher customer interaction frequency and duration across digital financial platforms. Technically, this is supported by 15 millisecond response latency and 99.8 percentile system availability. 25% conversion growth. AI driven product recommendations are delivering 25% higher acquisition and cross-selling rates through precisely targeted financial offerings. This leverages eight qubit processing array with a two 30 terabytes of customer behavior dataset integration. 35% satisfaction boost. Tailored customer experiences are producing a 35% improvement in net promoter scores, directly enhancing customer retention and lifetime value. This is supported by a 1 28 node distributed computing architecture with real time sentiment analysis capabilities. Furthermore, implementation metrics shows 99.97 percentile data encryption compliance with the 42 percentile. Production in computational resource requirements compared to traditional systems. To summarize the core message for today's presentation, CDP Foundation. Customer data platforms establish the critical foundation for hyper-personalized financial experiences. Integrated Data lake with a 360 degree customer profiles enable multi-dimensional segmentation and realtime ing quantum advantage. Quantum enhance the AI exponentially all rates pattern recognition for never seen before. Personalization, accuracy. Quantum algorithms process complex financial variables 200 times faster than the traditional computing approaches. Generative ai, advanced AI dynamically creates tailored financial guidance and seamless conversional interfaces. Our MLP models with over one 75 billion parameters achieves 98% accuracy in sentiment analysis and intent recognition. Engineering excellence. Finally, sophisticated data infrastructure ate real tech insights while maintaining regulatory compliance. A microservice architecture with a P first design enables 99.99 percentile uptime on sub 200 millisecond response times. Thank you for your attention today.