Hello, everyone. Thanks for having me here today. My name is Sudeep Meduri, and I'm delighted to present on this exciting topic, transforming fintech using AI, revolutionizing security, personalization, and compliance. Artificial intelligence is reshaping how financial services operate today. Making them smarter, safer, and more inclusive. So in this talk, we'll explore how AI drives innovation in key areas such as fraud detection, personalized banking, trading strategies, and regulatory compliance. We'll also dive into its potential to redefine operational efficiencies. and customer experiences while addressing ethical and regulatory challenges. So let's get started. Fraud detection is one of the most impactful areas where AI has made significant inroads. Traditional fraud detection methods used to rely on rules based systems. that were rigid and slow to adapt. AI, however, can identify anomalies within vast amounts of financial data with unparalleled accuracy. let me point an example here. the JP Morgan's coin system analyzes over 12, 000 commercial loan agreements in seconds, saving almost 360, 000 hours of manual effort. AI also uses Pattern recognition to adopt new tactics by fraudsters, continually learning and improving its responses. These advancements have reduced false positives by over 60 percent and also helped increase the true positive detections by almost 50%. It provides enhanced security by reducing fraudulent activities. And ultimately, it helps with customer confidence, increasing trust, and encouraging wider adoption of digital financial services. Another area where AI has played a significant role, is in personalization. So personalization is at the heart of modern customer experiences, and AI is making it possible to personalize individual user experience at scale. this is done by analyzing spending habits, income patterns, and financial goals, and AI is able to deliver tailored banking advice to each individual user. We also now have AI powered robot advisors, Like wealth front and betterment who offer customized financial planning and portfolio management at reduced costs. these platforms offer customized, investment strategies, which are previously accessible only to high net worth individuals at a very significantly reduced cost. Further, ML algorithms create customized investment portfolios aligned with individual risk appetites and ESG preferences. So this not only improves customer satisfaction. but also deepens loyalty and long term engagement with financial institutions. Personalization also ensures every customer feels understood and valued, which is a very key differentiator in today's competitive landscape. let's now look at algorithmic trading. So this is another area where AI has shown its transformative power, in financial trading markets where even milliseconds matter. AI provides an edge by analyzing vast amounts of data, including historical trends, price movements, trading volumes, and news feeds. deep learning models enhance trend forecasting accuracy, helping traders make informed decisions. Additionally, AI also optimizes trade execution by analyzing market liquidity to minimize costs and, market impact. firms leveraging AI have, reported increased profitability and improved market efficiency. Making it indispensable in fast paced trading environments. For example, firms like Citadel have heavily invested in AI, which provided them a competitive edge over their competitors. Let's now look at the role AI plays in regulatory compliance, which is an area which is very ripe for description, given the complexity and volume of financial regulations coming up day by day. AI powered compliance tech solutions can automate a lot of the data collection, processing, and analysis tasks, reducing manual effort and also errors. ML models are able to track regulatory changes across multiple jurisdictions, interpreting new rules and assessing their implications. Predictive analytics adds another layer by identifying potential compliance risks before they even materialize. And by streamlining compliance, AI not only helps reduce costs, but also mitigates reputational risks associated with non compliance for these financial institutions. Operational efficiency is another critical priority for a lot of financial institutions and AI has been delivering on this front as well. it helps automate routine, financial tasks, which helps reduce errors and also reduce processing times. AI chatbots handle customer queries 24 7, significantly reducing response times. Similarly, NLP technologies streamline back office operations by extracting key information from unstructured data in financial documents. And by automating a lot of these repetitive tasks, institutions can now redirect human resources to roles that truly require creativity and expertise, ultimately reducing costs while also improving service quality. AI is also redefining what customers expect from their financial service providers. we now have services such as voice activated banking, real time financial insights, and personalized recommendations, which are no longer optional. they are expected now, for each and every individual user. For instance, digital assistants like Bank of America's Erika provide seamless and intuitive interactions guiding users through complex financial tasks. AI powered chatbots are also able to offer round the clock support and financial guidance for users. Innovation is central to the evolution of financial services, and AI is at the forefront. AI powered credit scoring models provide a more accurate and inclusive assessment of borrowers, which expands financial access. AI also supports risk assessment and market analysis by processing and synthesizing vast amounts of data sets. RoboAdvisors and AI powered insurance products some of the other examples of how AI is creating new categories of financial services, making sophisticated tools accessible, to a broader audience. So, while, the benefits of AI in FinTech are immense, many challenges, still remain, data privacy and security are, one of the major concerns, especially with, regulations like, GDPR, et cetera. Institutions must also implement robust cybersecurity measures and transparent data practices to safeguard customer trust. Another challenge we see is algorithmic bias, which can lead to unfair outcomes in credit approvals or loan disbursements. It's very important to ensure fairness and that requires diverse training data sets and regular audits of these AI systems. Transparency and explainability of AI models are also equally critical to maintain user trust and regulatory compliance. So let's now look at what the regulatory landscape looks like and the challenges that it can bring. As AI is more adopted in FinTech, regulators face the daunting task of keeping pace with AI advancements. Adaptive regulatory frameworks that encourage innovation while mitigating risks are very essential. for listening. Algorithmic accountability will also likely become a regulatory mandate, requiring institutions to explain AI decision making process, and data privacy regulations are also expected to become stricter than what they are now, focusing on how AI systems manage personal financial data. There are already data localization laws in various countries which mandate that the data that is used doesn't leave the boundaries of the country. And these are only going to become more strict going forward. And finally, ethical guidelines will also play a key role in ensuring AI is used responsibly, balancing technological progress with societal well being. So I hope this presentation has shed light on how AI is transforming fintech, addressing some of the challenges, and paving the way for a more innovative and inclusive financial ecosystem. Thanks for having me. Thanks everyone for your time.