Hello everyone, I'm thrilled to have this opportunity to present on this critical and exciting topic at the Conf 42 DevSecOps 2024 conference. Leveraging AI in fintech, revolutionizing security, compliance and automation in DevSecOps. Artificial intelligence is a game changer in financial technology. enabling transformative changes in areas like fraud detection, personalized financial services, and regulatory compliance. so today we will discuss how these technologies are reshaping the industry and dive into both the opportunities as well as the challenges that they present. So before we begin, let me introduce myself. So my name is Sudeep Meduri. And I have had the privilege of working closely with AI solutions in the fintech domain for the past couple of years. So this session is mostly based on my experience working in this field. let's, dive in. here is an outline of what we'll cover today. so mostly we'll start with an introduction to AI and fintech, where we set the stage by discussing why this topic is so relevant and transformative. next we'll dive into key, applications of AI, including fraud detection, Personalized Banking Services, Algorithmic Trading, and Regulatory Compliance. We'll then explore the broader impact of AI on FinTech, looking at how it drives operational efficiency and innovation. Following that, we'll examine the challenges and ethical considerations, including issues like data privacy, bias, and workforce dynamics that it brings up. Finally, we'll wrap up with the conclusion and some actionable insights for leveraging AI responsibly in fintech. Let's begin with an overview of AI's role in fintech. So right now, AI is no longer just a buzzword in the fintech industry. It's a powerful tool that's redefining how financial services are delivered to the end user. So why does all this matter? Financial institutions deal with vast amounts of complex data and AI helps with streamlining this process, reduce inefficiencies and enhance the overall customer experience. So the current trends indicate that financial fintech companies and also traditional financial institutions both alike are adopting AI at a very rapid pace, and it's a necessity to stay competitive, meet evolving regulatory requirements, and reduce operational costs. today we'll explore how AI is driving this transformation, focusing on both, its benefits and the challenges we must address, to harness its full potential, responsibly. AI's impact, on FinTech is very profound, and its implications can broadly be categorized into four different areas. so the first one is fraud detection, AI Excel set analyzing transaction patterns in real time. it helps with identifying potential fraudulent activities with greater accuracy than traditional systems. In fact, fraud detection is one of the major use case of AI in many of the big tech companies. it also provides, plays a major role in personalized banking. so through advanced analytics, AI can provide tailored financial advice. making these services, more accessible and improving customer retention over time. it is also becoming prominent in algorithmic trading. it enables the vast analysis of vast market data to forecast trends and optimize trade execution. one example is the use of deep learning models in stock, to predict, stock, trends over a period of time. and lastly, compliance. AI automates the compliance process. it can ensure financial institutions stay updated with ever changing, regulations, which in turn can reduce, costs and, compliance risks. we'll discuss each of these applications in more, detail, over the next, few slides. so the fraud detection has always been a major challenge for the financial industry, but AI, provides groundbreaking solutions here. It helps with real time analysis. AI can analyze millions of transactions instantly to identify anomalies and flag potential fraudulent activity. it, can also be a useful tool for pattern recognition. ML models continuously evolve, recognizing new fraud patterns that traditional systems might miss as you keep training these models more frequently. It can also significantly impact financial security. It helps reduce losses, minimizes disruptions for genuine transactions, and helps build consumer trust. One example is a prominent multinational bank which implemented an AI driven fraud detection system. And it cut false positives by almost 60%, which not only reduces the cost, but also significantly improved customer satisfaction. personalization is also very key to modern customer, engagement and AI is one of the main driving forces behind it. it can be useful to provide tailored financial advice. It can assess individual spending habits, goals, and income. to offer customized guidance to, end users it, AI powered robo advisors, also democratize investment management, making financial planning affordable and accessible, to all, classes of the society. It also provides customer portfolios. AI considers unique factors such as ESG preferences, tax implications, and personal goals to build highly personalized portfolios. The key impact is that this leads to greater customer satisfaction and deeper engagement as customers feel that their financial needs are properly understood and prioritized. Algorithmic trading is another example of AI enhancing profitability and market efficiency. so market, AI can, analyze last data sets in real time. it helps identifying patterns and opportunities for optimal trade executions. so as we've discussed earlier, deep learning models can also help with predicting, stock trends by analyzing historical and current data, and enabling smarter investment strategies. AI also optimizes trade timing and execution, reducing transaction costs and enhancing overall profitability. There's a point of caution though here, while AI improves market efficiency, it also raises concerns about market instability caused by these automated trading strategies. Regulatory compliance, is another area where, AI, sorry. Regulatory compliance is another area where AI has made significant strides recently. AI also helps with automating, data collection, processing and reporting, reducing human error and improving accuracy. it helps with adapting to, various governmental regulations. ML models can track changes in regulatory requirements and update compliance processes automatically. AI also provides predictive insights and continuous monitoring. helping institutions manage these risks proactively. For example, some institutions using AI driven regulatory tech have reduced their compliance cost by up to 30 percent while improving the overall efficiency. So the broader impact of AI on fintech extends not just beyond these specific applications, it can lead to operational efficiencies, it helps reduce processing times, human errors, and operational costs driving productivity gains. It also provides better customer experience, can provide personalized 24 7 service through AI powered tools, like chatbots, which help redefine customer expectations. AI can also be a catalyst for innovation as it enables new financial products like alternative credit scoring models and blockchain based solutions. For example, AI chatbots routinely handle customer inquiries now, Freeing human staff to focus on complex issues, improving both their satisfaction, customer satisfaction as well as efficiency. Despite all the benefits that we have discussed so far, AI in FinTech still presents very critical challenges. The first one is data privacy. As everyone knows, financial data is very sensitive. It requires strong safeguards and adherence to regulations. One example is the GDPR that came up recently. There can also be the challenge of algorithmic bias. The poorly designed AI models can perpetuate biases, especially in credit scoring and loan approvals. Automation can also displace jobs, which creates an urgent need for workforce reskilling. And finally, when it comes to ethical AI, Transparent, unbiased, and secure AI models are very much essential to maintaining trust and ensuring ethical outcomes. To wrap up, AI is revolutionizing financial services by enhancing fraud detection, personalization, trading, and compliance. These advancements drive operational efficiency, innovation, and financial inclusion. However, challenges like data privacy bias require careful management as well. the FinTech industry ultimately must prioritize ethical AI development, robust data protection, and workforce upskilling to ensure these technologies serve the society, as a whole. thank you, for your time and attention today. I hope this, presentation provided valuable insights into the role, AI plays in FinTech. thanks again. Bye.