Welcome all for conference 42, prompt Engineering, 2025 online. I'm Swami Viro, working Vice President of Intelligence and Mission Learning at JP Morgan and Co, where I led in the intersection of advanced AI technology and financial systems with deep automation. I am dedicated to modernizing investment banking process, driving innovation, and ensuring data driven mission making. Today I'll be speaking about transf role of VE augmented generation agents in finance, and how intelligent prompt automation is. By streamlining workflows, enhancing data quality, and enabling smarter, faster operations. Let us discuss about critical infection points. So like mainly we have three infection points. One, data fellows and manual process and real time risks. Let us dive deep into these three things. The financial services industry faces unprecedented challenges in maintaining data consistency, regulatory complaints, and operational efficiency across increasingly fragmented systems. Traditional data management approaches can no longer sustain the velocity, volume, and complexity of modern markets. The IM of retrieval Augmenting generation system combined with MultiGen architectures represents a fundamental shift in how financial institution process information make decisions. These systems demonstrate measurably improvement across critical operational dimensions while addressing core challenges that help plant investment banking for decades. Okay, now let us talk about the data consistency crisis. So today, like we know. 10 million plus daily transactions, 70 and hundred plans and continuous operation of system 24 by seven. Investment banking operations sell evolved into extraordinarily complex ecosystem where data flows through numerous systems, platforms, and jurisdictions. Simultaneously, major investment banks process tens of millions of transactions daily with peak periods reaching exponential higher volumes during market volatility. Take daily transactions processed across multiple systems, requiring perfect consistency, integration points between front, middle, and back office platforms. We need perfect integration, continuous operations, global markets demand around the clock process. Each transaction generates multiple data points that must remain consistent across front office trading systems, middle office risk platforms, and back office settlement infrastructure. Traditional batch processing introduces latency. That creates windows of inconsistency while real time interaction attempts struggle with computational demands. Okay, now let us start, like in existing system, what are the complications with human cost? So mainly with the. Mainly data quality issues, manual reconciliation process, and regulatory requirements. That is dive deep data quality issues, combine integration challenges, information originates from driver sources, live internal trading systems, external market data vendors, regulatory reporting platforms, and counterparty communications. Each with different formats, update frequencies and quality standards. Highly scale provisionals spend significant time identifying discrepancies, researching root causes, and implementing corrections. What that adds limited strategy value, but remains essential for operational integrity. Manual reconciliation process cannot scale to vinyl modern transaction volumes while maintaining competitive speed. Even small discrepancies cascaded into significant problem when undetected across multiple systems. Regulatory requirements are another layer demanding detail, audit trails, data lineage, proof, and complaints across multiple jurisdictions. Okay, now let's discuss about what is the architectural foundational and how multi-agent system works. In this diagram. So query means like any trade, maybe it is in the different formats and it interacts with lm and before interacting LM it going to get its context from Vector database and after that and going to work with different agents there. State processing agent risk assessment, compliance monitoring, or settlement de can. So let us dive deep. So in framework, enhance language model capabilities by connecting them into authority to data sources rather than relaying solely and embedded training knowledge. When process inquiries about trade settlement status, the system reduces relevant information from transaction databases, settlement platforms, and counterparty communications before generating responses. This approach ensures accuracy, provides a trails. And allow system to work with up to date information. Extending beyond model training data. Multi-agent architecture, intelligent distributes across specialized components rather than monolithic systems. Individual agents focus on specific domains, trade processing. Monitoring or settlement reconciliation. Developing the expertise while coordinating to solve complex problems. Agent coordination protocol enables sophisticated workflows through well defined interfaces that maintain modularity while handling complex negotiations and optimizations. So all with the LMEs, like after that. So it's gonna mainly focus on semantic search and vector databases. So in this like in semantic search and vector database, we mainly have this components like document, organization, vector emits. Seman and contextual understanding. So for this document, organization, regulatory frameworks, trading policies, market data, and historical transaction, organized for such while reserving relationships second vector embeddings. Content transformed into mathematical representation that captures semantic meaning and context semantic criteria, contextual relevant information even without exact keyword matches. Finally, contextual understanding system handles financial terminology, variations across. Causes and time periods. This semantic understanding was crucial when dealing with financial technology that varies significantly across different contexts. Enabling the system to find relevant information regardless of how queries are phrased. Okay, now let's talk about the microservice architecture of lys. So we use microservice architecture everywhere, but it is pretty much similar. What we use some orchestration like a p, a gateway and different A components interactive here. So mainly the microservice architecture supporting AA capabilities provide the scalability, reliability, and flexibility demanded by production, financial systems, individual services, and specific functions, and scale independently based on demand, independent scaling services, scale based on demand. Massive transaction volumes during peak period while maintaining cost efficiency during normal operations system resilience. Service isolation ensures issues in one component do not cascade into systemwide failure. Next, security by design. Access control logging built the foundation with comprehensive. Okay, so next important topic is. So like we have data engineering. So first one, data source. Like for every brand, we need data source. Here in this investment banking, we use market data, internal reports, or regulatory documents, and we can use this as a context and prompt prepares the templates and intent classification and prompt optimization. So every year, lamp just need this prompt. Process the engineering prompts and gives the output in different format, automated compliance, monitoring, recommendation, and customer reports. So what are the core benefits with this brand? Engineering, mainly enhanced accuracy. Engineered prompts reduce hallucination and ensure domain specification. Second, decision making data interpretation and report generation Third, regulatory consistency, embeds complaints, both operational efficiency. Three professionals high value to us with scalable intelligence exchange, AI insights across this and client operational securely. Okay, now let us see how the realtime trade process works in the ai. Okay. So from execution to settlement in milliseconds. So we know that like in the current world, like so based on the trade type, it may take three plus one, or three plus three or three plus seven or three plus 15, or even sometimes it goes to plus 30 to settle. So in that way now it is reducing to millisecond. So it's a data achievement actually, when you trade executes a complex structured product. Intelligent agents automate workflows that previously required extensive manual intervention. The system immediately validates state economics. Checks, credit limits, regulatory complaints, and in shared settlement process. All within milliseconds of trade execution. Trade execution. Structured product is executed by trader and instant validation, close economics, credit limits, and complaints Verified. Finally, auto settlement process initiated automatically. Realtime reconciliation performs continuous comparation of trade details across systems, immediately flagging discrepancies for investigation. This immediate detection prevents small issues from combining into larger problems and dramatically reduces the operational burden of reconciliation teams. Okay, now let us discuss about intelligent reconciliation. So in Reconci we mainly talk about the traditional approach and a based approach. There is a lag to load this presentation. So mainly batch crossing overnight in the traditional approach and also, undetected discrepancies, numerous false positive from format variations, manual investigations of every PLA and delayed problem resolution. So all these things happen with the traditional approach. So with the a enhanced approach, we can achieve continuous severe time reconciliation, India discrepancy detection, contextual understanding of equivalence. Of false and prevention, so Amm system understands contextual, recognizing that various property names, settlement dates adjusted for already are minor price differences within tolerance for sure. Don't represent two discrepancies. This intelligent reduces false positives substantially allowing reconciliation teams to focus on genuine issues requiring investigation. Okay, what are the advanced risk assessments? Okay, advanced risk assessment, like mainly we're categorized into historical analysis position, Mandarin correlation detection and emerging risks. Okay. Risk assessment capabilities, benefit and LY from a system that rails and across vast amounts of historical data, current position, and market conditions. Risk manage systems that apply Redfin to current positions, AI and systems, identifying emerging patterns, correlations across seemingly unrelated positions and potential risks that don't fit standard models. Okay, next, historical analysis. Across of market data. Second position wondering realtime exposure assessment across portfolios, relation detection, identifying hidden relationship between positions, emerging risk, identification of non debt. The system continuously monitors market conditions, analyzes historical and assist portfolio exposure across multiple risks dimensions, simultaneously enabling more aggressive risk taking within appropriately limits, while maintaining prudent risk management. Where do complaints monitoring? Okay, here we have four different ways of complex monitoring, one regulatory tracking, pattern analysis, impact assessment, and proactive prevention. Complaints monitor, react to risk management rather than simply flagging transaction that violates explicit rules. A systems identifi patterns that suggest potential complaints issues before violations occur. Regulatory tracking. Monitoring of regular changes across jurisdiction, pattern analysis, identifying behaviors that suggest potential issues, impact assessment, evaluating how changes affect current strategies. Prior to prevention addressing issues before they became violations. The system understands regulatory requirements across multiple jurisdictions. Facts changes, and assesses how those changes impact current creating strategies and positions. This proactive approach allows compliance teams to orders potential issues before they become violations significantly reducing regulatory risk. Okay, now let us talk about performance at scale. So production system characteristics are like transactions per second peak processing capacity during market volatility should be like 1 million plus transactions per second, and system availability. Finance up time with automated failover. Is subsequent performance for interactive operations, the microservice architectural individual components to scale independently directing resources to bottlenecks services during peak periods while maintaining cost efficiency during normal operations. A. Continuous health monitoring ensures finance availability that financial operations demand trade validations occur within milliseconds of execution. This calculation complete quickly after support real time position, monitoring compliance, checks operated, trading speed without becoming bottlenecks. These aggress seal latency requirements demand careful optimization of every system component. Okay, let us talk about security and compliance implementations. We have mainly six topics here, like data protection, explainability, access control, model validation. Logging incident response data protection number one, comprehensive encryption covering data addressed in transit and in use. Sensitive financial information, protected external threats and unauthorized internal access control pipeline permissions with role based and tribute based controls. Dynamic authorization conditions based on context and risk level audit logging, comprehensive trail captures every system interaction. Secure logging infrastructure prevents tampering with audit regards, explainability. Here rationally for every a edition that human reviewers can understand and validates transparent edition making at mission speed model validation. Ongoing monitoring ensures a components continue operating correctly as markets evolve. Maintain system effectiveness. Finally, in general response, detection of ous behavior, automated containment and response to prevent escalation. Okay, now let's talk about miserable business impact. Here we will talk about four different things like cost reduction, efficiency gains, and error, and steady flexibility. Cost reduction, automation reduces manual effort for road in operations. Annual growing transaction volumes without proportional staff increases efficiency gains complete in minutes. In short of hours. Faster confirmation and settlement. Improve client service and comp to advantage error. Pure operational errors means less correction time, reduce settlement failures, and improve regular standing. Next, strategic flexibility content to portion new opportunities and new markets and launches products with operational infrastructure that supports growth. Financial institutions implementing these technologies report substantial operational improvements. The efficiency gains allows handling of growing transactional volume, minimal managing cost, transforming the economics of expansion, and enabling opportunities that would be economically unviable with traditional approaches. What is the path forward? So fundamental transformation of financial operations. Rad, enhance multi agent A. Systems are funda, transforming investment banking operations. This goes beyond automation enabling system that handle complexity. And speed The traditional approaches. Current state miserable through cost reduction, efficiency and risk management. Near firm evaluation, cost implementation, reduce risk, and expected capabilities, future potential. Autonomous operations advance reasoning and risk prevention. As a becomes essential financial institution must prioritize systematic. A adoption organizations committed to this transformation will thrive in increasingly competitive and technologically sophisticated industry. Thank you for joining us Agents in Finance, transforming Investment Banking through intelligent automation using prompts. I appreciate your engagement and thoughtful questions, and I'm grateful for the unity to share this exiting advancement with you in this conference. 24 2025. Feel free to reach out me in my LinkedIn Swami video. Thanks again.