Hi everyone. Hope everyone is doing great. I'm Ja Krishna having more than 14 years of experience in IT industry in implementing the high volume, high performance, large scale investment products using databases like Oracle Cyber, snowflake Cloud, stuff like Azure. A-W-S-D-V-T have a strong expertise in front back trading applications on. How they work, how users performs their reconciliation process for the regulatory and reporting needs. As part of my job responsibilities, I was primarily involved in building up multiple application using on-prem and hybrid and cloud technologies with very strong domain knowledge. Today we're gonna talk. Cloud-based reconciliation system and investment banking. Let's get into that. So the investment banking industry operates on very massive scale of data processing like millions of transactions daily. I. Accurate and timely consideration of financial data is essential for compliance, risk management, and decision making. This presentation will explore the transmitted power of cloud-based reconciliation applications and the impact in the industry. First, I wanted to just say few challenges about the traditional reconciliation process. So traditional reconciliation process is time consuming where there's so much of manual effort. While performing data analysis, preparation of sheets, initiating the data, and then performing that matching of the transaction. And I, in fact, the dispen and preparation of reports, everything is time consuming because most of the things are manual and this can lead to. In closing the periods, one is human error. Any kind of manual intervention increases the scope of error like data entry, mistakes missing some transaction, incorrect data matching. These error can result in discrepancies and potential. To inaccurate financial statements. Next, this joint systems legacy systems often work in isolation where different departments or outside partners use separate softwares. This fragmentation makes it difficult to get unified view of financial data. And delayed information. Legacy reconciliation process mostly operates on batching of the data updates, which means financial data is not real time, which results in not making decisions on time without, and they have to perform the reconciliation without data. Information and difficulty in audits. With all these problems like daily data, human errors, design systems there is more chances of failing in audits, which again redo all the process, filling kids a course time. So this is the current challenges which currently legacy systems are facing. So I'm gonna talk key features of the modern reconciliation. One is automated data matching. The cloud-based platforms automate the process of matching transactions from multiple sources such as banks, counterpart, custodians, trend, tradition, and some trading platforms. The system. Internal records with external records like ledge systems and bank systems, some statements and it flags any kind of expenses for further review. By setting up some rules and real time data synchronization, cloud technology allows. Data to be seen and updated in real time. This means that as invent transaction hackers, they're immediately reconciled with the corresponding records from other systems or tions. It significantly speed up the reconci concession process and integration with multiple systems. So cloud-based reconciliation platform can seamlessly integrate with various internal and external systems. Such as, so systems accounting, softwares, custodian devices, which provides a unified view of financial transactions at one place. And finally, reporting analytics. Cloud-based systems typically have powerful reporting tools and analytical capabilities. These tools allow forms to generate comprehensive reports on their reconciliation process and spot potential issues early or very near real time. These are few core components of cloud-based reconciliation architecture, which we can talk about now. So one is data addition layer. The system employs advanced APIs and connectors to ingested data from multiple sources, including trade confirmation, settling, transaction, and market data. Ted, the next. Step is transformation engine. So this ETL process standardize, andries the ingested data through automated workflows. And once the data has been transformed, as per the business needs, the reconciliation engine kicks off with high performance matching engine employees, the configurable rules and algorithms to perform multi reconciliation across the data sources. Once the reconciliation engine finishes his. Reconciliation process that the final step is reporting and analytics. The reporting layer leverages the business intelligence capabilities to generate real time dashboards, regulated reports, and predictive analytics that drives operational improvements. So these are some technical features and performance mat metrics, which we came across. So when we implement past three years on before implementation, what was our thing? And after implementation of cloud-based reconciliation what was our metrics. So it, it includes three years exception handling time. Which came down from th around 360 minutes, which is six, six hours to 18 minutes. So we can see the improvement and automatic batch rate, system availability. We know that cloud system have. 99.99% down to system availability where we can have region specific SY systems configured and then system up time, everything. It's that technical architecture leverages microservices with so many descriptive services, each maintaining independent scaling capabilities with guaranteed uptake. And these are. How the data quality. Quality of data is really important in any of these reconciliation systems. How we perform the data quality over here. First you perform data validation. So new rule network based engines achieve 99.8% accuracy while processing, or 15 million transactions per second. And then standardization, quantum resistant encryption enabled secure standardization across. 300 plus different source systems monitoring realtime system detect lys with 99.95. Nine six accuracy with 50 microseconds Adoption. Reinforcement learning reduces implementation time from weeks to just four plus hours. The process automation segment in financial reconciliation has grown. So much between 21 to 23, reaching market value of 89.5 billion. These are some stats, which we got it from multiple systems and regulatory compliance and automation. Automated reporting processing over 5.2 million regulated reports daily across 1 32 due with almost a hundred percent accuracy, reducing complaints, relative operational cost with 78% documentation management, advanced MI algorithms. Process 12.3 petabytes of compliance data daily with travel time, average 35 milliseconds and maintaining a hundred percent accuracy and emissions. So even if you see audit trials. Our blockchain based systems enable immutable record keeping of 15.7 billion daily events with a guaranteed detention period of 10 years. Instant access for regulator inquiries. So these are some more metrics about scalability and performance capabilities in cloud-based reconciliation system. So at a peak we can get like 12.8 million transaction process per second during peak up peak covers where so much data is being streaming. And even during that time we have 99.9999. Percent system up and more than 5.4 million conent. You just can, be supported across all over the globe, which is 1 4, 7 countries. And the system latency is like 12 milliseconds, maintained even at 25 million transactions per minute. This modern installation platform utilizes quantum inspired algorithms have achieved. Super scalability. Elastic computing resources automatically provision across 27 global regions within 800 milliseconds of detecting the demand spikes while maintaining sub millisecond latency. So when it comes to implementation, operation considerations, so when we use cloud infrastructure. Almost ev. Almost every institution now is erupt into the current cloud and then hybrid multi-cloud strategy, and then user training, 96% certification rate within first quarter of the deployment. Disaster query. Active configuration across five geographic regions. Support structure. A augmented systems resolve 92% of incidents automatically. Yeah. Financial organizations maintain an average of 2,840 cloud native application with reconciliation platforms requiring integration across hundred and 67 different systems. And 89 external services delegate. 18.5% of the Total IT budget for the transformation initiatives with change management programs typically involves 1500 thousand end users across 45 countries. So let's think about our future directions. Yay, artificial. In the next generation, AI transforms. Reconciliation through intelligent pattern recognition methods advanced a complex matching while. Real time anomaly detection prevents errors before the impact operations, reducing manual intervention by up to 85%. And then the blockchain technology enhances financial reconciliation by providing a decentralized ledger. This increases transaction transparency and fraud production, and data analytics and visualization. Real time analytics dashboards unlock. The actionable insights from reconciliation, data, interactive visualization, spotlight trends and bottlenecks, while predictive model focus, potential issues enabling proactive optimization of reconciliation workflows. Let's conclude. So of after looking into all these stats, going through multiple comparison between legacy and reconciliation legacy and future based and con cloud based reconciliation application, it has. A revolution moment for investment banking operations by introducing sophisticated automation, enhanced accuracy, and robust complaints mechanisms. These systems have demonstrated their capital to handle complex reconciliation process while maintaining. High levels of security and operational efficiency. As the financial sector continues to embrace digital transformation, reconciliation platforms will evolve further in Inc. Incorporating emerging technologies and addressing new challenges in the investment banking landscape. The successful implementation of these solutions has proven crucial for maintaining data integrity, operation efficiency, and competitive advantage. Thank you so much for giving me this opportunity. Hope this gives you some insight on how current cloud-based reconciliation, investment banking evolves, how it'll be in the future. I hope you have a very wonderful day. Thank you.