Hey, hello, everyone. myself, I am working as a software developer, metrics it solutions, located in Charlotte and see, today I'm going to talk about data warehousing architecture, for enhanced financial reporting, and the impact of, DW architecture, financial, financial reporting, provides, actually the expression of how financial institutions leverage data warehousing to or does the complexity of modern, financial, reporting. Brings with historical prospective on the evaluation of data warehouses transitioning from traditional on premises architectures to modern cloud based hybrid solutions. The focus shifts to examining the critical architecture. components, including dimensional modeling techniques like star and snowflake schemas and ETL processes designed to efficiently extract, transform and load the data from diverse financial systems. The discussion, emphasizes the importance of integration, business intelligency tools into data warehouse to enable advanced analytics. real time reporting and, enhanced decision making capabilities. Key, the key considerations such as regulatory framework ensure adhere to, financial regulations, like, GDPR and SOX, and also emerging, technologies such as cloud computing, a EA, and blackchain, are presented as the transformative forces, enabling, greater scalability and, data pricing and secure financial record keeping. This, presentation also highlights the miserable, benefits, derived from, modern data warehousing, including improved reporting accuracy, operational efficiency, and co. to real world example and case studies. It also demonstrates how financial institutions have leveraged these architectures to gain strategic advantages, streamline reporting and meet the regulatory. demands attendees, will gain actionable insights into designing scalable, secure and future ready data warehouses, tailored for the dynamic needs of the, financial sectors. And, I'm going to, the evaluation of their data, warehousing, in financial, reporting, um, like, in 1990s, a remarkable journey of technical advancement, aligning with the growing complexity and, demands of the financial sector. In 1990s, our data warehousing systems were functional, designed primarily for batch processing and basic reporting tasks. these systems serve as a centralized repositories to store and manage historical financial statements, and transaction records and, account reconciliation data. Their primary objective was to facilitate essential regulatory reporting and provide a concerted view of financial information, albeit with limited analytical capabilities. whereas in, coming to two thousands, the scope of data warehousing expanded significantly with the integration of advanced, BA tools and these, tools, empowered financial institution to perform risk modeling, portfolio analysis, and, automated, compliance monitoring and, transforming, data warehouses into critical. Enable of strategic decision making, data mining techniques, further advanced fraud detection mechanisms, and also enable detailed customer segmentation, paving the way for personalized financial services and improved operational efficiency. In the present era, the adoption of cloud based data warehousing has revolutionized the financial industry. These modern systems offer real time analytics, AI driven insights, and seamless integration with regulatory reporting frameworks, hybrid architectures combining on premises and cloud capabilities. enable, distributed processing of massive data sets, ensuring scalability and performance while upholding role of data warehousing in driving innovation, enhancing efficiency and ensuring the regulatory adherence within the financial, sector. And, the architecture components of financial data warehouses are, meticulously designed to manage complexity, of financial data and, support diverse business needs at the foundation, data sources. which encompass transaction processing systems, external market data feeds, and regulatory reporting platforms. These sources serve as the, origin of, raw and, capturing transactional details and, marketing trends and compliance related information essential for financial operations. Data from these sources is ingested into staging areas, which acts as a temporary storage location. Here, the data undergoes rigorous validation, cleansing, and transformation to ensure consistency and accuracy before being loaded into the central data warehouse. This step is crucial for eliminating errors and standardizing data format across the systems. The heart of architecture is Core Data Warehouse, a centralized repository, designed to store historical data and maintain dimensional models. This repository provides a single source of, truth enabling institutions, to perform in depth analysis and, trend identification, provides a single source of truth enabling institutions. the data is typically organizing. Always using techniques like, star, snowflake schemas to optimize, acquiring an analytical, performance. Now I'm going to talk about, business intelligency information. within the financial data warehouse, plays a pivotal role in transforming raw data into actionable, insights, driving, informed, decision, making across financial institutions. At the forefront, as a, real time dashboards, which leverage advanced BI tools to provide, interactive and dynamic visualizations. These dashboards allow financial teams to monitor key performance, indicators, risk matrix and market positions in real time. enabling them to track portfolio performance, training activities, and other critical operations. The intuitive nature of these dashboards ensures that extra complex data is presented in user friendly format, facilitating quick and accurate decision making. In addition to real time monitoring, ad hoc reporting capabilities empower Analysts to generate a highly customized, financial reports on demand and the reporting features, provides a flexible self service interface, enabling, users to drill down into transactional level details, explore trends and produce comprehensive report tailored to specific regulatory requirement. And, the agility is individually reduces the time. required for report generation, allowing institutions to respond swiftly to internal and external demands. And also, another transformative aspect of BI integration is predictive analytics. analytics powered by, AI and machine learning algorithms. By analyzing historical data, and identifying patterns, these tools forecast, more proactive decision making and predictive capabilities, enable financial institutions to optimize investment. strategies, enhance, risk management process and anticipate market changes and positioning them for sustained success in a competitive environment. Together, these BI integrations, elevate the functionality of analytical data warehouses, fostering a culture of data driven decision making, operational efficiency, and, strategic, foresight, and maintenance to the regulatory completion, compliance considerations. These are integral to design operation of financial data warehouses, ensuring that institutions meet the stringent legal and ethical requirements while safeguarding data integrity. A key aspect is data lineage, which involves establishing end to end tracking system to document the origin transformations. And the movements of the data, this comprehensive approach provides a transparent audit trails that support a regulatory reporting and demonstrate adherence to compliance maintained by maintaining a clear line is financial institutions can quickly trace and resolve discrepancies and ensuring the accuracy of the report. equal important is data governance, which enforces, rigorous controls over data quality, access and security. These governance, frameworks ensure that financial data remains accurate, constant, consistent and protected against breaches or unauthorized, alterations. Measures such as a role based access control, encryption and real audits are implemented to uphold the confidentiality and maintain confidentiality, with industry, standards. The accessibility of data is another critical consideration. is requiring a careful, balance between rapid retrieval capabilities and robust security protocols. This, dual approach ensures that regulatory, reports can be generated efficiently without compromising the security of sensitive, financial information. By implementing, multi layered authentication and, secure access protocols. Institutions can prevent unauthorized access while streamlining the report process. Finally, compliance, with international regulatory standards is a cornerstone of financial data warehousing. Requirements such as Basel, capital adequacy rules, Dodd Frank's reporting mandate and GDPR's data protection guidelines, necessitate the integration of automated monitoring and validation systems. These systems continuously, evaluate data for accuracy and, compliance. reducing the risk of penalties and ensuring, alignment with the evolving, regulations. Together, these concessions, form robust, framework that enables financial institutions to meet, regulatory demands with the, confidence, ensuring transparency, security, and, operational efficiency. Now I'm moving on to design, design of, data warehouse for financial systems. the design of data warehouse for financial systems is carefully, orchestrated process, that ensures seamless data flow from source systems to analytical applications while uploading integrity, security, and regulatory complaints. in its core, dimensional modeling plays a vital role, employing star and snowflake, schemas. Optimized for high, speed reporting in depth analysis. These models are structured to facilitate intuitive, acquiring, enabling, financial analysts to extract actionable insights, to, from, like, efficiency to, complex data sets. supporting these design, is the best implementation of robust, extract transform load process. These process ensure accurate and timely movement of data through the pipeline, incorporating real time validation and transformation rules to, cleanse the standardizing, cleanse and standardizing the data. This tip is crucial for eliminating inconsistencies and ensuring data entering the warehouse is reliable and ready for analysis. And a cornerstone of design is data quality and governance. which includes comprehensive frameworks to, monitor, maintain the data integrity. Automated compliance checks are, integrated to ensure all data adhere to regulatory requirements, reducing, risk of noncompliance and enhancing the, trustworthiness of, financial, reports. To address the stringent security requirements of the financial sector, security and access control mechanisms are implemented at multiple layers. This includes granular permissions, data encryption, and audit trails to protect sensitive information. From the unauthorized access or breaches, such measures not only safeguard the data, but also provide transparency for audits and regulatory reviews. Together, these architecture components create a robust foundation for enterprise, financial, analytics, allowing financial data to flow seamlessly across systems. while maintaining the highest standard of quality, security, and confidence. This holistic design ensures that institutions can access their data effectively to support decision making, regulatory reporting, and strategic, growth. And the implementation framework for financial data warehouse. Yeah, the implementation framework financial data warehousing structure approach is designed to ensure a seamless and efficient deployment that aligns with business objectives and technical requirements. It begins with a comprehensive requirement analysis where business needs are systematically gathered, stakeholder interviews, technical workshops, and reviews of existing data architecture. And, also it identifies the critical reporting analytical requirements. Maps them to technical specifications and evaluates the current systems. The capabilities to uncover gaps and opportunities, following, the schema design process focuses on developing robust and optimized data structures. tailored for financial analytics. This includes designing dimensional and fact tables, managing slowly changing dimensions to capture historical data changes and also define granular levels of data for precise analytical insights. Clear hierarchies also established to support intuitive and efficient data exploration. because to ensure high performance, the framework incorporates Performance optimization strategies, advanced query optimization techniques, strategic indexing and partitioning, HINs, schemas are employed to minimize the latency and maximize the data retrieval speeds. These optimizations are very crucial for handling the high volume of data, high velocity data, typical of financial systems. And the lastly, integration with existing system ensure seamless data flow and synchronization between the data warehouse and source system involves developing robust ETL pipelines for data extraction and transformation, implementing reliable API interface phases for the system communication, and establishing real time synchronization mechanisms. Throughout this integration, stringent measures are taken to maintain data integrity, ensuring consistency and across, accuracy across all our systems. And these components provide a complements for comprehensive framework for implementing, financial data warehouse. That means organizational needs enhance analytical capabilities and ensure long term scalability and performance. And now I am moving on to business intelligence and reporting. so business intelligence and reporting are pivotal elements of financial data warehouse transforming data into actionable insights for decision making and offering. Interactive isolation and drill down functionality. These features empower users to explore data at various levels of granularity from high level summaries to detailed transactional insights, enabling tailored reporting, that means diverse stakeholder needs. BI tools also enhance decision support by incorporating a sophisticated analytical capabilities. It includes historical trend analysis and predictive modeling, allowing financial institutions to identify patterns, anticipate market movements, and assess potential risks such as support strategy planning, portfolio management, and operational adjustments optimize outcomes coming to real time analysis for the strengthens reporting by enabling the process of streaming data such as live market fees or immediate transaction lags. This capability ensure that financial can respond quickly to changing the market condition, detect, analyze and making timely decisions. It is especially valuable, for the areas like high frequency trading, risk monitoring, and fraud detection. And these measure effectiveness of BA system, performance metrics are applied to track both technical and business objectives. This metrics assess system, responsiveness, query performance, data accuracy, and overall value delivered to end users, monitored these parameters, ensure that the system continues to meet, meet the organizational requirements. goals while identifying opportunities for the improvement. Correctively, these BI reporting capabilities provide a robust framework for extracting maximum value from the financial data warehouses, enhancing operational efficiency and driving informal decision making across all the enterprise. Coming to the benefits and limitations of financial data warehouses, the benefits and limitations of final shell data warehouses highlight their transformative potential while acknowledging, challenges in implementing, and maintaining one of the primary benefits is, enhanced decision making, achieved thoroughly streamed and reporting process. By considering an organization, organizing data for efficient analysis, organizations can generate reports 40 to 60 percent faster. Enabling quickly, quicker and more informed decisions. These efficiency translates, translates into improved operational agility and strategic foresight. Empowering institutions to respond proactively to market dynamic and internal demands. another advantage is a regulatory, excellence with the data warehouses offering a robust compliance framework. Automated process. And centralized data management reduce the reconciliation efforts by approximately 30 percent saving time and resources while ensuring adherence to a complex regulatory requirement. And these capabilities, enable organizations to meet stringent standards such as, base three and GDPR with the greater confidence and accuracy. However, the benefits, come with notable, implementation challenges. The initial costs of deploying financial data warehouse range from 5 to 50 million, depending on the scale and complexity of the system. This substantial investment is further compounded by ongoing maintenance. Expenses and the complexities of the upgrade infrastructure to accommodate, evolving business needs and technologies, data management hurdles, also pose significant limitations, particularly in maintaining, data lineage and handling unstructured data sources. Ensuring end to end data traceability across systems can be a daunting task, especially as the data values grow similarly. Feeds or scanned documents require advanced tools and techniques, techniques to, systems complexity. Despite these challenges, the benefits of financial data warehouses often outweigh the limitations offering unparalleled support for decision making, compliance, and operational efficiency in the financial, sector. And the future of, data warehouse, the future of, let me go to, the future of a data warehouse, the future of a data warehouse. is being shaped by emerging technologies that promises to enhance scalability, automation, transparency in data management and analytics. Cloud native architecture, are leading this transformation, offering scalable multi cloud solutions that enable seamless data integration across global operations. And the integration of AI and machine learning is another pivotal trend, revolutionizing the way financial data warehouses function. Advanced algorithms are automating time intensive processes like data cleansing, validation, ensuring data quality with minimal human intervention. Additionally, these technologies deliver real time financial insights by analyzing vast amounts of structured and unstructured data, enabling predictive analytics and proactive decision making in areas such as risk assessment and fraud detection. And edge computing and LOD are also gaining traction, particularly in scenarios that demand ultra fast processing and analytics. By leveraging, distributed processing networks, financial institutions can achieve, microsecond level transformation speeds and real time analytics, critical for high frequency trading and, dynamic risk management. This decentralization reduces latency and enables, rapid decision making. And, data source finally, black chain technology is a emerging as a game changer for financial data warehousing. It is decentralized ledger systems provide transparent and immutable record keeping. Ensuring data integrity and enhancing trust in financial reporting. Blackchain is particularly valuable, in areas such as trade finance, where it can streamline transactions, and reduce operational risk. Together, these trends are redefining the landscape of, financial data warehousing, equipping institutions with tools to native, navigate an increasingly, complex and competitive environment while driving, innovation and operational, excellence. Coming to the conclusion, Data warehousing is fundamental to the effective data management, and decision making, process of model financial institutions. It enables organizations to consolidate vast amounts of data from multiple sources, streamlined reporting, and drive strategic decision making. Successful implementation of data warehouse requires a careful consideration of various factors, including the, technical architecture, alignment with business objectives, and, adherence to regulatory, compliance. Financial institutions must balance these elements to create robust, scalable infrastructure capable of, supporting dynamic business needs while ensuring data accuracy and security. Thank you. Looking to the future, research in data warehousing will need to address several key areas. One of the most important, is the development of a standardized, measurement frameworks that can clearly demonstrate the value of investing in data warehouse technologies. And additionally, research should explore optimal architectures for managing emerging data types, such as unstructured data or real time streaming. advanced, integration of these data types with traditional structured data sources. Advancements in security mechanisms are also essential. Given the increasing, value of sensitive financial data being stored and processed, furthermore, integration of data values, with the data lakes, which store vast amount of raw data, should be explored to enable more flexible, large, large data. finally, the impact of evolving regulatory changes on data warehouse architecture should be examined to ensure continued, compliance in an ever changing legal landscape as the financial sector becomes increasingly complex and data driven, adaptable and scalable data warehouse architectures will be crucial for the future success. Institutions must be prepared to evolve their data infrastructure in response to emerging technologies, regulatory reporting, regulatory requirements, and business needs to stay competitive and resilient, in the face of rapid change. that's all, thank you, thank you everyone, for, giving me, this opportunity. that's all from me.