A major Asian banking giant needed a way to give its CFOs and finance teams something they’d never really had: accurate, timely, and actionable insight, delivered on demand rather than assembled by hand. The bank’s existing financial analysis processes were heavily reliant on manual reporting and static data analysis, and those outdated methods came with real consequences: significant delays, inefficiencies, and limited flexibility in generating financial insights. Every time a CFO or finance team needed a report, they had to rely on data analysts to prepare it, creating a bottleneck that slowed decision-making right when speed mattered most. Worse, the lack of real-time data meant decisions were often based on outdated information, making it difficult to respond swiftly to market changes in a fast-moving financial landscape. Compounding the problem, the complexity of handling both structured and unstructured financial data further hampered the bank’s ability to derive meaningful insights, an inefficiency that didn’t just impact day-to-day operational productivity, but limited the bank’s capacity for strategic financial planning and growth. The bank needed to shift from traditional static reporting to dynamic, real-time analytics that could provide a genuine competitive edge.
To address these challenges, Ascendion developed a Gen BI solution that streamlined the analysis of both structured and unstructured financial data, giving finance teams a single system that could interpret natural language queries and deliver real-time, visual financial insights.
The solution deployed AI models and Optical Character Recognition (OCR) to generate intuitive visualizations and impactful financial summaries, enabling finance teams to interact with data more effectively and extract insights seamlessly. A star schema architecture organized financial data for optimized retrieval and analysis, ensuring fast query performance and simplified pattern interpretation. Retrieval-Augmented Generation (RAG) strategies enhanced data retrieval efficiency, improving the accuracy of responses to complex financial queries, while a novel multi-agent framework handled accurate query processing and contextual response synthesis, ensuring natural language queries were interpreted correctly, delivering precise and relevant insights every time. The solution enabled real-time forecasting, budgeting, and analysis, allowing CFOs to make quicker, data-driven decisions instead of waiting on a report cycle.
Tech Stack:
Delivered 65% faster financial analysis, enabling quicker decision-making across the bank's finance function.
Reduced dependency on data analysts by 80%, freeing finance teams to interact with data directly rather than waiting on a report.
Decreased manual effort for reporting by 75%.
Delivered visuals that illustrate structured and unstructured data flow into a knowledge base, complete with feedback loops for real-time insights that keep improving over time.
