AI Automation of Financial Process Mapping

The company's finance department handles operations of high complexity, involving multiple roles, systems, and business rules. Documenting this scenario accurately and standardized was a growing need, and the model then in practice had limitations that motivated the search for a more robust solution.

• High Time and Effort Consumption: Manual mapping of financial processes required intense dedication from analysts, from conducting sessions to transcription, analysis, and generation of documentation artifacts.

• Inconsistency Between Sessions: Without a structured standard, the quality and depth of documentation varied depending on the responsible analyst, making comparison and use of records as a basis for improvement difficult.

• Limited Analytical Depth Due to Volume: The sheer amount of information involved in financial workflows made it difficult to systematically capture relevant nuances and details in each mapping session.

Squadra developed, with Genius, an AI-based automation framework to standardize and qualify the mapping of financial processes, from information capture to the generation of output artifacts.

Genius in Action: AI as a Process Analyst in the Financial Sector

Conception and Configuration: AI trained to read the process

The solution was structured around an AI model configured to operate as an expert financial process analyst. Using specialized prompts, the tool was calibrated to identify and extract the most relevant information from each mapping session.

• Specialized AI Prompts: Developed to systematically capture roles, systems involved, business rules, and points of attention in financial workflows, ensuring consistent coverage across all sessions.

• Processing Multiple Sources: The tool processes recordings, documents, and meeting transcripts, consolidating inputs from different origins into a unified analysis.

Processing and Delivery: From raw input to TO-BE ready artifact

With the configuration established, the AI automatically conducts the analysis and generation of documentation artifacts, delivering structured outputs ready for use in the next steps.

• Process Diagram: Automatically generated from the extracted information, it represents the current state (AS-IS) of financial workflows with accuracy and standardization.

• Problem Matrix: A structured document that consolidates pain points and bottlenecks identified by the AI, serving as direct input for process redesign (TO-BE).

• Consistent Standardization: All financial mapping sessions began to follow a rigorous standard, eliminating quality variations between analysts and deliverables.

• Enhanced Analytical Quality: The AI identifies operational nuances and details that enriched the understanding of financial workflows, resulting in more complete and reliable documentation.

• Analysts Focused on What Matters: With the automation of transcription, analysis, and artifact generation steps, finance professionals began dedicating their time to higher strategic value initiatives.

• Solid Foundation for Redesign: The generated outputs, process diagram and problem matrix, provide a high-quality foundation for the TO-BE stage, accelerating optimization decisions.

• Informed Decision-Making: Structured data and clear insights began to guide the prioritization of bottlenecks and the definition of next steps in optimizing financial processes.