The project aims to enhance the accuracy and efficiency of Jeeves' financial reconciliation processes. Currently, the finance team faces challenges in aligning transaction records across multiple systems, leading to discrepancies that can affect financial integrity and compliance. The project will involve analyzing existing reconciliation processes, identifying common discrepancies, and proposing improvements. Learners will apply their classroom knowledge of finance and data analysis to develop strategies that streamline these processes. Additionally, the project will explore the potential of using machine learning to automate parts of the reconciliation process, thereby reducing manual effort and increasing accuracy. This project provides an opportunity for learners to engage with real-world financial data and contribute to meaningful process improvements.

The deliverables for this project include a comprehensive report detailing the current reconciliation process and identified discrepancies. Learners will also provide a set of proposed process improvements, supported by data analysis. Additionally, a prototype or conceptual model demonstrating the potential application of machine learning in the reconciliation process will be developed. These deliverables will collectively aim to enhance the financial integrity and efficiency of Jeeves' reconciliation operations.

Tasks:

• Review financial transactions and match them against internal and external records.
• Investigate discrepancies and document findings.
• Assist in developing standardized reconciliation templates and procedures.
• Summarize progress and provide recommendations for process optimization.

Idea is to have an agent we can add to our product features :)