Automation of Reconciliation Process with Data Handling

Financial Analyst

Data Analyst

Alteryx

Microsoft Excel

Redshift

Project Objectives and Achievements

Objective 1: Develop an automated solution to handle the reconciliation process.

Objective 2: Address the issue of large data sets that exceed Excel's capacity.

Objective 3: Normalize non-uniform data for accurate reconciliation.

Key Features of the Automated Solution

Data Handling for Large Files:

Utilized Power Query and Python scripts to handle data extraction and processing outside of Excel's native capabilities.

Developed a VBA-based user interface to allow users to select and process large data files without crashing Excel.

Implemented chunking techniques to divide large datasets into manageable sizes for processing.

Automation of Reconciliation Process:

Automated the entire reconciliation workflow, including data loading, matching, and discrepancy reporting.

Created rules and logic within the VBA code to identify matches and mismatches between datasets based on predefined criteria.

Integrated Python scripts to perform more complex data manipulation tasks and then output the processed data back into Excel for review.

Data Normalization and Cleaning:

Developed robust data cleaning routines to handle non-uniform data formats, including inconsistent date formats, currency symbols, and missing data.

Automated data transformation processes to standardize different data structures into a common format, facilitating accurate reconciliation.

Used advanced text functions and regular expressions to clean and normalize data fields.

Error Handling and Reporting:

Implemented comprehensive error handling within the VBA and Python scripts to manage unexpected data formats and processing errors.

Developed detailed reporting features to provide users with clear, actionable insights into reconciliation discrepancies.

Performance Optimization:

Optimized code to enhance processing speed, reduce memory usage, and ensure stable operation even with large data sets.

Leveraged multi-threading capabilities in Python to parallelize data processing tasks and reduce execution time.

Project Outcomes and Benefits

Improved Efficiency: The automation reduced the time required for the reconciliation process by over 80%, allowing staff to focus on higher-value tasks.

Enhanced Accuracy: The normalization and data cleaning routines significantly reduced the incidence of data-related errors, improving reconciliation accuracy by approximately 95%.

Scalability: The solution can handle large and complex data sets, making it scalable for future needs without further modifications.

User Satisfaction: Positive feedback from users indicated a high level of satisfaction with the automation tool's reliability and ease of use.

Cost Savings: The automated process resulted in a reduction in labor costs and minimized the need for manual intervention and error correction.