Developed Machine Learning model based on XGBoost for payment days category of an invoice made by the customers to find the customers who are paying in time and those who are paying late which in turn reduced the Days Sales Outstanding. The DSO was reduced by 5 days which increases the collection process by 15%. This model was deployed on AWS

• Data Augmentation Technique has been use on images gathered from client to increase the size of the data set
• Converted pdf files into images using pdf2text and categorized images as cheques, bank statements, Remittances and invalid files.
• Built Transfer Learning VGG16 Model and Modified the last layers of pre-trained model with existing category name.
• We developed this model as a part of extracting data from cheques, remittances, bank statements and invalid files which saves time and reduces the manual process.

• Gathering the data by selecting appropriate attributes from ORACLE SQL server
•  Exploratory data analysis done to understand the insights of the data and to find missing values and outliers in the dataset and treated them with necessary techniques.
• Developed a machine learning model using clustering technique to identify the payment behavior of a customer to evaluate the risk.
• Build visualizations in tableau to present the key results for CFO’s.

• Working on the EMAGIA product for the finance based Company.
• Managed SQL database to retrieve the information which is further used to build the dashboards.
• Maintaining the Tableau server to create users, giving permission to the users for the dashboards and scheduling the dashboards refresh.
• Developed the Key CFO dashboard which was used by the CFO’s.
• Developed the dashboards for the Collections, Customer Portal Tickets and Sales Order Modules
• Developed the dashboard for Key Performance Indicator “Days Sales Outstanding”.
• Developed interactive dashboards which is used to navigate between the dashboards and implemented User access dashboards in the server using Company Id