Our Client is a major financial management service provider in the Healthtech industry, headquartered in the US. They develop financial management platforms intended to help healthcare providers to improve payment collections and automate payments to improve their growth and efficiency. Their revolutionary technology leverages predictive analysis and other data-driven revenue management insights to provide a highly personalized patient care experience.
Our client wanted an outstanding dashboard to automatically reconcile the daily Credit Card and e-Cheque transactions captured by the client’s payment processor. They also wanted the dashboard incorporated with in-depth analysis on all the factors and elements involved in credit card transactions and ways to identify key patterns for a transaction or a specific merchant.
Feathersoft’s Data Analytics and Big Data Engineering team worked for the project round the clock and delivered a solution meeting all the client requirements and are accessible 24/7. For the initial phase we divided the project into the following stages:
Stage 1: Big Data Engineering : As the first step, our skilled Big Data Engineers worked on building data pipelines using the raw data from the payment processor, where all the information is in the form of text codes (unstructured data). Then they transformed the data into structured formats by extracting the data using NiFi, parsing the file using PySpark, and loading the data into Hive, which is ultimately used by our big data analytics team.
Stage 2: Data Analysis and Transformation: Our Big Data Analytics Team processed the structured data file from Hive, analyzed, and transformed the data into two different dashboards: Analysis Dashboard and Reconciliation Dashboard.
- Analysis Dashboard: This dashboard provides an in-depth analysis of all the factors and elements involved in credit card transactions and identifies key patterns for a transaction or a particular merchant. The dashboard user can add any number of fields related to the payment for analysis. This dashboard is automated and gets updated daily and the report processed in the dashboard can be exported to excel format.
- Reconciliation Dashboard: This dashboard reconciles the transactions captured by the payment processor. The dashboard model and the computation are designed using Tableau. The dashboard gives an overall transaction summary as well as the merchant level transaction summary. Then it generates a report comparing the aggregate amount captured by the payment processor with that of the aggregate amount in the client’s database. If any mismatch exists, it will notify the user. The filter option is added in the merchant level transaction summary report where a ‘Status’ filter can be used to get the mismatches directly at the merchant level. With further drill-downs, the exact missing transaction or a list of possible missing transactions can be retrieved. This dashboard is automated and gets updated daily. By using the filter option, the user can also generate a customized report and export it in excel format.
Impact on Business
- As the process is automated, the user will be able to get the desired output in just a matter of clicks where we ensured reduced turnaround time.
- Achieved greater efficiency and accuracy due to less or no human intervention.
- Resulted in maintaining complete transparency in the entire transaction cycle.
- Customizable features and export options available in the dashboard allowed the client to extract specific information and generate a report on any transactions.
- The dashboard information allowed the client to eliminate all leakage in the process cost-effectively.