Yellowfin Case Studies Automated reconciliations deliver over 25% process efficiencies for St. LukesHealth
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Automated reconciliations deliver over 25% process efficiencies for St. LukesHealth

Yellowfin
Analytics & Modeling - Predictive Analytics
Application Infrastructure & Middleware - Data Exchange & Integration
Application Infrastructure & Middleware - Data Visualization
Healthcare & Hospitals
Business Operation
Quality Assurance
Predictive Quality Analytics
Process Control & Optimization
Remote Asset Management
Data Science Services
System Integration
St. LukesHealth, a Tasmanian not-for-profit health insurer, faced inefficiencies in their payroll group processing. The manual reconciliation of member payroll deductions with employer remittance advices was time-consuming and required the attention of their most experienced staff. The process involved comparing employer-provided PDF files with data in the HAMBS insurance operating software system, which was tedious and prone to errors. With 25 payroll groups to manage, the task was ripe for automation to free up staff for more productive work.
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St. LukesHealth is a Tasmanian not-for-profit health insurer established in 1952. The organization manages over 30,000 policies covering more than 62,000 people across Australia. As the membership grew steadily, the need to re-evaluate internal processes became apparent. The organization aimed to automate routine tasks to allow their experienced staff to focus on more value-adding activities. The first process identified for automation was payroll group processing, which involved reconciling member payroll deductions with employer remittance advices. This task was previously handled manually by the Member Services team, consuming significant time and resources.
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To address the inefficiencies, St. LukesHealth implemented ETL tools from Prometheus and the Yellowfin platform for data manipulation, visualization, and reporting. The first step was to request employers to provide data in CSV format instead of PDF, making it easier to manipulate and insert into a source CSV file. Sample templates were created for testing and validation, and a report was developed to support faster, more accurate reconciliation of payments. The new system displayed HAMBS system data on one side and the equivalent employer data on the other, allowing for quick identification of discrepancies. The automation process took two months to implement and has since seen incremental improvements, including alerts and broadcasts to further reduce operator intervention.
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The automation process has significantly reduced the time required for payroll group processing, with some tasks now taking as little as half an hour compared to an entire afternoon previously.
Staff satisfaction has increased as they are now freed from tedious, repetitive tasks and can focus on more productive work.
The degree of operator intervention has diminished with each improvement, making the process more efficient and less error-prone.
Processing efficiency gains of between 25% and 33% in payroll group processing.
Reduction in time required for reconciling one of the largest payroll groups from an afternoon to around half an hour or less.
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