Our customer is a regional prudential regulator responsible for the administration of a National financial governance framework to commercial and investment banks operating in the Pacific Islands. Their reporting process requires many thousands of rows of regulatory data to be collected from each licensee on a recurring basis.

The process of collecting the data was based on Microsoft Excel and required a signifiant amount of effort from the regulatory team to collect, clean and consolidate before reporting analysis and regulatory outcomes could be achieved.

Park Lane undertook a detailed discovery process where one of our data and analytics consultants travelled to the customer’s site and worked directly with the prudential regulatory team to deeply understand their objectives, constraints and existing processes.

During this discovery we documented the existing processes and created a technology roadmap, identifying the most appropriate, best of breed solutions to automate and improve data handling quality at each stage.

Within 1 week the Park Lane team built a working prototype demonstrating fully automated ingestion, transformation, persistence and reporting of the complex prudential returns data sets. Various opportunities for “blue sky” improvement were presented back to the customer for consideration, addressing many key concerns of the existing system and identifying significant opportunities for improvements.

During the implementation phase, we built a bespoke document management portal to ingest the prudential returns data using Google App Engine, which is processed by an ETL pipeline written in R, persisted in Google BigQuery and presented back as embedded reports via Google Data Studio.

Park Lane now operates this solution as fully managed service for the customer and is working proactively to introduce new features and functionality in close collaboration with the prudential reporting team.

The implementation of the automated prudential reporting system has produced the following key outcomes for our customer:

• Reduction in manual overheads – associated with data management and cleaning, regulatory team is freed up to focus on high-value activities
• Improved speed of delivery – data is ingested and reporting outputs are generated in seconds rather than days
• Improved data quality – Implementation of automated data quality checks and exception reporting
• Advanced analytics – persistence of data in structured format allows for application of advanced statistical techniques to gather deeper insight from the data