Regulatory reporting is becoming increasingly mundane task, fragmented data systems can make the task daunting.
No matter what kind of business your organisation conducts, in all likelihood, parts of your business spend at least one full working day each month pulling data together to produce reports. Eblocks is currently working on an automated reporting solution for a client in the insurance industry, where we are focusing on implementing the best possible technology that will support their needs while transforming their existing processes into a flexible and robust solution.
Report automation refers to the process of automatically creating reports using software. Generally, the software is prompted to automatically fetch data from the various platforms and integrating this data in the system. This automated data collection has made it possible for organisations to readily request live data, and share this data with all the stakeholders concerned.
Firstly, you will need to choose a tool that will best suit your automation needs within the organisation. Here are a few things to consider about the type of reporting tool you choose to use:
Our team has worked on a project to automate a Conduct of Business Return (CBR) report for a client in the insurance industry. This was once an ad hoc report, which has now become a regulatory requirement that must be produced quarterly. In the client’s current process, the report takes a total of 9 weeks to produce manually (this includes data extraction, data manipulation, validation by the various business units and consolidation into a final report that is submitted to the Regulator).
The principle behind this report is relatively simple - it involves counting and adding different values to answer various questions about the insurance products that the company provides to its customers. However, there are multiple different data sources concerned, which all operate in different business units that have their own rules of working.
By automating their reporting process, we will have reduced the need for manual intervention and increased the viability of the data produced while providing enough customisation to add value where it matters most.