As Reporting Demands Increase, Reduce the Labour and Cost Associated with Report Generation

Updated: Dec 17, 2020

Reporting is one of the most universal businesses processes, and especially so in an era with exponential data creation and arduous regulatory reporting requirements. As businesses look to improve the quality and monetization of data, decision makers increasingly search for insights through reporting. RPA allows onerous information gathering processes requiring consolidation between multiple sources, structured and unstructured data types, and systems to be automated and scaled, unburdening staff from manual, repetitive, and time-consuming tasks.

The Bot in a Nutshell:

The bot can extract source data from one or multiple sources, each of which could store large structured databases, or individual files (e.g. excel, ppt.), existing reporting dashboards, as well as unstructured data such as email, online HTML data, or even images.

It will then structure, manipulate, and compile the required information from each source into the pre-set reporting layout or template, and publish this to a list of pre-designated, or dynamically determined rules-based list of recipients. Unstructured data such as images and textual data may be processed using OCR technology and natural language processing respectively. Data may be structured using machine learning algorithms such as random forest, support vector machines, and neural networks to provide some initial summary of the data.


  • Finance

  • Regulatory compliance

  • Business metrics (KPIs)

  • IT performance

By using RPA, the workflow only needs to be created once and the report can be scaled and generated as frequently as required subject to pre-determined conditions and rules. As required, new systems, data sources, and reporting formats can be added to the bot, which allows add-ons whilst maintaining a homogeneous central architecture.

For a smarter reporting workflow, reports can integrate with online file storage platforms (e.g. SharePoint or Google Drive) to better utilize storage space.

High-Level Plan

Main Benefits:

  • Cost & scalability

  • Data quality

  • Smart data

Hands-On Considerations

Pain points targeted:

  • Growing volumes of data

  • Increasing requests for reporting

  • Time-consuming manual data massaging

Challenges to expect:

  • Data sources must be static (cannot change format)

  • Data massaging that requires decision making

What will the bot do?

To read more, find out our RPA use case on Reconciliation Processes.

You can also learn more about our expertise in Process Automation on our dedicated web page.