Uncovering Hidden Efficiency: The Power Of Process Mining

Fabrice Trioullier, Managing Director Financial Services Advisory at PwC Luxembourg (Photo © Olivier Toussaint)

Process Mining is a powerful technology that helps the organization gain an objective understanding of its process flows to spot inefficiencies and bottlenecks. In the Finance industry, Process Mining, may be a real accelerator to improve operational efficiency and reduce costs. The aim of this article is to provide an overview of the technology, its benefits, prerequisites, and examples of successful implementation in the financial sector.


Process mining allows organization to build the digital twin of the process – i.e. the history of how activities have been processed, from transactional data extracted from computer systems. The transactional dataset should contain at least three pieces of information: the transaction identifier, the name of the activity performed by the user in the system and the timestamp of the completion of the activity.

Any additional information will allow more in depth, multi-dimensional analysis of current process behavior, how it varies across different populations, and how it impacts selected KPIs.

Process Mining technology can be leveraged to target three types of objectives: discovery, compliance, and improvement.

  • Discovery involves analyzing activity logs to identify patterns, bottlenecks, and inefficiencies.
  • Compliance involves comparing actual process flows with ideal process flows (the “Happy Path”), to identify deviations and compliance violations.
  • Improvement consists of identifying potential process improvements based on the information and implementing corrective actions either by reviewing the implementation or by triggering these actions through the API or RPA integration.


To implement Process Mining, the following prerequisites must be met:

  • Availability of activity log: ensure that IT systems supporting the process generate the activity log with the necessary granularity.
  • Data quality: the activity log should be accurate, complete and consistent.
  • Business process understanding: the organization should have a good understanding of its business processes, including the inputs, outputs and the sequences of activities involved.
  • Stakeholders buy-in: the stakeholders, including the process owners, should be involved and supportive of the initiative, from the selection of the process candidate, definition of the KPIs and dashboard design to the implementation of the recommendations.


Organization may expect benefits from the Process Mining in the following areas:

  • Improved efficiency: by pinpointing bottlenecks and rework, technology will allow the organization to streamline operations and reduce the time it takes to complete tasks.
  • Improved compliance: by identifying deviations from the ideal process flows initially implemented, Process Mining enables the organization to take corrective action and ensure compliance of the process.
  • Cost Savings: Identified process improvements will help reduce duplication of work and improve productivity.
  • Enhanced customer experience: Process Mining helps identify areas where the customer experience can be improved to reduce the time it takes to complete tasks, reduce errors, and consequently increase customer satisfaction.

Use case examples

Loan Processing:

Used to analyze a local bank’s loan processing operations, Process Mining helped identify process inefficiencies, such as long wait times and rework, and helped make process improvements that reduces the time it takes to process loans by 50%.

Customer Onboarding:

A digital bank used Process Mining to improve the customer onboarding process. The bank analyzed the event logs from its IT systems to identify bottlenecks in the process, such as long wait times and incomplete documentation. The bank made process improvements that reduced the time taken to onboard customers by 40%.


Process Mining technology may not be relevant for all types of processes, especially if the process is mostly manual and/or fragmented between different IT systems. Otherwise, this technology provides a mature and effective data-driven approach to systematically understand, monitor and correct these business processes.

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