Process mining in banking: to chart the real stories and reveal oddities

Banking benefits from reconstruction and  analysis of business processes based on the actual traces in IT systems. The re-engineering of optimisable processes was shown in certain cases to decrease time spent on audit by 50%, shorten throughput time by 30% and shorten application processes (for loans, for instance), by 30 minutes per case. 

Banks invest in  AI for compliance (11.5%) and risk monitoring (14.4%), more than in  any other business area.  Is your organization keeping up? 

Because decision making should rely on all known facts. Not on what people think happens in parts of your system.  Not on assumptions that handbook guidelines are 100% being followed. 

Discover  pathways in your banking systems

Chart the stories (pathways) associated with the entities in your system, as they flow in reality: client journeys through departments, billing cycles, loan application journeys, and more. 

  • To know your customer. Understand quickly where the customer’s story fits in the bigger picture.
  • To transfer knowledge easily. Help new employees get a thorough understanding of the stories they need to know about to get them up to speed,  by showing them a snapshot of already completed cases. 


Quantify the stories (end-to-end average time delays, frequencies, fitness) 

  • To assess the impact of organizational changes on your business. Minimize the fear of innovation, by bringing on the table an understanding of its effects on the inner workings of your organisation. 

Pinpoint oddities: inefficiencies and noncompliance

  • Inefficiencies with respect to: 
    • resources: queues at a specific branch (bottlenecks)
    • cost: repetitive tasks that can be eliminated / automated (re-work cycles)
    • time: long throughput times; time demanding activities on client / bank side
  • Mismatches (incompliance to business rules, weird succession of billing activities, etc), for single process steps, or for entire journeys
    • activities that were expected to happen, but didn’t
    • activities (process steps) that occurred, but were not expected to happen
    • successions of process steps that might be considered fraudulent activity (suspicious transactions)


Have your expert investigate possible causes of oddities found, and identify opportunities to minimize waste, reduce costs and increase customer satisfaction. Check where processes could be automated to bring improvements. Bring digital transformation to maximize asset utilisation and transparency into your systems.

  • Workload. Are resources incapacitated because of too much workload? 
  • Workload distribution.  Is the workload well distributed among resources (employees / departments)?  
  • Habits. Look into the modus operandi across departments: Is end-to-end banking operations processing time longer in one department versus another? If yes, is there a valid reason for this?
  • Resource availability. Are there enough resources? Will adding more workers solve the problem?
  • Process complexity. Is it possible to simplify processes to some extent?
  • Demand. Is it the ever increasing number of demands from clients (e.g.  loan applications) that need face to face approval with a bank representative?
  • Client responsiveness. Is the time spent waiting for input from clients unusually long? Why?
  • Islands of expertise / know-how (Bus Factor). Are there activities with a small user set?

ThiaperProcess on sample banking datasets



ThiaperProcess in action