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Process mining in banks and financial services – what, why and how in 2024

Process mining is a popular method for businesses to gain insight into their processes by analyzing data of tasks, processes and key workflows. It uses data from different IT systems and business applications, such as financial transactions, to create a visual representation of how key processes work and how they can be improved. 

In this article, we show how process mining can be used in financial operations, and provide an example of how it can be used in banking to improve loan approvals processes.

What is process mining? 

Process mining is a data-driven approach for discovering and analyzing business processes. It looks for event logs in IT systems and uses this data much like a digital fingerprint to create a visual representation of each process and workflow. This allows businesses to gain insight into their operations and identify areas where they can improve efficiency. 

Process mining loans event logs example
Simplified example of event logs in a mortgage loan data set

Process mining is not a new technology. It originates in the field of data science and can be simply thought of as the application of data mining techniques to business process management. Thanks to significant investments and technological advancements such as artificial intelligence (AI) we have seen a rapid expansion of different kinds of processes mining software companies.

How can process mining help banks and financial services companies? 

Banking, financial services and insurance (BFSI) are known for being highly complex business sectors where competitive advantage can come from achieving operational excellence in relatively repetitive but service-intensive processes. This makes them ideal candidates for process mining.

Process mining can be used to analyze any financial processes and identify areas for improvement and automation in a data-driven way. For example, an insurance firm can use process mining in improving the claims process, a mortgage lender can use process mining across the loan approvals process, or an institutional lender can use process mining to help with reporting and audit processes. 

Key benefits of process mining in financial services

Process mining can help most financial services to improve efficiency and reduce costs to better serve the needs of their customers. Some key benefits for banks and financial institutions include:

  • Discover and remove costly process bottlenecks
  • Automatically detect fraudulent transactions.
  • Manage regulatory and compliance risk.
  • Identify automation or standardization opportunities.
  • Improve customer satisfaction and customer experience.

Case: improving mortgage loan approvals with process mining 

Let’s look at an example of how process mining can be used to improve home loan approvals in consumer banking. By analyzing data from existing loan applications, a financial institution can identify areas where the process is slow or inefficient and make changes to streamline it.

Examples of mortgage loan processes that can be mined

  1. Loan origination - steps taken by a new borrower to apply for a loan and for processing the loan application.
  2. Loan processing - tasks preparing the loan application for underwriting.
  3. Loan underwriting -  credit review, risk assessment and verification of eligibility for loan.
  4. Loan closing - executing the mortgage loan application and contract.
  5. Loan servicing - managing the scheduling, notifications and payments of loans.

For example, they may find that mortgage loan applications are taking too long to process, or that certain documents are being requested unnecessarily, leading to long average cycle times. For mortgage lenders, slowness in processing new loan applications can lead to higher fallout rates as customers may go for faster alternatives.

Example of mortgage loan process mining
Example: mortgage application process variants within Salesforce CRM

By making changes such as automating certain parts of the workflow or removing unnecessary steps, the organization can speed up loan approvals, maximize pull-through rates and improve customer service. 

Limitations of process mining in financial services

While conventional process mining has proven benefits to BFSI companies it has one major limitation. Many financial processes and workflows extend beyond the core ERP or CRM solutions. Think, for example, of the various business applications that are used to support case handling, such as Microsoft Excel for reporting, Adobe PDF for contracts and various cloud-based credit risk databases. In most cases, process mining is limited to the availability of event logs and each business application needs to be integrated separately.

Process mining in consumer banking - mortgage loans example

The key alternative to process mining is hybrid process intelligence software such as Workfellow. Hybrid process intelligence utilizes artificial intelligence to create generative event logs for all of your tasks and processes - providing you a truly 360 degree view on financial processes. The key benefit of hybrid process intelligence is that you get to track all the tasks, workflows and activities across all your business applications, and not just your ERP or CRM system event logs.

Conclusion

Process mining is a powerful data science technique that can help organizations in the finance industry to gain insight into their processes and identify areas for improvement. It can be used to identify bottlenecks in processes such as loan approvals, uncover discrepancies between internal processes and external regulations, and detect fraudulent activities.

One alternative to process mining solutions is process intelligence software, which gives you a more detailed end-to-end view of your workflows and IT systems. By using process mining or process intelligence, organizations can streamline their processes and improve efficiency, ultimately leading to better customer service and higher profits.

Written by

Lari Numminen

Chief Marketing Fellow
lari@workfellow.ai

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