Process mining is rapidly changing how financial institutions operate by providing unprecedented visibility into processes. According to Gartner, the process mining software market is projected to exponentially grow from $405 million in 2020 to $2.9 billion in 2025. This highlights the increasing relevance and adoption of process mining in finance.
In this comprehensive guide, we will explore the top 10 use cases of process mining in finance and share recommendations for financial institutions looking to leverage process mining.
How Process Mining Works
Before diving into the use cases, let‘s briefly understand what process mining is and how it works.
Process mining extracts data from your organization‘s IT systems to create a digital twin of actual processes. It connects to the event logs of your core systems like ERP, CRM, procurement and reconstructs detailed process flows.
Specialized algorithms analyze these event logs to model processes, measure variations, identify bottlenecks, and simulate improvements. This provides unprecedented visibility into your processes.
Now let‘s explore how financial institutions can leverage process mining to their advantage.
1. Increase Process Efficiency
By mapping processes end-to-end and analyzing execution metrics, process mining identifies bottlenecks and streamlines operations.
For example, by analyzing average claims processing times, an insurance firm discovered that nearly 68% of claims took over 5 days to settle due to inefficiencies. Process mining recommended automating aspects of claim verification to reduce processing time by 57%.
This use case demonstrates how mining can drive greater efficiency across financial processes.
2. Discover Automation Opportunities
Process mining provides unparalleled visibility into repetitive, rules-based processes which are ideal for automation through RPA software bots.
According to research by Everest Group, process mining initiatives uncover 2X more automation opportunities compared to traditional methods.
For instance, mining P2P processes helped a bank identify over 50% of invoice processing as automatable, saving 15K manual hours annually.
3. Continuous Compliance Monitoring
Financial institutions utilize process mining for ongoing compliance checking by comparing as-is processes against regulations and internal policies.
Deviations are detected enabling root cause analysis. This proactive monitoring ensures critical finance processes are audit-ready and compliant.
A global bank used process mining to reduce compliance audit costs by 30% and shorten audits from 9 months to 4 weeks.
4. Reduce Maverick Buying
Maverick buying refers to off-contract spends done without procurement oversight. Mining P2P process data helps uncover such non-compliant purchases.
By checking for key P2P steps like PO creation, appropriate spend authorizations, and invoice-contract matching, organizations can identify and control maverick spending.
A leading bank discovered over $2 million in maverick purchases and saved 12% in vendor spend through process mining.
5. Pinpoint Root Causes of Issues
By tracking processes end-to-end, mining tools highlight reasons behind bottlenecks, errors, and delays.
For instance, by analyzing customer payment behavior, a firm identified website navigation issues that increased late fee payments and rectified them to minimize such extra costs.
6. Boost Customer Satisfaction
Real-time process monitoring provides visibility into customer pain points. Analyzing customer journeys helps redesign processes to enhance CX.
A retail bank used process mining to discover customers‘ difficulties in account opening. By optimizing this process, they reduced account activation time from 4 days to 1 day.
7. Enable Innovation With Minimal Risk
Updating legacy finance processes comes with risk. Process mining reduces such risks by comparing processes pre and post-change to detect anomalies.
Ongoing monitoring provides alerts on process deviations, enabling early intervention. This allows banks to innovate their operations safely.
8. Detect Financial Crime
Analyzing large volumes of transactional data helps uncover suspicious patterns indicative of fraud, money laundering, or other financial crimes.
Process mining complements rules-based detection by identifying complex anomalies. This results in lower false positives and better fraud catch rates.
A global bank improved its AML screening effectiveness from 68% to 92% using process mining.
9. Credit Risk Modeling
By extracting insights from past lending and claims processes, mining can enhance credit risk models for more accurate decisions.
Factors like application processing times, frequency of rejections, claims ratios, etc. can be incorporated into risk models to minimize bad debt.
10. Optimize Collateral Management
Analyzing collateral and margin call processes helps streamline liquidity risk management.
Process mining provides visibility into collateral positions, warnings and recalls, and highlights process inefficiencies. This leads to optimized collateral management.
Best Practices for Process Mining in Finance
Here are some recommendations for successfully leveraging process mining:
- Involve business process owners throughout to ensure buy-in
- Prioritize high-impact use cases first to demonstrate quick wins
- Invest in cleaning event log data to get quality mining outputs
- Anonymize logs to protect sensitive customer information
- Start with user-friendly tools that integrate well with existing systems
- Focus on enabling process changes, not just insights
- Proactively monitor processes before and after improvement initiatives
- Appoint process mining competency leads to drive adoption
Get Started With Process Mining
Hope this guide provided a comprehensive overview of applying process mining in financial institutions. To learn more about process mining and shortlist tools, refer to:
If you need help with process mining technology selection or implementations, our experts can guide you.