We recently attended the American Banker-hosted webcast Addressing Market Needs for Business Process Automation. The panel discussion revolved around research conducted by Dana Jackson, Vice President of Research at SourceMedia and Canon U.S.A., which captured data from 300 loan servicing managers and executives. The insights from the study impact lenders including those curious about automation and machine learning in banking and loan servicing.
Key takeaways:
1. Loan servicing costs are dramatically impacted by regulation
Lending and loan servicing costs have increased due to regulatory compliance instituted over the last decade. According to panelist Craig Hughes from CC Pace, the fear of non-compliance has led to 2-3x more work, and regulations have led to a tripling of costs for servicing performing loans. He went on to say that improving employee efficiencies is a top priority as the workforce represents 40% off all costs associated with loan servicing.
2. Lenders are desperate for process automation
According to the data, 63% of the participants plan to implement technology solutions as a top priority for increasing competitiveness in loan servicing. They see the investment in technology as vital to improve efficiencies, mitigate risk, and reduce menial workloads so employees can focus on customer-facing and profit-generating initiatives.
“The information flow is dependent upon manual input that could be automated to speed up the process.”
-Study Participant
Even today, much of loan servicing work is performed manually by way of data-entry and massive, error-prone spreadsheets. For example, 30% of respondents indicated that workflows to manage lien releases and documentation processes were “All Manual” or “Mostly Manual.” Moreover, manual payment processing has the highest reported impact on servicing loans.
Top reported examples of lending management work that is done manually include:
- Lien releases
- Billing and payments
- Document and data management and record-keeping
- Regulatory compliance management
Automation isn’t a silver bullet, but the current amount of manual and menial work impacts cost containment and the drive to profitability. Automation absolutely reduces menial tasks, as seen in these several machine learning in banking use cases. Also, machine learning and associated predictive analytics provide the information employees need to perform better at their jobs.
“The current state has poor workflows and too many manual steps. We need to improve payment processing, communication, and documentation.”
-Study Participant
Overall, respondents are interested in automation tools that can streamline processes, improve accuracy, mitigate costs, improve compliance, and enhance customer service.
3. Payment Processing and Incidents
According to 47% of the participants, time spent manually reviewing documents and verifying loan data has the most significant impact on payment processing and onboarding. A top challenge for lenders relates to the additional administrative work and lousy customer experience associated with incidents like short pays or overpayments due to human errors. Reducing the frequency of incidents related to payment processing and billing was a top goal of lenders participating in the study. As a side note, have you ever read about the $35B overpayment? Talk about an incident.
Lenders see the automation of approval workflow process as a way to cut time and measure employee performance. For example, Rabbet gives loan administrators flexibility to create digital approval hierarchies and pre-programmed notifications when authorization is required.
To further drive engagement, when notifications go unanswered, alerts are created and can be used to escalate time-sensitive issues. Also, insights from these workflows help identify bottlenecks or individuals impacting processing speed. These insights allow leadership to allocate work based on employees’ strengths or provide additional training to develop necessary skills.
“Systems that are user friendly and don’t allow/mitigate human error as much as possible help banks function efficiently and effectively – better servicing the end customer.”
-Study Participant
Loan Servicing Software as a Solution
In real estate development, we’re seeing a rapid rise in modern built technologies. For example, fellow Austin company, Kasita, has taken modular design to a new level with stackable homes for underutilized land plays and urban infill developments. In addition, ICON, a construction technologies company, can print a 650-square-foot house out of cement in under 24 hours, a fraction of the time it takes for new construction.
But, what about advances in loan servicing and loan management software for commercial lenders?
Construction loan management platforms can perform robotic process automation (RPA). According to Mr. Hughes, RPA in a lending environment can address many of the challenges faced by lenders attempting to cut costs and improve profitability as it:
- Assists with decision-making and staying ahead of the curve
- Analyzes a portfolio to recognize trends
- Provides ongoing assessment of potential exposure
- Builds datasets to use for predictive analytics
- Automates data comparisons
- Automates data entry
Rabbet was the first to apply RPA to the post-closing construction loan management process.
Rabbet’s industry-first machine learning and process automation technology applies digital workflow automation to capture and organize documentation. It leapfrogs other technology offerings in the market which are limited to optical character recognition to create searchable PDFs, by also using computer vision, machine learning algorithms, and rule-based predictive modeling.
In addition to storing and providing digital document access, Rabbet technology parses information from the myriad of emails and PDFs included within a construction loan draw request. It then analyzes the data and performs “searches” to identify errors. The software creates reports and alerts based on rules. Example alerts for a construction draw review may include a warning that an invoice doesn’t reconcile with a receipt or that the approval of a draw is on hold pending the evaluation of an inspector.
Conclusion
Cost containment is a priority for loan servicing. To get there, lenders that adopt technologies like robotic process automation and machine learning solutions gain dramatic efficiencies. A process that had taken hours of human interpretation is now streamlined and done in seconds with improved accuracy (and fewer incidences).
Loan servicing software like Rabbet creates a single and secure portal that hosts post-closing loan servicing information in one location. This type of collaboration and centralization means loan administrators can easily audit an entire portfolio to ensure compliance. They can also run an instant project- or portfolio-level report like loan composition, cash flow projections, draw processing, and missing lien releases. Moreover, they have access to real-time information about payment status. All of this information helps lenders better communicate internally and with their customers.
