Automated loan workflows

Automated loan workflows are systems and processes for streamlining the lending lifecycle. They might involve automatic updates within a single system, integrations with external tools, communication with borrowers and agents, or any other tasks that would otherwise require manual action from an agent.

Some credit providers leverage automated workflows throughout their products lifecycle, even fully automating complex processes like underwriting. While some providers do choose to keep human judgement involved in complex, cerebral processes, most credit providers have made steps to modernize their operation through automation, moving towards greater efficiency, scalability, and consistency.

Introduction to automated loan workflows

Lending and credit automation takes many shapes. Even within the same loan management system (LMS), there may be several wholly different tools for automating tasks, with differing levels of human input, configuration, and complexity.

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For example, compare these real-world use cases for automated loan workflows:

• Automated origination and onboarding. Because applicants often need fast access to credit, many lenders have invested in automating their loan origination processes. Where a traditional application might have taken days or weeks to be approved, modern data integrations, decisioning models, and predictive analytics allow credit providers to fully automate their underwriting and origination—a borrower can be approved within minutes of applying, and funded all on the same day.
• Automatic payments. Payments fall on recurring, predictable schedules, and yet many borrowers forget them. Most LMS platforms support some form of payment automation, allowing borrowers to add bank cards or accounts and give lenders permission to automatically process payments on due dates, reducing delinquency rates and taking a mental load off the borrowers themselves.
• Automatic charge-offs. When a credit provider determines that it’s no longer worth it to attempt collecting on an account, they charge off the remaining balance. (This might be a significant balance which has gone delinquent, or a small remainder left over after the majority was repaid.) While many lenders rely on manual processes to pull reports and charge off accounts, some modern credit platforms offer tools to automatically charge off balances according to the creditor’s business logic. They might update accounts, notify investors, or send a message to the borrower depending on the lender’s specific policies.

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In each example, the level of complexity differs significantly. Automating an origination workflow generally requires multiple specialized tools (like underwriting systems, risk assessment, and decisioning engines) integrated together to form a single cohesive process. Automatic payments, on the other hand, involve far simpler logic—when a payment is due, process that amount—but still require integrations with PCI-compliant tools for saving card information as well as third-party payment processors. Charge-offs can be triggered by a number of different circumstances, but might be handled within a single LMS.

And these examples are only the tip of the iceberg. Every industry has its own unique loan management processes, and these industries all see their most forward-thinking players leaning into automation and modernization.

Understanding automation in lending and credit management

Automation offers significant advantages to credit providers, but properly implementing it requires planning, work, and the proper tools. Before they can automate their portfolio management processes, credit providers need to understand the specific details of how automation works on their credit platform, and should also be aware of the tools needed for a smooth rollout.

The nuts and bolts of lending automation

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While every LMS and LOS has its idiosyncrasies, automation generally follows a similar structure, beginning with careful configuration and leading into long-term continuous monitoring.

1. Configuring automations. Some credit platforms allow lenders to customize their automations; others design automation workflows themselves, and then make these available to their clients. In either case, the technical team designing the automation needs to decide a set of triggers and their effects. For example, a trigger might be an account being fully paid off, and the effect would then be a series of steps to close out the account and communicate with the borrower (thanking them for their business and perhaps offering other credit products).
2. Ongoing monitoring. Once the automation is configured and activated, the LMS will begin monitoring accounts, waiting for the trigger. It might rely on specific messages and flagging tools, or it might regularly check each eligible account for the triggering conditions.
3. Effects. Once an account triggers an automation, the system takes action. These actions might be fully automated and only involve the LMS itself and other integrated software tools, or it may alert agents and customers if any human action is required. (For example, some charge-off processes might require an agent to review and sign-off before closing an account.)

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Automations like these rely on similar technologies. For automations within an individual system, most platforms will use some kind of rules engine to specify the triggers and effects for different workflows. When communicating with other systems, they might use APIs and webhooks to send data back-and-forth in real time.

Similarly, an LMS might be integrated with other specialized systems that handle particular tasks, like communication, payment processing, or card management. And all of these processes might be enhanced with machine learning and AI, which is rapidly becoming a cornerstone of credit providers’ automated workflows.

How automation transformed origination

While there are countless examples of automated workflows in lending, origination stands out as a prime example of how automation has not only improved efficiency, but also the customer experience

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Before automation became widespread, origination was a lengthy process with predictable shortcomings:

• Inefficiency. With manual processes, applicants who might have urgently needed funding to bridge the gap between a sudden expense and their next paycheck might have to wait several business days (if not weeks) to get approval.
• Inconsistency. Although internal policies and laws like the Equal Credit Opportunity Act (ECOA) demand equal treatment for applicants, the judgement of individual underwriters sometimes delivers inconsistent results.
• Poor customer experience. Beyond the approval times, the application process was characterized by byzantine forms, limited visibility, and long stretches of waiting to hear back from lenders.

But over the last several decades, fintechs and later other lenders and financial institutions have automated their origination workflows, delivering a markedly better experience to everyone involved

• Rather than 2-3 business days, applicants can be approved and funded in 2-3 minutes.
• Automations keep approvals aligned with internal policies and regulations.
• Automations can run in the background of a modern application, giving borrowers real-time updates on their approval and funding process.

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The overall impact has been overwhelmingly positive: taking what used to be a chokepoint in many lending operations and turning it into a smooth, scalable process.

Automating your lending and credit workflows

Automating your operation might look like a daunting task, but it brings the tangible rewards of efficiency and a dramatically improved customer experience. Ultimately, automation allows agents to do more and do it better, opening up the possibility for scaling.

If you’re looking to drive efficiency, stay compliant, or grow your portfolio, reach out to us. We’d love to discuss how automation can improve your workflows, and show you the automation tools available in LoanPro.