Automated Loan Decisioning is the Future for Financial Institutions

Manual workflows for credit decisioning are becoming obsolete because they do not perform as fast, precisely, and reliably as automated processes. And with the rapid growth of customer-facing digital solutions powered by data analytics, more financial institutions are adopting automated loan decisioning tools to support their loan origination system workflows.

For financial institutions wondering whether automated loan decisioning is right for them, this article is the right place to start.

The future is now, and the future is automated.

Understanding the Benefits of Automated Loan Decisioning

In the lending industry, automated loan decisioning stands out among fintech tools because it improves efficiency at multiple levels for the lender and their customers.

With growing competition in the lending market, consumers have more options when shopping for credit. As such, banks and credit unions must stay ahead of their competitors by making the lending process customer-friendly, starting from loan origination through funding after a loan is approved.

As many as 75% of big banks use automated technologies to speed up the credit lifecycle and transform their digital lending pipeline. The rise of automated loan decisioning processes in the lending marketplace means businesses can upgrade and optimize their systems to match and surpass current standards, improving their efficiency and increasing the revenue they generate.

Let us dive deeper into the benefits of automated loan decisioning:

Streamlined Loan Decisioning Process

In general, it takes up to 14 business days to originate and disburse personal loans when borrowers  apply for funding via banks or credit unions. Auto loan approvals typically take hours or weeks, depending on which institution borrowers apply to. Even worse, it can take 30 to 60 days to secure a mortgage. Much of this time is spent completing time-consuming manual tasks during loan origination and underwriting (like redundant paperwork).

In addition to these manual tasks, loan approvals are often lengthy because loan officers must review applications to determine which applicants are creditworthy and which are not. This is not as simple as checking one’s credit score. The large combination of component factors results in delays, lengthening the entire lending process and limiting the number of loan applications a business can review.

That is why there is merit in upgrading to an automated credit application processing system.

An automated loan decisioning model can dramatically reduce the time spent manually reviewing loan applications and assessing credit risk, allowing lenders to focus on other critical tasks. Automated lending has also been shown to reduce human errors and improve data accuracy during loan origination, translating into higher loan approval rates and better loan pricing decision-making.

Enhanced Efficiency and Scalability

By automating each step of the loan process,  financial institutions like banks and credit unions can handle higher volumes of loan applications since they will gain an optimized internal resource allocation of their financial data. In turn, this allows businesses to scale loan offerings with optimized auto, commercial, or consumer loan software.

According to McKinsey, automated loan origination software can accurately extract data from applications, allowing lenders to prioritize lower-risk, straightforward loan applications over higher-risk ones that require extensive review. Such prioritization capabilities have been shown to improve efficiency and increase revenue by up to 13%.

As the market changes and lenders grow, automated loan decisioning helps them remain adaptable to these demands and scale up as needed. For instance, machine learning and AI-enabled decisioning models can learn to process more applications faster, allowing financial institutions to maintain the same efficiency even as they expand their loan portfolio.

Improved Risk Management and Compliance

In the lending industry, businesses are expected to manage risks proactively before they can impact customers (and, ultimately, the lender). A crucial component of risk management is adhering to the predefined rules and regulations stipulated by regulatory standards.

For instance, SOC 2 is one of the widely applicable standards that help businesses manage data privacy, security, and confidentiality risks during loan origination. Compliance with SOC 2 can help a financial institution exercise regulatory governance and oversight across the organization, effectively catching risks as they develop.

However, complying with SOC 2 via manual processes can be challenging since reporting on the framework requires extensive data collection and analysis. With an automated loan decisioning model, lenders can curate the data needed for compliance reports via advanced analytics, staying ahead of various types of risks throughout the lending process.

Implementing Automated Loan Decisioning: Key Considerations

Now that we have covered the benefits of automated loan decisioning, what are some practical considerations a financial institution should consider for implementing it seamlessly?

Data Integration and Analytics Infrastructure

For any automated loan decisioning model to produce accurate and reliable results, it must receive high-quality data. Otherwise, any insights it generates will not be helpful to a financial institution looking to make robust automated decisions.

Here, integrating diverse data sources—traditional and nontraditional—will help augment the model’s precision. With a larger diversity of data, businesses can better understand consumers’ behavior, anticipate changes in the market, and identify new opportunities. That said, adding these various data sources also means investing in robust analytics capabilities that can easily identify trends across data and generate accurate insights.

At the same time, a business implementing data integration and analytics should consider establishing comprehensive data cleaning and enrichment processes to make data more usable, whether for determining lending risk or refining the automated decisioning model.

Utilizing Machine Learning for Intelligent Decisioning

Intrinsically, machine learning tools get better at generating automated insights over time, especially as they are trained on more data. When developing an automated loan decisioning model that performs intelligently, you can feed it historical data patterns, which it can analyze to enhance the accuracy of future loan decisions.

Artificial intelligence (AI) also helps create credit-risk models that can be applied to any credit application and effectively assess borrower creditworthiness. For instance, these AI models can create custom risk factors and credit signals to help identify anomalies in data sources like bank statements or rent and utility payment history.

Striking the Right Balance: Human Intervention and Automation

Even when applying automated loan decisioning models to optimize the lending process, a financial institution must maintain a healthy balance between automation and human intervention. Relying heavily on automation can make processes too standardized and miss case-by-case intricacies across loan applications. And, likewise, human-dependent processes are prone to errors during data input, as well as subjectivity bias.

The ideal situation for making robust lending decisions involves leveraging human expertise when managing exceptions and complex loan application scenarios to support automated decisioning tools. Here, a lender’s internal business expertise comes into play, especially when dealing with large amounts of complex data from various sources.

Maximizing Customer Experience with Automated Loan Decisioning

With the lending market continuously evolving and becoming more competitive, lenders are expected to appeal to their customers’ interests when developing any tool. However fast and efficient, an automated decisioning tool would be impractical if it does not help a business meet consumers’ most pressing needs.

So, what are some of the needs financial institutions can anticipate from customers and end users?

Faster Response Times and Improved Service

One way to maximize the customer experience with automated loan decisioning is to understand their needs at the different touchpoints of the loan origination workflow. Typically, slow response times and long wait times are the norm with traditional lending processes.

By leveraging data analytics, lenders can optimize customer-facing touchpoints of the loan origination workflow and speed up response times, improving service. Such changes communicate a sense of value to customers, especially when they are accustomed to lengthy loan applications.

Personalization and Customized Loan Offerings

Another way to leverage automated loan decisioning to optimize customer experience is by personalizing loan offerings. Here, lenders can collect data from customers’ interactions with the loan origination system and understand which interactions were more favored than others.

Best Practices for Implementing Automated Loan Decisioning

Now, let us cover best practices that will help effectively implement automated loan decisioning:

Choosing the Right Technology Partner like Fuse

Rolling out new technology can be challenging without the right support, whether it is troubleshooting an integration, optimizing a process, or scaling up the platform as a financial business grows. When partnering with a technology company like Fuse, lenders receive guidance on choosing the right automated loan decisioning solutions to support the loan origination workflow.

For instance, finding the right loan origination software for credit unions and other financial institutions will typically come down to one that will secure data, enable on-demand scalability, and quickly integrate across the existing infrastructure.

Continuous Monitoring and Optimization

Once the automated loan decisioning tool has been established, lenders must ensure it fully integrates within the loan origination workflow. This requires continuously monitoring and optimizing all relevant automated decisioning processes for long-term success.

Iterative data collection will also help generate data-driven insights to help make effective business decisions. Along with understanding the insights generated by data analytics tools, it is important for businesses to continuously improve customer service by constantly improving the experience.

Implement Automated Loan Decisioning with Fuse

At Fuse, we believe financial institutions can provide faster loan origination to their customers hassle-free. Our loan origination system (LOS) automates credit decisioning with flexibility, security, and scalability, allowing lenders to customize lending processes, achieve industry-standard security, and scale on demand at the enterprise level.

Besides being a next-gen, industry-leading LOS, Fuse will help businesses reach their revenue goals much faster than the manual loan decisioning workflow.

To learn more about Fuse and get started, schedule a demo.

Sources:

AICPA. SOC 2- SOC for Service Organizations: Trust Services Criteria. https://us.aicpa.org/interestareas/frc/assuranceadvisoryservices/aicpasoc2report

Forbes. How Long Does It Take To Get A Personal Loan? https://www.forbes.com/advisor/personal-loans/how-long-does-it-take-to-get-a-personal-loan/

Forbes. What To Consider When Upgrading Your Decision-Making Solution. https://www.forbes.com/sites/forbesfinancecouncil/2022/08/16/what-to-consider-when-upgrading-your-decision-making-solution/?sh=6d9a5d35239f

QuickenLoans. How Long Does it Take to Buy a House? https://www.quickenloans.com/learn/how-long-does-it-take-to-buy-a-house

McKinsey. Best of Both Worlds: Balancing Digital and Physical Channels in Retail Banking. https://www.mckinsey.com/industries/financial-services/our-insights/best-of-both-worlds-balancing-digital-and-physical-channels-in-retail-banking