The benefits of an automated loan origination system collectively add immense value for you and your customers.
Deloitte and the Institute of Management Accountants (IMA) surveyed finance professionals across the globe and found that respondents plan to implement or are already implementing robotic process automation (RPA) (22.8%) and artificial intelligence (AI)/machine learning (ML) (21.4%).
Why should your organization be left behind?
In this article, we explain the benefits of an automated loan origination system and help you understand how exactly it can impact your loan origination process.
Automated Loan Origination Vs. Manual Loan Origination
Manual loan origination is a loan origination system where you handle the customer’s application manually, from processing documents to assessing the customer’s credit risk profile.
On the other hand, an automated loan origination system allows you to automate the end-to-end loan process, from application to documentation, underwriting, and administration.
Automated loan origination systems typically use a combination of technologies like intelligent document processing (IDP), RPA, AI, and ML.
Automating the loan origination process solves two critical challenges for lenders:
- Keeping the turnaround time under control
- Finding specialists who can reliably complete the pre-underwriting process
An automated loan origination system involves using software solutions. Your team will likely experience a learning curve, and that’s why you should work with a company that can ensure the learning curve is gentle. Blanc Labs partners with UiPath to provide automation solutions—learn how Blanc Labs hyper automated a client’s loan origination process.
The Benefits of Automation the Loan Origination
Like with any other investment, you’re probably asking what you stand to gain after investing in an automated loan origination system. Let’s talk about the most prominent benefits of automating loan origination.
According to a McKinsey study, automating the KYC process can increase the number of cases processed by 48%. Automation also minimizes the risk of error, further improving productivity.
Solutions powered by AI and ML can fast-track loan processing, allowing your team to process more applications per month. Your staff won’t have to handle paperwork manually or spend hours hunting for minor details in the customers’ documents.
This allows your team to dedicate more time to other revenue-generating activities and exceeding customer expectations instead of mundane tasks.
Think about the KYC (Know Your Customer) process as an example. You could have a team member collect KYC documents, verify them, and store them for future reference. Or you could use IDP to automate all these steps, allowing your team to focus on sales and support.
Once you receive a digital copy of KYC documents from a loan applicant, you can use IDP to input the data into your system — with zero manual effort.
An Accenture case study explains the impact of automation on one of their banking clients. The case study describes how Accenture helped them create new automated processes that improved productivity by over 40%.
Accurate Decision Making
The best automated loan original systems use your strategy and data in the system to make accurate decisions. Machine learning algorithms can even provide deeper insights for decision-making by eliminating data silos.
For example, you might have applicants you can’t score based on traditional credit models. Machine learning models can help assess the risk of such applicants, allowing you to achieve financial inclusion objectives without breaching your risk threshold.
According to a report by McKinsey, you can automate various parts of credit decisioning or make better decisions based on the data collected by the automated loan origination system. Here’s how an automated loan origination system can help improve credit decisioning:
- Credit qualification: Using automated systems to qualify a largely unbanked or underbanked segment by analyzing thousands of data points that were previously inaccessible (such as data from social media and browser history).
- Limit assessment: Determining the maximum amount to lend a customer by assessing data from financial statements, tax returns, and other documents using optical character recognition (OCR) as well as new data sources like email and e-commerce expenditure (with the customer’s permission).
- Pricing optimization: Analyzing an applicant’s risks with more data instead of simply relying on traditional credit scoring models allows for determining a more accurate rate of interest while keeping risk costs low. For example, ML models can use natural language processing (NLP) to draw insights from a customer’s interaction with the sales reps or determine a customer’s propensity to buy based on the types of financial products.
- Fraud management: Detecting fraud is critical when loan origination processes move fast. There are various ways to detect fraud effectively using AI. For example, you might use image-analytics models to interpret a person’s expressions before the brain has a chance to control them.
You can create rules based on these insights to automate one or more parts of the loan origination process, and you can also change these rules over time. For example, the World Bank uses a Decision Authority Matrix to automatically leave low-risk decisions to RPA, while some critical decisions require the involvement of the President.
Automated systems help create well-defined workflows. The system gives you more control and visibility over the process, allowing you to regularize processes like storing documents, manual approvals, and assessment of an applicant’s risk. The lending automation system relies on the information already in the system and trigger-based actions to accomplish this.
With a well-defined workflow, the next step is always clear. But in some cases, there may be caveats. For example, the customer might have requested a loan for an amount larger than your brand can approve. You need to communicate this to the customer along with the amount your institution can lend.
The best automated loan origination systems can use rule-based decision-making to automatically trigger a notification to the customer, informing them about the maximum amount that can be lent by your branch.
You can also address other caveats using the rules-based mechanism. For example, you can configure the system to make complex calculations based on the underwriters’ criteria for a consistent approval workflow.
Scalability is one of the most significant bottlenecks for any lender that uses manual processes. Here are the problems with manual loan origination:
- Hiring fast enough to meet a sudden spike in demand is tough.
- Scaling back during an economic downturn can be challenging.
The Origination Insight Report by ICE Mortgage Technology (a leading cloud-based provider for lenders) reported that, on average, lenders take about 50 days to close a loan.
Lenders can overcome this bottleneck with automation. You can automate various parts of the process to increase scalability, such as:
- Collecting pre-qualification data: A team member might spend hours manually identifying pre-qualified candidates, but automating can earn all of that time back for your team member. Assessing forms using IDP and AI can help automatically select pre-qualified customers at scale.
- Streamline information management: Paperwork, whether physical or digital, can get messy in large volumes. Automation solutions can help you and your customers fetch the correct information quickly without shuffling through a stack of papers. This allows you to work faster and process more applications each month.
- Data extraction: You can extract data from physical and digital documents using IDP, AI, and NLP. These technologies can transmit the extracted data to business applications like a customer relationship management (CRM) or enterprise resource planning (ERP) system. Whether you’ve approved 100 applications or 1,000, you won’t have to spend a single minute migrating data manually when you use these technologies.
Insights for Continuous Improvement
An automated system collects a large volume of internal data while performing repetitive tasks. The same system can put this internal data in perspective by drawing insights from big data.
An automated system can offer four types of analytics:
- Descriptive analytics: Involves looking at historical data to identify trends. This type of analytics helps you learn more about your customers — what customer traits influence loan performance? When lending to high-risk borrowers, which loan structure and tenure minimize the risk of default?
- Diagnostic analytics: Helps understand why something happened based on historical data.
- Predictive analytics: The system’s algorithm predicts what will happen based on data.
- Prescriptive analytics: Involves using AI, ML, and structured and unstructured data to answer how you can make something happen. Example: you can determine the geographic locations where you’re most likely to see success for a specific type of loan product.
The best automated systems auto-generate reports and summaries, so you can quickly go to your dashboard and get key metrics for your loan origination process.
Better Customer Experience
Digital originators are quickly gaining market share and giving banks a run for their money. The reason? They match a modern customer’s need for convenience, speed, and transparency.
The faster you process applications, the better your customers’ experience. The problem, though, is that the loan origination workflow is often extensive. You need to assess the applicant’s credit profile and collateral and check all the regulatory boxes.
An automated loan origination system can do much of the heavy lifting for you. It can help make decisions based on the customers’ credit profiles, verify the validity of the collateral, as well as ensure compliance.
You’ll be able to focus on delivering improved customer experiences by minimizing processing times and using your own time to focus on value-adding tasks. As McKinsey explains in one of its articles:
“Instead of processing transactions or compiling data, [the operations staff] will use technology to advise clients on the best financial options and products, do creative problem solving, and develop new products and services to enhance the customer experience.”
Automate Loan Origination with Blanc Labs
Selecting the right partner to implement automated loan origination helps ensure you have the support you need to address the complexities of lending automation. That’s where Blanc Labs comes in.
Blanc Labs can create a personalized automation system for your organization. The three steps Blanc Labs uses to help lenders include assessing your needs, streamlining your loan origination processes and related platforms, and creating an automation system using technologies like IDP, AI, and ML.
Book a consultation or discovery session with Blanc Labs to learn how we can help automate your end-to-end loan origination process.