Articles

The Benefits of an Automated Loan Origination System

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.

Improved Productivity

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.

Well-Defined Workflows

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.

Enhanced Scalability

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.

Articles

10 Tips to Successfully Implement RPA in Finance

Automation has taken the finance industry by storm, and for all good reasons. Banking automation technologies like Robotic Process Automation (RPA) come with the promise of streamlining processes and increasing efficiency.

According to Forbes, RPA has the potential to transform the way finance functions, from reducing manual errors to freeing up valuable resources. To help you maximize the benefits of RPA in finance, we’ve put together a list of 10 tips for successful implementation. But first, let’s take a step back and explore what RPA is.

What is Robotic Process Automation (RPA)?

RPA is the use of software robots to automate routine and repetitive tasks like document processing, freeing up employees to focus on strategic activities that can lead to better customer service or innovations that could meet customer expectations.

The bots are programmed to follow specific rules and procedures to complete a task, just like an employee. They can interact with various software applications and systems, such as spreadsheets and databases, to collect and process data. The bots can also make decisions, trigger responses, and communicate with other systems and software.

When a task is triggered, the bot performs it automatically, eradicating the scope of errors that may be produced through manual processes. The process is monitored and managed by a central control system, allowing adjustments and updates to be made as needed.

Think of RPA as a digital workforce, working tirelessly in the background to complete tasks that would otherwise take hours to complete. With RPA in finance, your financial institution can increase efficiency, reduce costs, and improve the accuracy of its processes.

The Use of RPA in Banking Automation

There are many ways in which RPA can be used in banking automation. Here are some of them:

Customer Service Automation

RPA is capable of automating routine customer service tasks such as account opening, balance inquiries, and transaction processing, allowing bank employees to focus on more complex customer needs.

Loan Processing

RPA in banking and finance can streamline the loan processing workflow by automating repetitive tasks such as document collection and verification, credit score analysis, and loan decision-making.

Fraud Detection and Prevention

In 2022 alone, banks and financial institutions lost $500K to fraud. Technologies like RPA can analyze vast amounts of data to detect and prevent fraud in real-time, improving the accuracy and speed of fraud detection. It may also report any suspected fraud to the banking authorities as soon as it is discovered.

KYC and AML Compliance

Verification and compliance document processing can eat up a major share of your institution’s resources. RPA can automate the process of collecting, verifying, and analyzing customer information, helping banks to comply with KYC (Know Your Customer) and AML (Anti-Money Laundering) regulations.

Back Office Operations

RPA can automate various back-office tasks such as data entry, reconciliation, report generation, monthly closing, and management reports, freeing up employees to focus on more strategic initiatives.

Payment Processing

Manual data entry in payment processing can lead to manual errors and longer processing time. RPA can automate the payment processing workflow, including payment initiation, authorization, and settlement, reducing the risk of errors and improving efficiency.

Risk Management

Banks and financial institutions are constantly at risk from various sources. RPA in finance can help institutions identify, assess, and manage risks by automating data collection, analysis, and reporting, improving the accuracy and speed of risk assessments.

Internal Compliance Monitoring

RPA can automate the process of monitoring and reporting on compliance with internal policies and regulations, reducing the risk of non-compliance and improving overall compliance management.

The potential of RPA in banking and finance is unlimited. When combined with Intelligent Automation technologies, RPA can leverage Artificial Intelligence (AI) and Machine Learning (ML) to provide more intelligent process automation. While the technology in itself is efficient, financial institutions must know how to implement it for maximized benefit.

10 Tips for Successfully Implementing RPA in Finance

According to the Deloitte Global RPA survey, 53% of organizations who took part in the survey have already begun their RPA implementation. The number is expected to rise to 72% over the next year. Entering the RPA race can be quick, but managing and scaling it is a different ball game. Before getting started with automation initiatives, it is important to consider the following tips.

Start Small

While RPA might seem useful to rejuvenate all of your systems and processing, it is important to consider the business impact and start small. Beginning with a smaller project, for example, a single process or department can help build momentum and demonstrate the benefits of RPA. It also allows the organization to gain experience and develop a better understanding of the technology before scaling up.

Define Clear Goals

The key to successful RPA implementation rests in your goals. Decide what you want to achieve with RPA in consultation with your IT department. Having a clear understanding of the goals and objectives of the RPA implementation will ensure that resources are allocated appropriately and that the project stays on track. Aim for specific, measurable, achievable, relevant, and time-bound (SMART) goals.

Involve Stakeholders

Engaging stakeholders, such as the management, finance employees, and IT, can help build better design and smoother change management for the RPA implementation. It also ensures that the RPA system is well-integrated across departments and addresses the needs and concerns of all stakeholders.

Assess Processes

When a financial institution leverages too many bots to automate processes, it results in a pile of data. They may be tempted to use ML on the data and create a chatbot to make customer queries easier. However, it can lead to a poorly planned ML project. Thus, a thorough assessment of the processes is critical to ensure that the RPA implementation does not get sidetracked. This assessment should include an analysis of the tasks, inputs, outputs, and stakeholders involved in the process.

Choose the Right Tools

Selecting the right RPA tools and technology is critical to the success of the implementation. Decide the mix of automation technologies your institution requires before reaching out to a vendor. Factors to consider include the cost, scalability, and ease of use , as well as its ability to integrate with other systems and applications.

Define Roles and Responsibilities

Clearly defining the roles and responsibilities of the RPA team, including project governance, testers, and users, is important to ensure that everyone knows what is expected of them. Remember that automation is a gradual process, and your employees will still need to interfere if it is not properly automated.

Ensure Data Security

Protecting sensitive data and customer information is a key concern in finance. RPA needs to be implemented in such a way that data security remains unaffected. Discuss with your vendor about strong security measures to ensure that data is protected and that the confidentiality of customer information is maintained.

Plan for Scalability

There are thousands of processes in banks and financial institutions that can use automation. Thus, the RPA implementation should be planned with scalability in mind so that the technology and processes can be scaled up as needed. This helps to ensure that the RPA can be combined with AI and ML technologies as necessary and the implementation remains relevant in the long term.

Monitor and Evaluate Performance

Continuously monitoring and evaluating the performance of the RPA bots is critical to ensure that they are operating effectively and efficiently. Also, do not forget to keep HR in the loop so that employees are informed and trained about the changing processes and how to use them in a timely manner. Regular evaluations should be conducted to identify areas for improvement and to make adjustments as needed.

Foster a Culture of Innovation

Encouraging a culture of innovation and experimentation can help to ensure that the organization is prepared for the future. Consult your IT department and automate your entire development lifecycle to protect your bots from disappearing after a major update. Invest in a center of excellence that can create business cases, measure ROI and cost optimization and track progress against goals.

Implementation Automation with Blanc Labs

At Blanc Labs, we understand that every financial and banking institution has unique needs and challenges when it comes to RPA implementation. That’s why we offer tailor-made RPA solutions to ensure seamless implementation for our clients.

Our RPA systems are designed to be flexible and scalable, allowing our clients to start small and grow as needed. We provide end-to-end support, from process assessment and design to implementation and ongoing assistance, to ensure that our clients realize the full benefits of RPA.

Booking a free consultation with us is the first step toward successful RPA implementation. Our team of experts work closely with each client to understand their specific requirements and goals to build a customized RPA solution to meet their needs.