Why Automated Loan Underwriting is Crucial to the Success of Modern-Day Lenders

Sep 06, 2021

Banking | 11 min READ

The Need for Automated Loan Underwriting

Accelerating the loan application and approval process has always been the biggest priority for the people applying for the loan and the lenders. Until recently, manual underwriting used to be the only way to disburse loans. The advent of automation has opened a new era of opportunities for companies who need loans and fintech firms. Technologies such as Robotic Process Automation (RPA) and machine learning (ML) further assist fintech companies in capturing clients' information and generating recommended conditions the client needs to meet to receive loan approval.

Preeti Agarwal

Global Program Director

Banking Vertical

Birlasoft

Automated underwriting also significantly enhances loan providers' profit margin from cost cuts in manual underwriting labor and improves customer satisfaction. If companies are looking for Automated Loan Underwriting, there are strong reasons to validate their belief. Automated Loan Underwriting enables digital verification processes throughout the loan cycle. It automates processing and underwriting events entirely as well as the document generation process and also allows digital delivery of the same. The process facilitates real-time integration of all associated parties with the loan origination system (LOS) for exchanging data between applications and introduces e-closing, recording, and vaulting options.

How Does Automated Loan Underwriting Work?

There are many technology tools available today in the market that are revolutionizing the lending sector globally. AI, ML, RPA, NLP, Optical Character Recognition (OCRs) are some of the few new-age technological advancements that genuinely possess the potential to bring a dynamic shift in the way loan approvals and disbursals are taking place globally.

For instance, with its ability to reduce loan-processing time by up to 80%, RPA is poised to transform the digital lending space, as is evident from its rapid adoption by fintech companies. RPAs significantly reduce human errors, automate mundane tasks, assist with regulatory compliance, enable significant cost savings, promise 24*7 support and lower the risk of cyber fraud.

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In crux, RPAs expedite loan processing and disbursement and enhance the productivity of the workforce as well. Let's look at other such technologies that make loan processing a convenient and hassle-free affair:

Loan Application

When it comes to tracking and monitoring loans for defaulters, AI models monitor and track all the incoming payments and predict a loan default score that is updated over time. If a customer is identified as being at high risk of default, then the software prompts loan executives to draw out a different deal for such customers.

During a recent survey by CSA, it was found that mortgage companies are aiming to reduce the loan processing time from prequalification to closing. Their objective is to reduce cycle time by at least one day, with about 35% wanting the implementing process efficiencies to be sliced as much as five days off of the cycle time, and another 30% planning to shave off 6 – 10 days, while the other aspirational ones aimed to reduce cycle time by 11 days or more. The same survey also stated the impact that automation can bring and that 70% of respondents believe technology or automation is the solution to improve performance.

Documentation

Consumer mortgage lending has always been paper-intensive as it requires hundreds of documents to be generated for each application, which doesn't even include loan servicing documents. Such requirements arise due to the complexity of mortgage lending processes where organizations are dependent on multiple systems, databases, workflow tools, and reports throughout their operations, which mandates processors to access numerous resources to complete specific processes.

Managing such loans can be exceedingly resource-intensive without an intelligent automation solution in place. Automating such methods can help businesses manage documents and data, reading and compiling data pulled from several sources and in various formats such as third-party websites, PDFs, and email into a single view. Automation also enhances the audit capabilities of the lender, while encoding checkpoints into the bots will result in a faster and streamlined secondary review and testing.

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How Does Automated Loan Underwriting Work?

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Credit Assessment

The expanse of automation has made it possible for lenders to program their creditworthiness and risk parameters into an automation lending solution, thereby helping companies to move away from manual assessment, which is too time-consuming and, at the same time, error-prone. Automated mechanism even facilitates relatively inexperienced personnel to speedily screen loan applications, which in turn enhances customer service by providing a fast turnaround to loan applications.

Moreover, they allow a high degree of customization, and these standards can be set up for different types of loans, which comes in handy for small business lending as many small business clients may not fall under the gamut of conventional approval criteria. Besides, as another plus point, an automation lending solution allows the institution to devote its underwriters' time to applications that need further review, rather than having them process 'normal' loans that can be cleared via a programmed template. AI can assist financial agencies in determining borrowers' creditworthiness and reduce loan defaults using predictive analytics and NLPs.

Credit Decisioning

Data mining and analytics are fast changing the lending landscape by helping companies capture real-time data and process it to their advantage. Mining the appropriate data & information makes a credit decision process accurate and hassle-free. No two loans are the same in the commercial lending space. The commercial loan market is still highly dependent on human judgment when it comes to loan sanctioning. With the help of automated tools, there is an emergence of auto-decision based on the lender's particular policies and business rules, which is standard practice in retail credit.

Automation equips lenders to make the right decision by using ai based credit scoring, pre-screening applications and assisting loan officers in assessing risk and preparing the proposal for the decision-maker. As we are fast moving towards the 'Mobile-First' world, mobile-enablement is increasingly deployed in the decision-making realm. To this end, fintech companies are arming their executives with laptops, smartphones, and tablet devices fully loaded with applications, which ultimately help them make lending decisions while on the move, significantly reducing the time to approval.

Loan Disbursal

Through traditional lender platforms like banks and NBFCs, the loan disbursal process usually takes weeks to approve a loan. Through digital lending, users can get their loans approved in minutes. Today, technology has become the catalyst behind quick and efficient fintech services, which anyone can access anywhere through an internet connection and a smartphone. Fintech firms have catapulted the lending domain in many ways, and the most significant impact that the customers can experience is in the reduced time for loan disbursals.

Thanks to the fast shaping of digital lending platforms, the time to actually disburse loans is just between 24 - 48 hours post-acceptance. These companies are well equipped to offer paperless and hassle-free money lending services to customers. Loan disbursal can also happen remotely very efficiently through digital channels such as bank accounts, eCommerce accounts, or mobile wallets. These cashless channels enhance operational efficiency and reduce fraud by providing access to their funds in a matter of seconds.

Why Must Lenders Embrace Automated Loan Underwriting?

Shorter Lending Cycle Times

Digital lending is intended to eliminate operational bottlenecks and speed up the process of loan approvals and dispersals. Owing to these ever-evolving advancements, lenders are able to execute real-time data assessment for application approvals or rejection. They can undertake quicker loan decisions and maximize customer engagement. They are able to constantly monitor the creditworthiness of borrowers.

Digital lending business models are increasingly proving to be more cost-effective than traditional banking models. Here lenders do not need to maintain brick-and-mortar structures or pay for expensive legacy IT systems. Therefore, customers receive more affordable loans and access to new financial tools with a significantly lower cost structure.

Better Customer Experience

Digitalizing the loan origination process offers a quick, convenient, and efficient online experience that the customers today are looking for. With the growing expanse of enhanced digital tools, customer-facing web-based portals and application program interfaces (APIs) are able to facilitate digital onboarding of prospective and existing customers' data straight to the lender's loan origination platform. Once the data is received, lender-defined business processes can automate the next step, clearly demarcating loan applications that are ready for approval and loan applications that require more documentation.

Going one step ahead, highly advanced digital loan origination platforms can be even more customer-friendly as they pre-fill the customer data within the origination platform basis the data feeds received. In doing all these, banks are ultimately offering greater convenience to their customers as the amount of paperwork has drastically reduced and the process has become highly efficient.

Streamlined Lending Operations

Manual mortgage underwriting is hampered by frequent branch visits for endless paperwork, resulting in longer wait times. Through manual underwriting, the financial information provided by the applicant via supporting documents, like pay stubs and tax returns, are scrutinized after being verified with third parties, such as employers and banks. Additionally, with mortgage loans, both the lender and the applicant need to wait for an assessment to be scheduled and processed, which further increases the cycle time.

In stark contrast, forward-looking mortgage lending companies that use automated underwriting processes are able to reduce loan processing times and cut interest rates, according to Insider Intelligence's Online Mortgage Lending Report. The physical presence of customers is not required at the branch office for any loan disbursal process. The entire process is seamless and online, which empowers lenders as well as customers to focus their attention on other value-added functions.

Why Must Lenders Embrace Automated Loan Underwriting

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Robust Credit Risk Management and Pricing

ML and automation tools such as OCR are assisting the analysts by accurately and efficiently tabulating the borrower's financial statements for the rating process. These automated tools map the data into a chart of accounts in the balance sheet, income/expense, cash flow, and tax forms, making the risk assessment process more manageable. Tools such as automated risk rating based on probability of default (PD) and loss given default (LGD) models promptly capture essential risk metrics for loan assessment.

To reap the maximum benefit of such digital platforms, combining two processes makes it even more productive. For instance, commercial lending space mandates that each entity in the group traditionally has to have its own financial statements assessed individually to have a risk rating assigned in borrower groups. In such a scenario, an automated loan origination system applies instantaneous group ratings based on the consolidated financial strength of the lead borrower and the application of cascaded or distributed ratings from the parent entity. Such a process saves considerable time in the rating process.

Data Management

With a view to diversify and offer high-quality services to the clients, lenders need to have the capability to draw actionable conclusions from a pool of data available to them. Machine learning algorithms, in this regard, can help lenders identify the latest trends in the market and support customer requirements more efficiently. Lenders are already leveraging big data to reduce the cost of customer acquisition, improve underwriting models, and establish early warning systems.

Scalability for Growth

Earlier, due to the scarcity of technology tools available at hand, conventional financial organizations used to perform manual verification of loan applications involving understanding varied documents for better classification, analysis, stacking and extraction, etc., before the actual lending process, which used to be time-consuming.

As the processes are now getting tech-enabled, the lenders can now verify the consumers' profiles regardless of their geographical locations by using tools like geotagging, eKYC, and other online verification processes, which is eventually helping them unlock the untapped credit market and enhance their scale of operations. Automated underwriting system and processes make it easier for lenders to perform the same task with enhanced security features in the shortest possible time.

Regulatory Compliance

National Mortgage News research shows the average added cost of compliance reviews or due diligence processes per bank per transaction is $300, while for some banks, this figure is reported to be as much as $1,000 in additional costs. Tech-savvy firms are able to leverage compliance as an asset instead of a necessary evil.

Automated regulatory compliance process manages the entire task efficiently and cost-effectively than an in-house, manual process. Fully automated cloud-based lending platforms are regulatory compliant, customized to meet local market rules, and updated on a timely basis as new regulations occur.

Improved Employee Productivity

According to a study by Oxford Economics, the adoption of a flexible and scalable platform can drive loan volumes by 15% to 20% while reducing operational costs by 20%. Through digital lending platforms, loan volumes can go up by as much as 20%, while loan processing can speed up by 25-40%. The sales channel gets more productive as the digital networks bring in additional leads and business. The employees of fintech firms can be utilized in other value-added applications involving human intelligence and thereby enhance their overall performance and organizational productivity

Allied Market Research estimates the market growth of digital lending platforms to reach $19.88 Bn by 2026, at a CAGR of 19.6%. An increase in the need for reduced loan management time among borrowers & lenders, the rapid shift from traditional lending to digital lending will drive the future of the global digital lending platform market. Lenders looking to ride on the growth bandwagon don't have an OPTION but to shift towards automation, or else their sustenance will be difficult.

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