Enabling better, faster lending decisions through automation

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Enabling better, faster lending decisions through automation


This content is contributed or sourced from third parties but has been subject to Finextra editorial review.

New technology is emerging at a rapid pace in financial services, enabling the reimagining of previously manual processes, streamlining of decisions, and transformation of the way financial institutions interact with their customers. Profitable growth has always depended upon making the right offers to the right customers at the right time; but in a new era of competition marked by technology-first entrants and evolving customer expectations, top performing credit providers must employ these new technologies effectively to address the evolving needs and demands of their customers.

Much of the industry recognises the need to employ new technologies like artificial intelligence to improve the efficiency of current processes. According to a UBS Evidence Lab report, 75% of banks with over $100 billion in assets are currently using AI in their operations, compared with just 46% of institutions with less than $100 billion in assets. Additional market research conducted by Moody’s Analytics of financial institutions in the UK and Northern EU regions shows that although just 30% of lenders are currently leveraging machine learning (ML) and artificial intelligence (AI) solutions in their operations, 70% of those polled are seeking ways to use such technologies for portfolio monitoring and insights.

Implementing new technology however is no easy task for an organisation. Many institutions are saddled with disparate legacy systems, incomplete and inaccessible data, and arcane, highly manual workflow processes. These institutions can begin with identifying the key gaps in their credit process that would benefit from automation to capture efficiency gains and cost optimisation.

Successful digital transformation begins with assessing the current customer and employee experiences to prioritise the investment for maximum impact. Automating or digitising the status quo is insufficient to remain competitive. Bankers need to think holistically about the customer lifecycle and the potential for profitable growth that technology and thoughtful process enhancement enables. With that outcome in mind, banks can create an iterative vision with multi-disciplinary support from sales, credit administration, IT, finance, capital planning and the executive leadership team.

Recent advances in technology for credit analysis and financial statement processing make lending a key area for transformation. Delays in the pursuit of deals that are unlikely to be approved result in both opportunity and material costs for the customer and the bank. Reimagining the allocation of human expertise and process sequencing around this technology can deliver better decisions, faster—a key to improving the customer experience.

Applying automation to every stage of lending

Automation has the potential to positively serve every stage of the loan lifecycle, from identification and pre-screening applicants, to credit underwriting and customer onboarding and portfolio monitoring and covenant management. Specifically, here are a few ways automation can accelerate and improve the loan origination process:

• Pre-screening: Automation can be used right from the beginning of the loan origination cycle, by helping to pre-screen applicants based on whether they meet the bank’s overarching lending policies and credit criteria. By screening out those requests that are unlikely to result in closed deals, lenders and relationship managers can spend most of their time and resources serving the needs of qualified borrowers.

For example, pre-screening through a client portal that allows for two-way communication can provide lenders with early indicators of an applicant’s credit worthiness, by employing an automated risk score. The risk score may assess various factors like the applicant’s geographic location, industry, time in business, total assets and revenues to ensure they meet the lender’s minimum criteria.

Applicants can also be pre-screened for required compliance including KYC and OFAC validation, prior to any analysis being performed. This reduces the need for lenders to spend time, effort and energy on low-probability opportunities or cookie-cutter submissions that can be automated. This process improvement empowers lenders to focus on exceptions and more challenging requests that demand deeper expertise and analysis.

If the applicant is successfully pre-screened based on such criteria, the application can be moved to the document collection phase, at which point full underwriting and credit analysis can commence.

• Loan applications: Speed to decision is paramount to customers. New automation technology can help reduce inconsistency and delays in obtaining necessary information and documentation from prospective borrowers during the application process.

Automation at this step enables a standardised, auditable, repeatable process for each loan type and ensures that the appropriate documents are obtained from the borrower prior to beginning the process.
Improved customer communication and experience ensures that loan officers and credit analysts receive all the information they need to produce a decision, quickly and accurately.

Improved customer communication and experience ensures that loan officers and credit analysts receive all the information they need to produce a decision, quickly and accurately.

• Document collection: In commercial lending, collecting business and owner financial statements, corporate documents, and required identification efficiently, are essential to the credit decisioning and monitoring workflow. Automating these workflow steps through self-service cloud-based portals and routing effectively to the proper decision-makers improves efficiency, reduces time to close and eliminates redundant processes. It also puts power in the hands of the applicant, providing them with additional control and visibility into the borrowing process through online alerts and notifications.

• Spreading: A common customer pain point is the length of time required to receive a credit decision. To manage risk, banks must take many steps between accepting the initial request and providing a final response to the customer. While important for the lender, to the customer this process primarily represents a delay in realising their goal. Investments in automating the financial spreading step can help redefine the customer experience by shortening the credit decision timeline, while also enabling the institution to process a higher volume efficiently.

One high-impact and popular investment for accelerating credit decisioning is financial statement spreading automation. A Moody’s Analytics poll of more than 35 lending institutions in the UK and Northern EU region found that 40% of respondents are currently automating their spreading processes. Key to evaluating the capacity of the borrower to repay, the credit decision cannot proceed without this foundational data input. Legacy processes relied on manual spreading—an approach that is time consuming, and prone to errors. A combination of digitised data for public companies, direct interfaces with customer systems, and machine learning tools allow banks to quickly realise efficiency and enhance accuracy. In some cases, these tools enable banks to avoid spreading altogether (i.e., by leveraging pre-spread data or directly extracting data from customer systems) or automate the spreading activity through machine learning algorithms.

Consider the example of a large US Bank that offshored its spreading operations. When the pandemic hit and stay-at-home orders were issued, employees in those offshore locations faced unstable internet connections at home and could no longer support prior loan volumes. The bank adopted an automated spreading solution that not only helped them maintain their current levels of production, but also enabled them to spread financials up to 95% faster than previously.

Automating the financial spreading process using AI that combines tools like natural language processing (NLP), optical character recognition (OCR) and machine learning eliminates the manual, error prone component of the task. Spreading automation adds immediate and significant value, freeing credit professionals to focus on areas that better utilise their training. The spreading automation tools can also enable detailed risk assessment, by calculating key ratios and comparing them to benchmarks, highlighting potential risk drivers for credit analysts to review, assess and approve. In addition, the reduction in manual processes, along with automated spreading and document collection has the potential to reduce time to close from a typical 7-10 days down to just hours.

• Customer onboarding: Once the loan is approved, it must be closed and the customer needs to be onboarded into the bank’s core systems. Moreover, new borrowers and customers must be properly screened for anti-money laundering/know your customer (AML/KYC) compliance, as well as against national and international terrorist and criminal enterprise watch lists. These processes as setup today have, multiple touchpoints. But through a combination of automated workflows and customer-friendly, intuitive cloud-based onboarding, this process can be significantly less onerous, providing a better customer experience while ensuring full compliance and exceptional risk management.

Automation is the key to meeting, and exceeding customer expectations

The employment of digital tools for automation throughout the lending process offers several real, tangible benefits to a financial institution. They include improved speed in every step from application to underwriting to close, while enabling greater efficiency, accuracy, and the use of fewer staff resources. The result is a better customer and employee experience, with the potential to generate higher volumes of loans at the same or lower risk. This same concept of leveraging technology to achieve scale and improved performance can be extended to the front office of your institution as well. Identification of high value and high potential customers or segments can be exponentially improved through the use of the right algorithms, and when combined with an effective pricing of the holistic customer relationship, becomes a crucial differentiator against the increasing competition.

The landscape for lending is changing rapidly, and financial institutions face new pressures from every direction. For financial institutions to maintain competitiveness and profitability, they must ensure they can provide needed financing in a timely and efficient manner, while always keeping an eye on offering an outstanding customer experience. Automation is the key to the future of lending, and those financial institutions that embrace it early, and effectively, will harvest the greatest rewards.

Want to have your say? Click here to register for the upcoming Finextra webinar with Moody’s Analytics, on Thursday 4 November 2021 at 15:00 GMT, as a panel of industry experts explore how creditors can streamline the lending process – both for the benefit of clients and their own office – through automation and digitalisation.


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This content is contributed or sourced from third parties but has been subject to Finextra editorial review.