What does automation mean? How can automation increase your loan officers’ productivity, streamline commercial loan origination, and make your clients happier?
Introduction
Many software programs can be used to assess credit and originate loans for both traditional and non-traditional lenders. Financial institutions are becoming more aware of the need to improve their practices to improve efficiency, decision speed, and productivity and enhance customer experience.
This paper examines the traditional lending process and the various stages of credit approval to determine how automation can be used to improve and standardize the underwriting procedure.
Problem Diagnosis
Commercial lending is all about creating economic benefits through funding enterprises while also ensuring that the lender can generate a profit, create value for shareholders, and manage risks. It can be not easy to assess the creditworthiness of a business. Financial institutions’ tools can have a significant impact on underwriting standards and approval times, as well as the cost of loans and any unexpected losses. Financial institutions want to automate and streamline the lending process in order to overcome these challenges and improve the quality of their loan portfolio and customer satisfaction.
Why do so many banks struggle to achieve their objectives today?
Many lenders still use paper-based and manual loan approval processes that seem to be out of date in a digitalized world. They have slower decision-making times than many customers would like, as well as an internal data management issue that confuses management and external examiners.
Commercial loans come in a variety of sizes and levels of complexity. As an example, let’s use spreadsheets as one of the manual underwriting methods that are most widely used today. Spreadsheets can be great tools and are probably the most popular software models today. It is unlikely that their designers had loan underwriting as a goal when they designed their application.
It cannot be easy to use a spreadsheet for credit underwriting. It can take a long time to enter data and finances, and the uniformity may diminish over time. Sometimes, data entered in a spreadsheet will be re-entered into the lender’s core systems. This doubles effort and creates duplicate records. This method is flawed from a perspective of storage, lineage, and retrieval, as well as portfolio insights.
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Manual and paper underwriting methods are inconsistent, inaccurate, and time-consuming. Automation allows for the streamlining and consistency of data flow at any stage of the loan-origination process. It can also speed up the process while providing audit and control benefits.
Here’s how to:
Figure 1 shows a typical process of commercial lending. Each banker who reads this article will be able to recognize and visualize the stages in their organization. Consider the amount of staff involved in each step, the bottlenecks that occur, the hardest steps, and the time it takes to complete the process.
Customer Service
Collecting financial and other information about the customer or prospect is the first step to any new loan or relationship. This task is laborious and time-consuming today. It is often dominated by the filling out of forms, printing or electronic documents, and maintaining a physical file. The greater the number of times the lender enters and re-enters the data contained in electronic and printed copies, the more likely it is that the data will be inaccurate.
In a survey conducted by Moody’s Analytics, the question was asked, “What is the biggest challenge you face when initiating the loan procedure?” 56% of the bankers who responded cited the manual collection and back and forth between the client and the bank.
Automating the collection of financial data, as well as other required customer information, can reduce inconsistencies and delays. Web-based portals for customers and APIs can be used to digitally onboard new prospects and existing customers directly into the loan origination platform of the lender. Once data has been received, business rules defined by the lender can automate the following step, allowing it to differentiate between loan applications ready for decision-making and those that need more documentation.
Data feeds can also be received by more advanced automated loan origination systems, which pre-populate the customer information fields on the platform. Importing customer ownership hierarchies is one of the most useful applications. Uploading organization diagrams that visually represent the key entities in a group and their interrelationship will automatically create customer ownership hierarchies. Importing such information can reduce administrative burdens for complex borrowers.
How often do bankers have to enter information from the CRM system into the credit application when a borrower changes their details or ownership structure after a change? It would be easier and less prone to errors if the CRM system integrated seamlessly with the loan application software and data from one system flowed natively into the other. This type of integration is possible with the best loan origination platforms.
It is common practice in many financial institutions for the front office to keep separate records on the same client. This may be done for compliance purposes, but it can lead to inefficiencies and errors. A platform for automated credit origination allows multiple teams from different departments and locations to electronically access the same documents based on their needs and purposes, thereby creating a single point of truth. It is possible to maintain the integrity of customer data by implementing user identification and access protocols. This will ensure that only individuals with the appropriate privileges can access the information. This is a much better option from an auditing and control perspective than open-access file directories.
Credit Analysis
Spreading the financial information you receive from your customer or prospect is a manual and repetitive process. Our recent poll asked: “How much are you automating of the loan process today?”. The results were astonishing. 50% of respondents said they did not use any automation tools at all, while only 31% used automated credit analysis and decision techniques.
How can automation help credit analyst create accurate financial spreads upon which to base their risk assessment and lending appetites?
Modern advanced loan origination systems have enhanced technology, which, when given the appropriate permissions, allows the lender, via a web-based portal, to interact with their commercial customers’ systems. It can, for example, extract relevant financial data from tax returns and accounting software to be used in a credit assessment.
Financial spreading automation can help the analyst to accurately and efficiently tabulate the borrower’s statements in order to rate them. By allowing automation tools like optical character recognition (OCR), machine learning, and other methods to read the financial information provided by the borrower, it’s now possible to map this data into a chart in the balance sheets, income/expenses, cash flow, and tax forms.
This process can be completed almost instantly. The lender may even pre-screen the borrower sco, re them, and then make an in-principle decision within minutes.
It is the ability to save time that allows analysts to do their risk assessments. It may involve data interpretation, ratio analyses, and forecasting to assess the financial risk and repayment capacity of the borrower. Credit analysis may also include automated credit rating based on probability of default models (PD) or loss-given default models (LGD). These tools instantly deliver essential risk metrics to assess loans.
The benefits are compounded when automated tools for customer management and credit assessment are integrated into the same platform. In the commercial lending world, one example is borrower groups. Each entity within the group has to be assessed individually for a risk score. If the lender’s policies allow, an automated platform for loan origination that instantly assigns group ratings based upon the combined financial strength of the leading borrower and cascaded ratings or distributed ratings by the parent entity can save significant time in the rating.
Credit Presentation & Choosing
Automating the approval of commercial loans is all about analyzing the data and information and then presenting them clearly in order to make a decision. Automating your lending process from beginning to end will provide you with accuracy, near-real-time data, and increased efficiency.
Most bankers can get a pretty good idea about their lending appetite after gathering information, analyzing financial statements, performing a ratio analysis, creating some scenarios, and completing a risk assessment. If it’s positive, you can prepare a credit application or presentation for the risk department to decide.
Many lenders prepare and collate several pieces of paper that are related but separate. They do this in a prescribed manner, which adds to the approval time, especially if it is a new relationship.
A solution for an automated credit application combines elements such as the Customer Management Module, Financial Analysis, and Risk Assessment with a loan structuring tool or collateral management system and electronic credit memo. Automated credit applications do not have to be as complicated as they may seem at first. The best-in-class platforms integrate seamlessly with the existing systems and applications that lenders already have in place to perform these functions.
There are only a few banking applications today that combine all stages of credit approval. By using the data already stored on the origination platform to create pre-configured documents that mirror a lender’s traditional paper-based forms, the analysis can be conducted automatically.
Software vendors have also taken over the final decision of whether to approve or reject the loan. Two loans are never alike in the world of commercial loaning. On the low loan value/high volume end of the spectrum, it is possible to observe the emergence and use of automatic decisions based on the policies and business rules specific to the lender. Automated decision-making is common in the retail credit industry.
Commercial loan decisions are still largely based on human judgment. Automation plays a major role in the pre-screening of applications, assisting loan officers with risk assessment, and preparing the proposal for decision-makers. In the decision-making process, mobile technology is becoming more prevalent. Lenders are equipping their executives with tablets, smartphones, and laptops loaded with apps that allow them to make decisions on the go. This reduces the time it takes to approve loans.
Covenants/Monitoring
The asset must be monitored and managed annually, quarterly, or even monthly after the loan origination. Banks face a major challenge in identifying a standard process for collecting financial data that will satisfy ticklers and covenants, as well as policy exceptions. When methods aren’t clearly defined, and manual tools are used, tracking can be risky. Moody’s Analytics has seen mid-tier lenders struggling with large portfolios of loan covenants that are still tracked using spreadsheets. Examiners are often skeptical of such methods and demand a robust solution.
Although automated covenant solutions are available outside an origination system for accuracy and efficiency, it is better to integrate them into the overall solution. The covenants can be recorded as part of the application process for the loan. This saves time and allows the details to be anchored in the approval record.
A calendar-based automated covenant/tickler provides peace of mind by ensuring that the right information is collected on time. If the correct documentation is not provided or certain covenants aren’t met, automated notifications will be sent. Dashboard alerts can be used to flag an impending or immediate breach via automated testing.
Portfolio Risk Management
With the traditional paper-based manual loan underwriting method, lenders struggle to understand what exposures exist in their portfolios and how they change over time. Most lenders set risk appetite limits and have stated their risk tolerances. Formulating these rules, however, is a pointless exercise unless the lender can access an accurate portfolio reporting tool.
The improved data integrity and data traceability, as well as the overall governance that comes with a platform of the highest quality, are powerful reasons to automate loan origination. Data integrity can be compromised by using multiple systems to store the same data. Data is stored on suboptimal systems, and the amount of keying/rekeying increases. Lenders spend a lot of time and money reconciling portfolio data when such conditions exist. It can take several weeks before a clear picture is revealed. By then, it may be too expensive and late to fix a problem or issue.
It is important not to underestimate the cost savings that can be attributed to accurate capital usage measurement. The direct costs of overstating risk-weighted assets in your balance sheet are substantial. At least one major European bank has saved several hundred million dollars in capital after a major data cleansing project. The real lesson, however, is not to let things get to this stage.
Automating the key stages in the loan origination procedure helps to ensure that data on risk is under robust governance and controls. Automating the process to provide key business insights via a powerful reporting tool can also add value.
Conclusion
Automation has improved the efficiency of many industries around the world. In many ways, banking was an innovator. However, the process of lending to small businesses and commercial enterprises is still done in the same manner as it was done decades ago.
The commercial lending landscape is changing. Many traditional lenders, spurred by the rise of technology-enabled competitors, are adopting automated methods to streamline their loan origination process. Competition is not the only factor. Lenders who recognize the need to improve efficiency, productivity, and customer service at higher levels will also implement technological solutions. Cost savings and the need to meet stricter regulatory examination standards are other factors that drive these lenders. Others are motivated by the desire to regain control over their data and gain more precise business insights.
Few, if any, lenders are motivated to use automation to reduce the human intelligence of commercial lending. Most see automation as a way to retain talent and use bankers’ time for things that really matter, like risk analysis and customer relation management, rather than administrative tasks.
Automating the loan underwriting process can be challenging, but it can also enhance the institution’s brand as a market leader and innovator.
