In the current commercial lending market, financial institutions are increasingly mindful of improving their practices in many areas to increase efficiency, decision speed, and productivity, and to enhance their customer experience. Here automation has a big role to play. The process of automation can automate and streamline your commercial loan origination process, increase the productivity of your lending officers and make your customers happier. So, let’s know in detail how automation can improve the credit process.
Commercial lending is about generating economic benefit through the funding of enterprises, while ensuring the lender can make a profit, create shareholder value, and manage risk. Assessing the creditworthiness of any business can be a challenging task. The tools a financial institution uses to do so can impact underwriting standards, timely approval, cost, and the scale of any unpredicted losses. Why are so many banks today struggling to achieve these objectives?
Many lenders use manual and paper-based loan approval procedures. As a result, they have slower decision times than what many customers want, and an internal data management problem that creates more work for bankers. Manual and paper-based underwriting practices lack consistency, auditability, and accuracy, and are above all, time-consuming.
Automation can allow for the streamlining of disparate systems, provide reliable and consistent dataflow for any stage of the loan origination process and quicken the overall process, while delivering solid audit and control benefits. Let us see how a typical automated loan origination process works.
Customer Management
The first step in any loan decision is collecting the financial and other necessary information from the prospect or customer. Today, this task can be labor-intensive and difficult to complete. Often it is dominated by form filling, electronic or printed documents, and a physical customer file. The more often the information contained in these electronic and paper documents is entered and rekeyed into the lender’s systems, the greater the possibility of inaccurate data being recorded. This data collection is one of the most tedious jobs. Automation can mitigate the inconsistency and delays of manually collecting financial data and other mandatory customer information. Customer-facing web-based portals and application program interfaces (APIs) can facilitate digital onboarding of new prospect and existing customer data straight to the lender’s loan origination platform.
More advanced automated loan origination platforms are also capable of receiving data feeds that pre-populate customer information fields within the origination platform. One of the more useful applications is the import of customer ownership hierarchies. Organization diagrams, visually depicting the key entities within a group and the inter-relationship between parties, can be uploaded to create the customer ownership hierarchy automatically. For complex borrowers, importing such information can relieve a huge administrative burden.
In many financial institutions, it is normal practice for the business front office and the risk department to maintain their own separate records for the same customer. The later might restrict access to certain information for compliance reasons, but usually this duplication leads to unnecessary inefficiency and inaccuracies. An automated credit origination platform enables multiple teams across departments or locations to access the same customer documents electronically, according to their need and purpose, creating a single source of truth. The application of user identity and access protocols within the system can be effective, maintaining the integrity of the customer information and ensuring only those individuals with the correct privileges gain access information. From an audit and control perspective, this satisfies examination considerably more than open access file directories.
Credit Analysis
One of the most important stages of the commercial risk assessment process is spreading the financial data you have received from your prospect or customer, typically another manual and repetitive task. Automation can play a big role here.
Today’s advanced loan origination software has enhanced technology that, with appropriate permissions, allows the lender to interact via a web portal with its commercial customer’s systems. For example, it can extract the relevant financial data required for a credit risk assessment from accounting software, tax returns, and other documents. Automated financial spreading can assist the analyst by accurately and efficiently tabulating the borrower’s financial statements for the rating process. Allowing automation tools such as optical character recognition (OCR) and machine learning methodologies to read the borrower-supplied financial information, it is now possible to map that data into a chart of accounts in the balance sheet, income/expense, cash flow, and tax forms. The process can occur almost instantaneously and even allow the lender to pre-screen, score the borrower, and provide an in-principle credit decision in a matter of minutes.
Credit Decisioning
Automation in the commercial loan approval process is about mining the appropriate data and information, and presenting it clearly to make a credit decision. For many lenders, the credit application represents another manual exercise in preparing and collating several separate, yet related, pieces of paper, often in a highly prescribed fashion, adding to the processing time for approval, especially for a new relationship.
An automated credit application solution combines the elements of the customer management module, financial analysis, and risk assessment with some form of loan structuring tool, collateral management system, and electronic credit memorandum. In today’s banking software landscape, there are a few applications that package all the stages together for credit approval. However, by using the data and information already stored in the origination platform, pre-configured document templates mirroring a lender’s paper-based credit forms can be automatically produced to conduct their analysis.
The final step, the decision to approve or decline the loan, has also been made-over by software vendors. In the world of commercial lending, two loans are never the same. At the high volume/low loan value end of the spectrum, it is possible to see the emergence of auto-decisioning based on the particular policies and business rules of the lender. In the retail credit environment, automatic decision making is already commonplace.
Automation is playing a significant role in pre-screening applications and assisting loan officers to assess risk and prepare the proposal for the decision maker. Mobile enablement, in particular, is increasingly used in the decision-making step. Lenders of all sizes are arming their executives with laptops, smart phones, and tablet devices fully loaded with applications enabling them to make lending decisions while on the move, once again driving down the time to approval.
Portfolio Risk Management
With traditional manual, paper-based loan underwriting methods, lenders often struggle to see what exposures are in the portfolio and to see how these exposures change over time. All lenders have stated risk appetite tolerances and most set appropriate risk-based portfolio limits to guide their loan officers. However, formulating these rules is an academic exercise, unless the lender has an accurate portfolio reporting tool at their hand.
A powerful rationale for automating the loan origination process rests with the improved data integrity, data lineage, and overall governance that comes with a best in class origination platform. We have already discussed how data integrity is compromised when several systems are used to store the same data. The amount of keying and rekeying is multiplied and data is stored in sub-optimal systems. When conditions such as this exist, lenders spend considerable time and resources reconciling their portfolio data before they can usefully analyze it. Several weeks can elapse before an accurate picture emerges, by which time it might be too late and costly to address a particular issue or problem.
Automating key stages of the loan origination process helps ensure that risk data is subject to robust governance and control. Further automating, to deliver key business insights through a powerful business reporting tool can add significant value as well.
Conclusion
Automation has increased the efficiency of numerous industries worldwide. Banking was, in many ways, an innovation pioneer, however the business of originating small business and commercial loans is still carried on much the same way it was decades ago.
The landscape for commercial lending is now changing. Spurred on by the emergence of more technology enabled competitors, many traditional lenders are getting in on the act by adopting automation methods in their loan origination processes. Competition is far from the only impetus. Lenders that recognize a need to be more efficient, productive, and responsive to their customers, with higher levels of service, also look to implement technological solutions. These lenders are also driven by cost savings and requirements to meet more stringent regulatory exam standards. For others, the ability to take back control of their data and to gain sharper, more accurate business insights is the motive.
Automation enables lending organizations to process more qualified loans each day – and at a lower cost per loan – providing these organizations the much needed advantage to stay competitive, in a dynamic business environment.
Insight Consultants end-to-end loan automation solution
Insight Consultant’s, technology solutions offer a multitude of solution accelerators, which ensure faster reaction to complex market solutions. We are experts in the loan cycle management. Our solution entirely scalable and can be deployed in your existing system for meeting compliance requirements in a rapidly changing environment. It supports decisions across the entire retail lending lifecycle by automating origination and decisioning workflows, assesses credit risks, calculating pricings and manages the integration with third party data providers.
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