How Automation Can Enhance the Loan Origination Process
The global lending market is set to hit $8809.25 billion in the year 2025, at a CAGR of around 6%. [1]
Despite the repercussions of the COVID-19 pandemic, the global lending market saw double-digit percentage growth given the increased lending requirements from companies trying to restructure their operations in the event of the pandemic.
This was done, given the need to put new technologies and resources in place that adhere to the social distancing guidelines whilst affecting business operations at the very minimum. The pandemic coupled with the sudden increase in lending requirements pushed banks and financial institutions to upgrade their lending processes, something that they have been putting off for the longest time ever.
The need to process an increased number of loan requirements, churning out government-sanctioned PPP payments, all with limited resources at hand served as a major push to lending institutions, banks, and even credit unions to see the need to adopt fintech and integrate automation into their current loan origination processes.
There’s no question about the benefits of automation in the loan origination process (LOP). However, in this article, we do a deep dive to see how automation enhances the loan origination process at each step of the way.
The problem that automation is solving in LOP
There are three things that any lender hopes to achieve from their lending operations:
- Make a profit
- Manage risk
- Create value for the shareholders
To ensure they achieve all three, one of the most crucial tasks they must undertake is assessing the creditworthiness of their borrowers. What tools and resources they use to underwrite the application and the decision tools following that, play a major role in ascertaining the three outcomes mentioned above.
Streamlining the overall loan origination process and putting automated systems in place can ensure uniformity in the process, reduce errors, reduce turnaround time for each application as well as, boost returns on each application approved. The results would be a loan portfolio of improved quality and heightened customer satisfaction.
Banks and lending institutions are still struggling with providing customers with a seamless lending experience, given their heavy dependence on manual and paper-based lending systems. This results in slower decision making, errors in data entry and subsequent lending stages overlaps in data (double entry) and decreased transparency amongst all stakeholders.
Most lenders still use spreadsheets, which although an extremely useful tool when it comes to calculations and formulas, wasn’t made with LOP in mind. As such, the efficiency of a tool like a spreadsheet is often curtailed in these scenarios.
Automation at various stages of LOP
There are 5 distinct stages when it comes the lending process:
- Customer management (LOP)
- Credit analysis (LOP)
- Credit Decisioning (LOP)
- Monitoring covenants (Post loan origination)
- Portfolio risk management (Post loan origination)
Each of these five steps can be automated and therefore boosted to maximize efficiency and output when it comes to the three outcomes every lender looks out for, as mentioned earlier. For this article, we are going to focus on the first three steps that are expressly focused on the loan origination process (LOP)
Customer Management
The first step in any lending process, irrespective of the kind of institution, is collecting customer information. This process is generally done manually by filling up forms and submitting necessary documents to the bank/financial institution by the applicant. The information is then again manually inserted on digital platforms (data entry) by bank employees.
This step entails both errors and frequent back and forth with the customer given missing information or gaps in the information received or fed into the bank software.
Automating this step by providing customers with web-based portals or online applications that customers can fill up themselves and which will only be accepted once all the required documents are in place can reduce both errors in manual data entry, double-entry, and lack of documents that lead to increased turnaround times and reduced customer satisfaction.
Credit analysis
Credit analysis is one of the most crucial stages in the risk assessment process. Based on the information received from the applicant, the bank creates a financial spread. This spread helps them in carrying out the risk assessment. Creating the financial spread is often a manual process carried out by the credit analyst. Yet again, given the manual nature of the process, it is both time consuming and error ridden.
Automation can help reduce the errors in this process as well as reduce the time needed to create each spread by not only speeding up the process but also allowing the analyst access to more information than what has been given to them by the customer during application.
Today’s lending automation software with improved tech integrations, allow appropriate permissions that give lenders access to multiple systems from where they are extracting the kind of data needed to ascertain the creditworthiness of the applicants. These could be systems that manage accounting software, or tax returns, etc.
The entire process can be conducted in mere minutes, right from pre-screening, and scoring the borrower to providing a credit decision.
Credit decisioning
Once the financial spread is in place and after running the necessary analysis and testing out projected risk scenarios, credit analysts receive a pretty good picture of the risk associated with the application. Now it is the task of the risk department to confirm their decision based on the documentation presented to them by the credit analysts.
The entire process of collating and verifying documents is mostly a manual one. Apart from using models for financial analysis and risk assessment, most of these tasks are still massively manual. Having automated credit decisioning software can help reduce errors, process outliers and related inefficiencies. Most lending software for business allows third-party API integrations that allow banks and lending institutions to automate their credit decisions and improve their returns by ensuring covenants that are a sure shot win for both parties.
With Big Data and models such as predictive analytics and regression analysis in place, lenders can now confidently churn out loans that minimize their risk and maximize profits.
Conclusion
Automation is synonymous with efficiency. Boosted efficiency and optimization of the processes with a minimum margin of error are a given when it comes to automation. While many might think in the lines of “who needs to reinvent the wheel” when it comes to lending processes, traditional LOP might seem pretty much like horse carts rolling down cobbled streets while SUVs are zipping past them with unparalleled speed and safety benefits.
Given the spurt of small and medium-sized enterprises dotting the global landscape and each with their credit requirements, the lending industry is no longer catering to only individuals and enterprises. It is catering to a much larger (and growing) sector that is fighting to stay relevant, competing, and growing even in a pandemic-stricken world.
This means lenders now have opportunities galore. All they need is the right processes in place to find their niche and start operating and growing in their market. By giving the SMBs the kind of financial support they are looking for, be it working capital loans or cash flow assistance, with the right lending automation software, lenders can win big in any market.
Citations
[1] http://www.businesswire.com/news/home/20210308005616/en/Global-Lending-Market-Report-2021-COVID-19-Impact-and-Recovery-to-2030---ResearchAndMarkets.com
[2] http://development.asia/explainer/heres-how-alternative-credit-scoring-can-improve-poors-access-loans
