Post COVID, lenders are in a difficult situation. Non-performing loans have increased in number. As a result, default rates have risen, At the same time, loan book growth has stagnated because of lower consumer spending and investments post covid. Hence, banks are facing double whammy.
As per the latest report published by S&P Global, global banking earnings will decrease by a double-digit percentage — and maybe by over 25% depending on how well banks can reduce their overall cost of operations, improve underwriting process and improve the overall book quality.
The Need for Digitization
Banks have a limited perspective of a customer’s economic sustainability in the current uncertain climate. Manual underwriting techniques, on the other hand, detract from rather than enhance a financial institution’s ability to handle the present credit crisis.
To make decisions based on candidates’ applications, early loan origination systems depended on a small number of factors (10 to 20 maximum). In the event of doubt, the system referred the application to a human advisor for further review, as shown in the image above.
The financial arms of Chinese mega apps — WeBank, MYBank, and XWbank – condensed the entire loan origination process flow into a three-step model termed “310,” using upcoming technologies such as AI, blockchain, and cloud computing.
- 3 minutes to fill out the online loan application form.
- 1 second between loan approval and funds transfer.
- 0 human interventions in between.
This makes you wonder about the dangers. Last year, the average NPL ratio for the three banks was 1%. But it isn’t just Chinese digital banks that are leading the way in lending. FinTech startups supplied 38 percent of personal loans in the United States in 2018, while banks provided 28 percent and credit unions 21 percent.
In 2013, banks issued the majority of personal loans (40%) followed by credit institutions (31%), with startups accounting for only 5%. Pricing and convenience are still the top factors for choosing a lending provider, according to a 2020 consumer survey conducted by Nomis.
Source: Nomis
How to Approach Loan Origination Automation
Banks in 2021 need an end-to-end loan application process to stand out in the fintech wave.
However, due to legacy software constraints, it may not be possible for every financial institution to digitize the entire loan origination cycle in one go. This isn’t, however, a legitimate reason to put off changes.
All steps of the loan origination process can be automated gradually. You may decrease the operational and financial pressure on your systems while still reaping the benefits of automation by choosing for staged adoption.
Let’s go on a tour around automation together, shall we?
Shift to digital loan origination process
Automating paper-based loan applications is low-hanging fruit. Implementing an online portal for gathering consumer information and requesting papers, as well as doing basic KYC, allows you to:
- Customer sign-ups and remote onboarding process
- Errors and missing documents have decreased in number.
- Reduce the amount of stress on the operating team.
Borrowers are on board as well: 67 percent of Millennials are comfortable filling out a mortgage application on their computer.
Check out our guide on digital account opening systems to learn more about how to create an easy and productive online loan application procedure. Many of the stages are also applicable to lending in terms of design and compliance.
Think beyond traditional data points and processes
As previously said, a simple automated loan origination system can assist in sorting all incoming applications into the appropriate buckets: quick yes, requires further consideration, and a firm no. For the past decade, most systems have been doing just that.
The ultimate value of automation, however, resides in producing enhanced scorecards for lenders using both traditional and alternative credit data sources, such as self-submitted data, rental payments, and asset ownership information. Mobile data presents an intriguing possibility for lenders because it serves as a strong proxy for credit-thin or credit-invisible consumers’ repayment possibilities.
Adopt predictive analytics for risk evaluation
Big data, predictive analytics, and machine learning algorithms are a trio of technologies that can increase credit scoring accuracy and precision. The most recent generation of algorithms can sift through massive amounts of data to uncover hidden patterns and create very accurate predictions about how a customer’s attributes influence their capacity to repay their loan on time.
In a matter of minutes, such sophisticated algorithms can crunch forecasts and offer results to your team. Alternatively, they can approve all borrowers who meet the given criteria automatically. In any situation, you have a very realistic picture of prospective hazards on which to take action.
Automated and real-time loan underwriting
Underwriters can save time by automating the process of drawing conclusions and suggesting the best loan conditions. This improves your ability to make real-time judgments and appeal to price-conscious customers looking for the greatest bargain (without magnifying the risk of a default).
Furthermore, computerized loan underwriting can result in:
- Compliance obligations to be implemented in a unified manner.
- Improved auditing and consistent loan results
- Reduced volume of obligatory manual reviews leads to increased team productivity.
Built-in loan origination system analytics can also assist your company in identifying decisions that increase credit risks and, as a result, boost portfolio yields by delivering the best rates at the best times.
Instant loan approvals and disbursement
Finally, you can set up an online system for borrowers to approve loans. To confirm customer identities and automatically save document copies in your systems, you can use e-signature software or biometrics.
For smaller personal loans, you should additionally enable same-day or next-day account funding. In a tailored communication, set the timetable expectations for increasing amounts of funding.
Deploy Early Warning System (EWS) post disbursement
Post disbursement, banks need to have a reliable solution to keep a track of all the loans and identify irregularities to take immediate action. EWS by Pirimid looks for 300+ data points on a real-time basis to alert lenders if there is a reasonable probability of the borrower defaulting next EMI.
The Bottomline
Lending operations can be digitized and automated to bring you closer to your consumers. Your institution may gain new business despite financial constraints by playing the role of an eager, flexible, and data-driven assistant, methodically rebuilding its portfolio one digitally verified borrower at a time.