Introduction
The advent of technology has undoubtedly changed the way things function in the world. In times where people depended on traditional banking and other offline sources for loans, credit underwriting was a tedious and very long process for borrowers. Now, with lending accessible through digital platforms, credit underwriting has also had to evolve to fit the customer’s expectations.
Borrowers prefer reaching out to a lender that is not going to demand tons of paperwork and then say no after a 6-week process. This is where automation has made life easier. Automation of credit underwriting for banks and credit unions covers the shortcomings of the traditional underwriting techniques. The automated underwriting process models the legacy underwriting system and powers it with artificial intelligence and machine learning to support faster yet cheaper and more effective credit underwriting.
What do you mean by Automated Underwriting?
The traditional lending industry is now pivoting towards automated credit underwriting as it not only shortens the wait time for clients but also helps banks stay competitive and enhance the customer experience. This growth can be tracked from the pace at which the market for Global Lending Software has been growing. The market was valued at USD 2,615.8 million in 2017 and is estimated to grow up to USD 5,579.4 million by 2024, witnessing a CAGR of 11.6% over the forecast period.
Automated underwriting can be employed in almost all types of personal and small business loans and is primarily employed in the conventional loan processes which have standardized underwriting procedures and elementary amortization schedule for installment payments. But this is just a start. Startups are emerging who are now targeting the corporate loan market and other complicated fixed income instruments which were earlier a strict purview of face-to-face “relationship” banking.
The Benefits of Automated Credit Underwriting
Following are some crucial ways that ease the underwriting process and support it:
1. Better Productivity
Here, it is a win-win for both the lenders and the borrowers. The automated credit underwriting system saves time for both the parties which facilitates quicker decisioning and reduced processes that traditional underwriting requires. The automated underwriting also ensures the borrower’s requirement of shorter processes is met, but with accuracy that does not imperil the lenders’ balance sheet as well.
2. Better Decision Making
Algorithms do not make clerical mistakes. A human, however efficient is prone to have a bad day, which can cost a lender millions of dollars in non-performing loans. With the power of machine learning and an increase in data on such loans, the automated systems are becoming better at predicting which loans will do better.
3. Smarter Fraud Detection
Loan fraud is steadily rising. Credit card fraud is a multi-billion dollar industry in itself. Automation cuts down the risk of fraud substantially and systematically. How? The robotic processes use advanced predictive analytics that can swiftly trace risks that are associated with disbursing loan to a client. These processes employed raise red flags wherever there is a discrepancy detected, which in turn enables smarter fraud detection.
4. Empowered customer experience
Loan documentation might seem just a back-office process. But is critical to ensure you are following all regulations. With lawmakers levying billions of dollars on fines on erring banks, you do not want to be next on the list. Automated documentation of underwriting and loan disbursal ensures a seamless solution for the bank. It is also cheaper than outsourcing it to a third world country and hoping your data is not hacked.
5. Consistency in Underwriting
The automation increases the bank’s ability to underwrite, approve and document the credit in a more personalized way but yet be compliant to the bank’s policies. It overcomes the shortcomings of the bank staff’s ability to interpret the bank policies that may differ from employee to employee. Also, automation considers all the loan-risk factors associated with loan policies that might be missed in traditional underwriting but are crucial to be considered for a loan decision.
6. Scalability
It is tough to achieve scalability in a traditional process of underwriting as in order to understand varied documents for better classification, analysis, stacking and extraction, a good amount of understanding of the lending industry is crucial. You need an amazing army of experienced analysts and such analysts cost a lot of money. This can be overcome by automated underwriting. One system can be used for your entire underwriting process. The analysts will focus on making the system better rather than vetting each individual case.
7. Easier and improved compliance to the regulatory requirements
The best part of automation is that you update a rule and it gets implemented throughout as per the filters you put. Thus, the regulatory requirements which might sometimes be overlooked in a traditional system, are always in check in the automated systems, which results in better compliance.
8. Better Auditing
Automation of documenting processes eases out the documenting and lending processes which further powers easier and hassle-free auditing. This serves as a booster for traditional banks and credit unions as it facilitates accuracy and keeps fraud in check.
9. Consistent and better-defined workflows
Needless to put, the process of automation works via a defined process which makes underwriting well-defined, resulting in the consistency of the process and results. It overcomes the flaw of traditional underwriting where the risk of missing out on details is always present and you run the risk of misinterpretation or personal bias.
Conclusion
With online lending disrupting the traditional lending sector, the advent of automated credit underwriting has yet again revolutionized the credit underwriting process. While automated underwriting started off with lending startups, it has gained adoption in the traditional banking industry as well. The role of human to determine creditworthiness is not diminished via credit underwriting automation. It is in fact enhanced as the analysts are now empowered with powerful algorithms which do the heavy lifting in pattern recognition and repetitive tasks.
Even more importantly, it is becoming impossible to serve customers if your competitor is giving them a credit decision in 5 minutes and you need 5 days to give them an offer. Enhanced service or even better interest rates will not be a big enough moat.