Globally, the lending business is increasing and scaling new highs despite a pandemic that changed the way workforces moved and operated. Despite all the impediments caused by COVID-19 such as lockdowns, lending businesses of major banks have seen considerable growth. This feat was possible because of the technological upgradation of these banks to digital lending solutions that automate the whole process of loan origination and are highly efficient.
With the advent of AI-driven solutions coupled with consumers’ awareness of the benefits of automated lending products, the loan origination process will never be the same again. Machine learning helps in integrating the databases and processing the loan approvals at a faster rate than legacy banking. Consumers are exercising clear choice for speed and efficiency of a loan application processed after they check out LOS software products.
The technological advancements in AI were shaped long before the global pandemic. However, the need for social distancing helped catapult its acceptance in the banking industry. After experiencing the benefits of automating the loan origination solutions, things will only progress towards incorporating more machine learning in every possible facet of the lending and banking business. Now that banks and lending institutions have addressed the elephant in the room by upgrading their lending process, a requirement that was on the back burner for some time, digitization is the only way forward.
Now that it is imperative that loan origination software and digital lending are driven by automation and technological upgrades, let us see the scope of these changes shortly.
Prospective consumers who are looking to take a loan can use self-serving Omni channels of distribution to avail of loan products without visiting a brick and mortar bank. The initial stage of any loan origination is collecting the required documents related to the applicant’s identity and financial information like pay stubs, bank statements, and tax returns. In legacy banking, this step was a tedious process that involved document collection and again data was filled manually by a bank employee. This is the stage where the maximum number of human errors can occur.
With automation, all the requisite documents are uploaded by the applicant by scanning the images and giving permission to seek missing information or cross-verify its authenticity. They can access either a web portal or a bank’s app to fill in their information, and scan and submit their documents. Till all the fields are filled out, the application is not accepted. The applicant also is prompted at every stage with helpful messages that assist in filling out the application online.
Data-entry-related errors are avoided as information will be accepted only if it is matching. For example, if a person is entering a wrong zip code, a prompt appears stating the given information may be wrong or not relevant. As all the documents are scanned and reviewed online, the increase in turnaround time because of the lack of documents is reduced. With these efficient features, winning the customer’s appreciation is ascertained for digital loan prospects.
Analyzing the loan applicant’s ability to pay for his loan is one of the most important stages of a loan cycle. Based on the information sourced from a loan applicant, the banker has to create spreadsheets to understand if cash flows are adequate to meet the periodical loan repayments. Depending on the data available, the banker may ask for additional collaterals or a higher rate of return if the prospective loan carries a higher risk of turning into a stressed asset. This entire process is manual in traditional banking and often is the cause for the delay in the processing of a loan application.
Automation of this process through digital loan software reduces the time required to calculate through spreadsheets. Artificial intelligence can automate complex formulas and spreadsheets for real-time applications. Decision-making is facilitated in split seconds based on the real-time results. This entire process is error-free and time-saving, as different spreadsheets need not be created. It also gives recommendations to approve or disapprove a loan. In this manner, the credit decision is free of human intervention and quality loans are processed to reduce their chance of turning into bad loans.
With the projection of fewer clients walking into avail loans, banks can downsize the real-estate costs required to maintain the infrastructure and large workforces. Centralized offices can replace the spread of multiple branches thereby creating a single route and reducing costs. The time saved from eliminating performing repetitive tasks in processing loans can be used to strengthen the relationship with consumers with help of digital LOS.
When efficiency, accuracy, and risk mitigation are enhanced through automation of loan origination, more loans are serviced at reduced costs and lower risk than the ones that were serviced in the past.
AI can prevent fraud by flagging suspicious transactions that are out of character. An alert warning the bank is immediately applied. Even genuine customers who may be trapped with suspicious phishing links are alerted in advance, thus reducing the probability of a mishap. Such measures not only deter defrauding offenders, but also protect the reputation a bank has built over the years. When customers realize that they are banking with a trustworthy organization that is efficient with its process, they tend to repeat their business in the future.
The future of lending across the globe is set out to be automated and this will not be backward ever. Banks and mortgage institutions will do well to embrace the change and let the old order phase out and ring in the new one.short url: