Why Automation Improves Loan Origination
The Gen-Z consumer is depending on AI-driven solutions for most of his banking services. When checking out loan offers, he is interested in seeking a product that can cater to his requirement with ease and speed. They access Omni channels and search for loan products that need hassle-free documentation processes and transparent yet fast approvals or rejections. This would mean that even if a loan application is rejected for any reason, an applicant will appreciate a straightforward report which outlines why he does not meet the approval criteria.
The explosion of the lending business aided by technology is changing the dynamics of the banking industry. Although the pandemic restricted the movement of people outside of their homes and nearby vicinity, the lending business trajectory has grown during this period. The reassuring growth of a lending business when governments were imposing lockdowns would not be accomplished if it was not for the umpteen benefits of digitization. The benefits of automation extend beyond simple tasks. Loan underwriting software helps in reimagining the way bankers spend long hours after carrying out complex calculations in legacy banking. It simply integrates all the available data, checks for authenticity of the financial information and documents uploaded from consumers’ end, and makes credit assessment in an efficient and accurate mode.
The benefits of loan origination solutions are many and primarily try to achieve operational efficiency by reducing the time and costs with accurate data integration, mitigating risk constantly through tracking and monitoring tools and creating an enhanced experience for all the stakeholders like customers, employees, and shareholders of the business. Now that we know that automating the banking operations has a good impact let us probe some of those benefits that improve loan origination:-
Saves time, money, and manpower
A digital loan origination solution automates the whole cycle of a loan starting from application to servicing of the last payment. All the stages involve initial customer engagement to collect data and documents related to the applicant’s information, reviewing the given data with external sources to establish the creditworthiness of the prospect, loan disbursal, and monitoring and tracking the payments and changes in the client’s risk profile to back with extra collaterals and covenants if required are automated.
Automation reduces the scope for errors, saves time as duplication of data or compilation is reduced, and calculates the credit analytics through algorithms that perform the tedious task without errors. The credit approval is based on the assessment and is in principle with regulatory measures and the credit policy of the lender. Again, this entire process is performed in very less time. The increased number of loan applications that can be processed by an automation system translates into saved time and manpower and thus saves money.
Integrated data source
Machine learning is very useful in extracting valuable information from unstructured data. In manual banking different procedures of loans are performed by different departments. The employees engaging the customer in the initial stages are as clueless as the applicant till an email informing the credit decision is announced. The credit approval is done by the underwriters who analyze a lot of information through spreadsheets and complex calculations. But due to the efficiency of robotic processes, important procedures like credit analysis, credit decision, and risk management are carried out within a very short time. The data is collated with third-party API integrations and sourced as a single truth. As the turnaround time to announce the credit decision is quick, there is no need to keep updating the status of the loan application. Yet this is updated on a real-time basis.
Serve the underserved
Lending institutions restricted their operations to fully paced out areas that attract capital and talent. As a result, small businesses and faraway towns with limited resources have never been able to avail of lending facilities. With time at hand and increased operational efficiency which may soon make the loan origination process on partly auto-pilot mode, it is time to focus on markets that have not been penetrated by lenders.
If mom-and-pop shops and small local businesses start receiving the support of lending institutions, the ability to reach out to nearby markets with local produce increases. Capital from loans can be used to build the business and expand horizons. A farmer growing organic fruits and berries can start making jellies and jams that can be sold in nearby big cities. In turn, the people living in big cities get to experience locally grown, fresh food which is a healthier version of what they are used to. Inclusiveness of underserviced areas will not just increase the bank’s lending business but result in a dominion effect on the trade ecosystem.
Risk-adjusted loans
Machine learning through neural networking and third-party API integration can collate all the data. The manual process of analyzing the data from documents collected physically and verifying the data used to be a lengthy and tedious process that was prone to human errors. Based on the loan product, now the credit analysis is done by AI tools that read the spreadsheet and come with the results of a credit assessment. In some cases, the turnaround time from submitting a loan application and credit approval based on assessment is approximately ten minutes. In rare cases, the turnaround time may stretch to the next business day, yet it is a remarkable feat in terms of the legacy loan origination process. And all these feats are achieved without compromising regulations, the internal credit policy of the lender, or the risk metrics of the loan product. Only eligible applicants whose creditworthiness is established from an accurate determination of information are approved to avail of the loan. Even in the case of first-time applications where a credit score is not established, automated LOS reads the borrower’s solvency.
Conclusion:
Automation of loan origination is an end-to-end process that efficiently handles all the procedures from client engagement, data integration, and underwriting to the closure of a loan. The digitization of lending services will reduce the burden on employees and increase customer satisfaction while maintaining the highest standards of risk management.
