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How is AI Bolstering Lending Business Growth Trajectory?
- AS Team
- Oct 18, 2022
Artificial intelligence and machine learning are deployed in the new improved LOS software by banks in the end-to-end loan origination cycle.
The new age digital loan platforms powered by AI tools can automate the workflow process by optimizing it and predicting the next course of action with a high probability of success. It simply means that the loan process will be faster, error-free, and meet the quality standards to avoid increased risk in the future.
Regulatory impact on lending
Lending is important for economic growth, where the liquidity in the financial and trade market increases with the money multiplier effect. After the subprime crisis and financial apocalypse of 2008, global banks have enhanced regulations like Basel III rules. Banks must comply with these rules to avoid systemic risks.
Apart from the regulatory requirement, banks need to protect their interest and avoid lending to applicants who can't meet its eligibility criteria. With the number of applications banks receive, it is not possible to reduce the human errors and biases associated with the loan process.
AI-based digital lending software has algorithms that can overcome the limitations of traditional processes in mitigating risks and measuring up to the regulatory framework.
Scope of AI in lending
Digital loan software uses AI tools like cognitive thinking and ML tools encompassing neural networks to aid the process of mapping different functions for decision-making. With the help of the third-party integrated APIs, verification of borrower information from banks, income tax returns, and credit bureaus becomes easy and establishes the accuracy of the information submitted.
Let us check five outcomes of AI and ML-based digital tools used by the latest loan origination software that is changing the way the lending process was carried out for the better:
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Data mining for better outcomes
Lending to large corporations is relatively easy as the information is available in public domains, and audit data is available with exchanges and organizations’ websites. Offering personal loans and education loans is also based on individuals' credit scores and income sources to prove they can pay. However commercial lending to unlisted businesses is risky. Yet it is vital for the banks, consumers, and the economy as a whole to focus on the growth of commercial lending.
AI modeling can be customized to verify factors that have a holistic approach to commercial lending where there is potential but insufficient data.
Instead of rejecting the application as it was done in the traditional process, LOS with AI algorithms helps screen for macroeconomic information like sector growth, borrower's ability to scale the business, probability of success or failure, and sensitivity factors that can affect the business cash flows.
With intelligent insights and statistical modeling, target time series can be run to identify the macroeconomic variables that can work in favor of the approval of credit. In this manner, credit officers can establish quality loan applications from the load even for otherwise doubtful cases.
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Increased efficiency
The initial cycle of loan origination is largely repetitive work yet crucial for taking the right decisions. With AI automated digital loan software tasks that are tedious, repetitive in nature, and predictable are automated. For instance, when the borrower uploads his information, his address and personal details are captured from the data source through scanning and filling of templates. If the address has to be filled in three different spaces, all the information is auto-filled. Once the application is filled through accessible multi-channels, credit assessment and underwriting decisions are carried out by the software, mostly within twenty-four hours from the time the loan application was submitted. Post-approval cycle like funds disbursal, monitoring, and managing covenants is also carried out by AI automation-enabled tools.
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Enhanced underwriting
AI eliminates the errors that can occur due to insufficient data or unverifiable information. Right during the onboarding of customers, their data is checked for its sufficiency. If the applicant forgets to submit any data, the application is not accepted with a prompt to submit one to consider and further process the application.
The given information is further verified through API integration to establish its truth and reject the fraudulent application after raising a red flag against the applicant for future reference.
The number of checks that can be carried out through a LOS is more than human capabilities. All these factors improve the underwriting process and increase the value of the portfolio by approving only loan applications that promise growth and stability.
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Improve customer experience
When commercial loan applicants, specifically the SME borrowers, have serviced a loan in 1-2 business days, they stand to gain the advantage of time. These businesses cannot borrow ahead of time as they pay interest for funds that are not used. In the traditional system, often SME borrowers applied for loans when they did not need them so that they could be available to be used when they needed them, simply because of the length of time it took to approve a loan.
When the timelines are shortened, value is created while enriching the customer experience and laying a cornerstone for a long-standing relationship.
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Elevated bankers role
Several banks took time to adopt automation for lending as many biases and myths engulfed the future of human role in the banking sector. However, the purpose of AI and automation in lending is to complement the presence of humans and not replace them with their jobs. Despite the increase in better cognitive algorithms to make robots speak and work like humans, many functions are limited to human capabilities. In fact with automation streamlining the entire process, bankers have sufficient time to engage their customers and enable value-added services.
Conclusion:
The quality of a loan portfolio depends on various factors like the borrower's net worth valuation offered as collateral, covenants that can be agreed upon, future macroeconomic scenarios, and the growth story of the sector and business for which the loan was applied. AI can analyze all these data points in less time while enabling coherent credit decisions. This is the reason loan automation is changing the way lending works for the better.
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