Access to credit is a critical element for families, individuals, and businesses to build wealth, achieve economic stability, and climb the economic ladder. The process to gauge the creditworthiness of borrowers or credit underwriting is therefore important to boost the lending business.
Credit underwriting used to be a long and tedious process for borrowers with traditional banking and other offline sources. Today, lending is accessible via digital platforms, which has led to changes in credit underwriting to keep up with the needs of the public. It has eliminated operational bottlenecks, leading to more streamlined lending processes.
The use of machine learning (ML) and other software helps lenders make underwriting decisions faster, with improved accuracy, and with less human involvement. A lender’s ability to draw actionable conclusions from a large pool of data from ML models is key to diversifying and offering high-quality services to clients. In this article, we will understand the importance of automated credit underwriting software and look at how machine learning can help transform current practices.
What is Automated Credit Underwriting Software?
Underwriting is central to the credit business. An automated credit underwriting software helps loan officers calculate an applicant’s credit score based on their annual income, credit history, and assets.
Automated processes like Robotic Process Automation (RPA) and machine learning help Fintech companies collect client information and recommend loan approval conditions based on that information.
Using machine learning underwriting models, many small business owners and consumers can have their creditworthiness assessed and decided with ease.
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How Machine Learning Simplifies the Credit Underwriting Process
With exponentially increasing data and computing power, machine learning now has a huge impact on our everyday lives and a good example of this is credit underwriting.
Banks and lenders can use ML and AI solutions to evaluate creditworthiness to increase credit availability and/or reduce credit costs. Lenders typically have a lot of data on an applicant when they underwrite the loan, which can be leveraged to improve underwriting models, reduce the cost of customer acquisition, and establish early warning systems. Some of the benefits of incorporating Machine Learning include:
- Enhancing the accuracy of predicted default risk to provide credit to more creditworthy borrowers.
- Verifying customer profiles with ease.
- Eliminating human errors.
- Minimising defaults and losses.
- Reduced mispricing because of inaccurate estimations of default risk, along with improved credit terms offered to some applicants.
- Developing and updating models more rapidly and more cost-effectively.
With alternative data, such as rent or mortgage payments, lenders can provide loans to more people. Although they may be good candidates to repay a mortgage, they might have been rejected otherwise because, for example, they lack a traditional credit history.
Machine Learning in Credit Underwriting Software
Here are some processes machine learning and related technologies can play a huge impact on:
1. Loan Application
With machine learning, lending firms and financial institutions can find complex patterns and relevant variables in the data that were not visible before. Access to this type of information allows lenders to select borrowers for loans.
A credit risk assessment based on such information can be more accurate than conventional rules-based underwriting. ML models can track and monitor all incoming payments to track defaulter loans and calculate a loan default score as time progresses. In cases of high-risk customers, the software prompts loan officers to look for alternatives.
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Consumer mortgage lending has always been paper-intensive since thousands of documents are required for each application. In mortgage lending processes, these requirements arise because organisations are dependent on various databases, workflow tools, systems, and reports, which require them to access various resources in order to complete specific processes. Machine learning can automate routine tasks and set criteria for whitelisting potential customers, approving or rejecting borrowers by scanning documents and auto-categorising them without any human intervention.
3. Credit Assessment
ML-based automation allows lenders to program the creditworthiness and risk parameters into an automated lending solution, enabling them to replace conventional credit assessments, which are time-consuming and error-prone. The automated mechanism allows even relatively inexperienced personnel to screen loans quickly, thereby providing a faster turnaround time for customers.
Small businesses that may not fit into conventional approval criteria can benefit from credit risk assessments with extensive customisation to suit different types of loans with the predictive power of machine learning.
4. Loan Disbursal
Usually, banks and non-bank financial companies take a few weeks to approve a loan through traditional lending platforms. Digital lending and the combined power of RPA and ML allow users to receive approval in minutes. These technologies make use of factors like online purchases, utility bill payment history, and social media profiles to approve the customers who meet the criteria.
Lending is a vast business, and technologies such as machine learning help harness huge amounts of customer data and find patterns in borrower behaviour to determine creditworthiness without human intervention. This not only helps in assessing risks but also in scaling the loan approval process and reducing delays in processing and credit decision-making time. This can be a boon for lending companies to decrease defaults and increase approvals with the help of custom ML models trained on their proprietary data, thereby driving profitability.