According to a 2015 Javelin Strategy study, about 15% of US consumers are impacted negatively by “false declines” when seeking small business loans or personal loans from lenders. These are borrowers turned away due to faulty data analyses and interpretations by humans. It ends up costing lenders over $118 billion a year due to lost opportunities.
Thanks to Machine Learning. Lenders can now use AI-powered tools to determine borrowers’ creditworthiness from multiple datasets and make accurate faster lending decisions. For instance, small business loans by Camino Financial are powered by Luna.ai, which uses advanced machine learning techniques to understand Latino business behavior.
What is machine learning?
Machine Learning (ML) is the use of Artificial Intelligence (AI) to teach computers to think and act like humans. Such computer systems can access data, learn from it, and gradually improve their accuracy and functionality from their past experience without further human programming.
How does machine learning work
Machine Learning uses data-defined patterns or a set of rules to explore data and identify patterns in it. Complex computer algorithms are then employed to learn and improve upon such experiences on their own. Since such systems can mimic past human actions to predict future behavior, they are used to automate tasks and streamline industry operations.
Also Read: 7 Ways Technology Is Being Used To Modernize The Construction Business
Benefits of machine learning in lending services
1-. Big data analysis
Machine learning helps lending companies solve big data problems. Today, lenders have a lot of data to analyze to arrive at accurate lending decisions. Machine Learning techniques help lenders identify, sort, and analyze borrowers’ data from multiple data sets seamlessly so that they can make accurate loan decisions quickly.
Apart from traditional data sources, these tech tools can monitor someone’s online/mobile activities and convert that information into a credit score that borrowers can use to make better lending decisions at a higher degree of accuracy. Visit the link in the intro to learn more about small business loans.
2-. Cater to thin-file borrowers
These are individuals with little or no credit history. Lenders are using machine learning to monitor and analyze their “alternative data” such as data from mobile apps, social media accounts, web activities, payment systems, bill payments, online/mobile transaction history, and more.
AI tools can comb through these vast amounts of fragmented data, convert it into a credit score, and use it in the underwriting process to devise and distribute financial products and services to this group of borrowers.
3-. Faster lending process
Unlike human underwriters who need days to weeks to analyze borrowers’ data, machine learning models are updated in real-time. They gather data from the borrowers’ credit reports, income reports, bank transactions, credit history, and alternative datasets. They then crunch those numbers and correctly build the borrower’s risk file in seconds.
They can return immediate credit assessment of multiple borrowers, which helps lenders make lending decisions within a few hours or days and serve more customers within a short call.
4-.Slash human bias
Both borrowers and lenders can succumb to human bias. Businesses and individuals can lie, while borrowers may be open to gender bias, systemic racism, or rooted stereotypes concerning particular groups of borrowers.
AI and machine learning help remove human bias in the lending process. These systems collect customer data and learn their credit behavior to determine their creditworthiness, loan terms, and eligible loan amounts without any human bias.
5-. Targeted marketing
Lenders are using Machine Learning and Natural Language Processing to understand their prospects better and target them with the right financial products. These tools rely on social media data, browsing histories, online activities, and data from apps and mobile devices to dig into client’s preferences, interests, and financial habits.
This enables lenders to segment their clients base and target each group with the right content and financial product, making for more personalized and customized client interactions. Thus, lenders can convert more leads, lower customer acquisition costs, and maximize their ROI.
6-. Excellent customer service
Machine Learning and Natural Language Processing have made it possible for lenders to offer excellent customer service/support 24/7. Chatbots and Robo advisors can understand customer queries and suggest solutions, provide financial advice and tips, and help users gain powerful insight into complex financial matters.
7-. Fraud detection
Lenders use AI-powered fraud detection and monitoring tools to monitor and detect suspicious activities on customers’ accounts. If they detect unusual behavior on credit card usage or bank account withdrawals, they send real-time alerts to users so that they can act immediately.
Also Read: Artificial Intelligence: How The Current Workforce Is Affected By Technology
How better lending services improve people’s life
The use of Machine Learning in the lending land has come with many life-changing opportunities for businesses and individuals alike. They include:
- Access to faster lending services for business and personal goals
- Easy applications and pre-approval process
- Excellent customer service and support
- Ability to obtain financing with poor or no “traditional” credit score
- More secure financial services via instant fraud alerts
- Financial advice and tips to make better financial decisions
- Save time by accessing loans online at any time without the need to visit banks
AI-powered small business loans
Fintech lenders lead the pack in the use of ML to offer small businesses loans. The top among them is Camino Financial. This fintech lender uses Luna.ai to offer small business loans to entrepreneurs.
The AI-powered platform helps the fintech lender understand Latino business behavior and insights and automate workflows. That leads to reduced credit default risk, reduced customer acquisition costs. Thus the lender can offer more competitive small business loans to minority business owners at favorable terms.
Also Read: Online Debt Consolidation: How Web Technology Has Changed the Face of Debt Consolidation
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