For many years, the lending industry relied on an established formula of financial statements, credit histories, and experienced loan officers. That formula is being challenged, however, by artificial intelligence (AI). Platforms that leverage AI in lending decisions are changing how financial institutions assess risk, rank borrowers, and make lending decisions.
Rather than relying on paper and number crunching, AI models can evaluate thousands of financial data points in seconds, enabling faster, data-driven insight that offers efficiency and consistency in financial decisions.
The question from many financial leaders is whether or not AI delivers a better outcome than the traditional human model of lending.
In this article, we’ll consider the differences between the use of AI in lending decisions and traditional methods of lending and examine each one’s strengths and weaknesses.
The Human-Centric Approach of Traditional Lending Decisions
The traditional way of underwriting small business loans is a people-driven, labor-intensive experience.
Through traditional financing, loan officers and their underwriting teams evaluate a borrower’s ability to repay a loan using factors such as credit scores, financial statements, business plans, and personal interactions.
This manual credit assessment process depends heavily on professional judgment and industry experience.
Advantages and Disadvantages of Traditional Lending
The biggest advantages of the traditional approach are found in connecting with borrowers. Experienced lenders can look beyond mere numbers. They can take into account factors such as potential for business growth, ability to effectively manage a business, or long-standing and loyal customer relationships.
This personalized approach leads to longer-term loyalty, as small business owners value personalized attention. That said, the traditional model invariably has drawbacks.
- Judgments can be impacted by old or unconscious biases about a borrower’s ability to repay, contributing to inconsistency across the lending process and limiting credit access for underserved communities.
- Manual and human review takes more time and often results in delays, increasing the operating costs for a lender.
- Not meeting timelines is also frustrating for today's borrowers, who by now have often had digital experiences that move much faster. They may go elsewhere if your financial institution doesn’t provide those experiences.
Nearly 78% of businesses are already using AI in their own business operations, according to McKinsey. In a world where banking customers are increasingly expecting a fast digital experience, the traditional loan process is becoming quickly outdated.
Yet relationships are a significant part of the banking culture. Building rapport with customers by understanding their needs and developing trust is still essential as lenders consider embracing technology and modernizing their lending processes.
AI in Loan Underwriting: How It Works
While the traditional approach values personal connection and experience, modern lenders are discovering how technology can complement those strengths. Lenders utilizing AI in lending decisions no longer have to rely solely on conventional risk indicators, such as credit scores and financial statements.
AI systems use machine learning to analyze a broader set of financial data; trends in cash flow, transaction history, online behavior, and even seasonal patterns of business activity. This practice is extremely beneficial for borrowers who don't have an extensive business credit history, helping promote fair lending and expand credit access.
Predictive Modeling and Tailored Risk Insights
AI-enhanced models detect patterns that people might otherwise overlook. By tracking borrowing trends, like spending, repayment behavior, social media interactions, and business development, AI can assign the level of credit risk and estimate the likelihood of a borrower defaulting versus paying on time.
Predictive modeling can also determine an interest rate based on a risk assessment of the borrower. Together, these metrics enable lenders to propose loan offers within their own risk-return parameters, helping them to maximize their pricing and risk management while mitigating potential defaults.
Automation to Enhance Speed and Confidence
Through automation and using AI in lending decisions, lenders can evaluate extensive volumes of applications in minutes rather than days. This technology reduces workflows and lessens unpredictability while improving fairness, equity, and consistency of lending practices.
Financial institutions can also process more loans with fewer people and reduced operational risk and costs. Lenders also gain more confidence in decision-making.
Improving Fraud Detection and Customer Experience
Leveraging leading technologies, including generative AI (Gen AI) tools and recognized anomaly-detection algorithms, can identify suspicious activity across applications, documentation, and transaction ledger records.
AI systems can quickly flag concerning activity before it impacts compliance support or customer damage, helping protect the bank and reduce costs for both lender and borrower. In addition, utilizing intelligent chatbots and AI-driven communication can enhance customer experience.
Balancing Innovation with Responsibility
While the benefits of AI in lending decisions are significant, successful adoption requires strong data governance and secure infrastructure. Financial institutions must partner with reliable technology providers when using AI in lending decisions. It’s also essential to continuously monitor their systems to ensure accuracy and fairness.
When properly implemented, AI-driven lending combines efficiency with responsible lending, enabling lenders to make smarter, faster, and fairer credit decisions in a digital-first economy.
Comparative Analysis: AI in Lending Decisions Vs Traditional Lending
Comparative Analysis: AI vs. Traditional Lending
| Traditional Lending | AI-Driven Lending | |
| Decision Speed | Can sometimes be weeks to months; manual verification | Minutes; automated analysis |
| Data Sources | Credit history, financial statements | Financial + behavioral + alternative data |
| Bias & Fairness | Human bias always a possibility | Can reduce bias, but dependent on data quality |
| Scalability | Limited by human capacity | Highly scalable |
| Customer Experience | Relationship-driven, valued by customers | Personalized and instant, enhancing the customer experience |
| Risk Assessment | Based on rules and judgment | Based on patterns and probabilities |
Case Study: Owners Bank Partners With Biz2X For a Digital-First Small Business Lending Experience
Owners Bank, a digital arm of Connecticut's third-largest bank, Liberty Bank, was launched to bring a modern, no-nonsense approach to small business lending.
Owners Bank needed a multiproduct lending platform that could handle everything from application intake to underwriting and loan management. The bank also has ambitions to expand its footprint beyond Connecticut into states like Florida.
The bank sought a scalable digital solution that could deliver speed and increase volume and efficiency for both customers and staff.
Owners Bank partnered with Biz2X, implementing its Pro Journey platform to power its digital lending operations.
The platform supports AI in lending decisions for term loans, lines of credit, and business credit cards, integrating AI tools that assist with ID verification, credit bureau checks, and tax statement analysis.
Since going live in July 2023, the results have been impressive with over 300 applications processed, an average loan size of $75,000, and a turnaround time of just three days.
Best Practices When Implementing AI in Lending Decisions
For lenders considering AI in lending decisions, the greatest results will come from a thoughtful and phased approach. The following best practices can help your financial institution launch AI more effectively, in a manner that builds trust for all stakeholders.
1. Begin Small.
Choose a single loan product segment, like a small business line of credit or equipment financing, and pilot in that segment only. This allows your internal teams to test, assess, refine, and revise your AI solution prior to a broader scale-out.
2. Choose Partnerships Wisely.
Using AI in lending decisions is only as strong as the data and the platforms behind it. Clean, consistent, and bias-free data minimizes risk and increases model accuracy. An agile platform with a proven record of continuous uptime is also important. Choose only reputable platforms and third-party partners to ensure real-time, quality data and platform performance.
3. Be Transparent.
Regulators and borrowers expect explainability, particularly when using AI in lending decisions. Use tools and documented processes that explain the rationale of why and how AI has made its loan decisions. Transparency will enhance trust and assist with regulatory compliance.
4. Educate Relevant Team Members.
Ultimately, blending technology with your lending team's experience and business acumen offers the best of both worlds. Your loan review team will benefit from education in data literacy, ethics in AI, and interpreting the data that comes out of your AI system. Make sure your team is well-informed so it can have more confidence when using AI in lending decisions.
5. Monitor and Revise As Needed.
AI systems will behave differently in varying market conditions. Your financial institutions' lending processes should likewise evolve, as many factors can impact decision-making. Documented regular ongoing reports, testing, and audits will ensure the most stability for your loan books and greater accountability to ensure better inclusion for borrowers.
Conclusion
AI isn’t here to replace lenders. It's a strategic partner that allows lenders to make faster, fairer, and more informed decisions. Lenders will ultimately move toward a hybrid model where AI works alongside human judgment and expertise.
AI in lending decisions is achieved by efficiently analyzing large data sets, drawing observations about patterns and risks, and assigning creditworthiness rapidly and consistently.
Meanwhile, there will always be a need for humans to offer real value through customer relationships. Bank staff are also instrumental in intervening in complex situations or managing high-dollar value loans.
Banks that find the proper balance of using AI in lending decisions and understanding of a customer's individual situation will enjoy the most success in strengthening their loan portfolios and building loyal customers through better data-driven financial decisions.
FAQs About AI in Lending Decisions
1. What are some use cases for AI in loan underwriting?
AI can quickly review extensive borrower data to assess the risk of extending credit, prevent fraud, and enhance personalized loan offers. It can streamline loan application and underwriting, while offering predictive insights for improved decision-making and operational efficiencies. The newest uses of AI in lending are chatbots and agentic AI to improve customer engagement and experience.
2. Are small business owners comfortable with the use of AI in lending decisions?
Yes. Most are already using AI, according to a recent U.S. Chamber of Commerce report. In addition, many have grown accustomed to embedded lending solutions that make it easier for them to obtain financing in the applications and platforms they’re already using.
3. What are the first steps for banks exploring AI-powered lending solutions?
Defining a clear objective, such as improving operational efficiency or boosting revenue for your financial institution, should be the first step. Also, outline which use cases AI can help you achieve your objectives. For example, streamlining and automating loan applications can boost efficiency and help you scale. Finally, partner with a trusted Fintech provider to discuss strategies and implementation.
4. What are the biggest risks of using AI in lending decisions?
The biggest risks are data falling into the hands of spammers and schemers. Other potential risks are regulatory challenges, algorithmic bias, and lack of transparency. These risks can be minimized by partnering with reputable Fintech providers and third-party vendors who prioritize data protection and compliance.
5. How long does it take to implement AI-powered loan solutions?
Some platforms can take several months. But Biz2X can have a loan origination system up and running in 30 to 45 days, depending on the scope of solutions. For example, the platform’s Rapid Launch Accelerate SBA software can be implemented within 30 days. Biz2X’s platform is also configurable to meet the specific lending services offered by your institution.
