How AI Technology Can Streamline Business Loan Application Process 

From ChatGPT to AI software integrations, artificial intelligence is taking over almost every industry. With approximately 15% of the venture funding raised for AI vendors in the lending industry, the finance industry is no different. Through machine learning technology, many tech companies are partnering with alternative lenders to simplify the business loan application process. Alternative lenders already typically offer a streamlined application process compared to traditional banks, yet AI technology can make the process even simpler for small business owners. Let’s dive into the traditional challenges of the loan application process, the benefits of AI in lending, and automation strategies for business loan processes.

AI, or artificial intelligence, is machine-learned technology that simulates human intelligence. It generally requires specialized software for writing and training machine-learning algorithms.

Benefits of AI in Business Loan Application Processes

Loan application processes are typically cumbersome, especially with a traditional bank. It can be time-consuming for both the applicant and the application processor to sort through the requirements and necessary paperwork. This is where AI comes in – by streamlining the process for faster results. AI can help improve the business loan application process by:

• Reducing processing time
• Enhancing risk assessment and credit check results
• Providing an enhanced customer experience

Traditionally, the banking industry as a whole is seen as technologically averse. The business loan application process in particular is bogged down with tons of paperwork and requirements. Once all that paperwork is submitted by the applicant, the lender then manually reviews the loan application. They first check for completeness and accuracy, check the qualification criteria is met, and verify the loan documents. All information is cross-checked against reliable sources to ensure accuracy.

Then, the lender establishes the credit score of the applicant by verifying with credit bureaus and sometimes even manually calling credit agencies.

Challenges to the Traditional Loan Application Process

Traditional lenders, like banks and the Small Business Administration, spend a lot of time processing loan applications. Applicants often wrestle a mountain of paperwork, leading to delays in gathering and submitting documents. Traditional lenders manually enter and cross-reference information, making the process achingly slow and vulnerable to human errors.

Inexperienced borrowers, especially startups without a credit history, face additional challenges within the traditional loan application process. Their lack of financial records can hurt their chances of approval, as lenders often heavily rely on credit history to assess risk. Manual processing can lead to biased decision-making. This bias can disproportionately affect those who may not have the expertise to produce polished paperwork.

AI streamlines the internal lending processes, determining more accurate creditworthiness by analyzing large volumes of data from credit bureaus, bill payments, bank statements, and even social media. AI can analyze large amounts of data to identify credit patterns and trends accurately and at a much faster rate than most humans can.

The advantages are substantial:

• faster loan approvals than ever before
• reduced due diligence costs
• lower credit losses
• decreased risks for lenders
• a robust defense against fraud

Gone are the days of slogging through piles of paperwork and enduring lengthy wait times. From gathering and verifying documents to assessing creditworthiness and risk, AI driven automation is redefining and simplifying the journey for both lenders and borrowers.

1. Collecting Documents and Data for Borrowers

In traditional business loan applications, document collection typically involves the applicant manually submitting stacks of physical and digital documents. This loan application process is often very time consuming and prone to errors due to manual verification. However, the game completely changes with the integration of Optical Character Recognition (OCR) models.

Custom OCR models eliminate the need for any manual intervention, extracting both machine and handwritten text from loan documents, ensuring accuracy and efficiency. These OCR models can extract information from a variety of documents including income tax return forms, bank statements, and accounts receivable and payable statements. this extracted key information is formatted and cleaned, making it easy to correlate these key values with other loan processing information. AI helps in collating all important data from various loan documents and helps them organize in a format suitable for fast processing.

2. Verifying Financial Documents and Bank Statements Automatically

AI uses intelligent algorithms to decipher and verify the information, making sure it aligns with the data provided by the applicants. This not only speeds up the verification process but also minimizes the chances of human error. AI can not only verify the authenticity of the original document from counterfeits, but it can also verify if the key information extracted from various income/tax documents falls under the range of loan qualification criteria. For example: AI can read the income statement and reject an application if the annual gross sales are less than $250,000 if that is a requirement for the loan.

Compared to manual processing, lenders often invest a significant amount of time processing various documents before they even reach critical information that can lead to can to an automatic rejection. Manual processing can be time-consuming and might involve a series of repetitive tasks, potentially causing delays in the decision-making process.

AI accelerates this journey by automating the verification of financial documents and bank statements. It quickly identifies any red flags or inconsistencies, allowing lenders to notify applicants promptly.

3. Risk Assessment and Credit Check using AI Models

AI plays a pivotal role in risk assessment and credit checks within the small business loan application process, offering multiple approaches to enhance accuracy and efficiency including:

Machine Learning Algorithms: These algorithms analyze historical data like an applicant’s credit history and repayment behavior to predict future creditworthiness.

Optical Character Recognition (OCR): AI-powered OCR models can scan text in loan applications to extract key information. This helps in making the process faster.

Rule-Based Systems: Lenders establish rules and criteria for loan approval and the system automatically first checks whether the applicant meets those criteria. It’s a structured approach to quickly filter out unqualified applicants.

While AI can analyze vast amounts of data and automate various aspects of risk assessment and credit checks, human oversight is vital to ensure unbiased lending decisions and maintain ethical lending practices.

To create a more comprehensive and creditworthiness check, AI can utilize various factors and alternative data, including:

• Traditional credit data: This includes credit scores, payment history, and credit utilization.
• Business data: AI considers a company’s financial statements, cash flow, and industry-specific data.
• Banking and transaction: Analysis of bank statements and transaction history to assess an applicant’s financial habit.
• External economic factors: AI can incorporate macroeconomics factors such as inflation rates, industry trends, and geopolitical stability.

4. Generating Quicker and Accurate Lending Decisions with AI Technology

AI enhances the lending experience for borrowers by enabling fast processing, accurate risk management, and minimal human intervention, resulting in quicker decisions. The automated collection of documents and data simplifies the application process, while automatic verification of financial documents and bank statements ensures accuracy and minimizes delays. AI-driven risk assessment and credit checks provide borrowers with faster approvals and more reliable lending decisions, ultimately streamlining the overall experience.

Efficient data extraction using OCR models minimizes errors by the process of converting printed or handwritten text into digital data which expedites the data collection process. Data verification can be done in split seconds with the help of machine learning AI models speeding up the loan qualification process. AI models can analyze data and assess risks faster, leading to more efficient and expedited lending decisions.

AI isn’t just a buzzword; it’s a tool that makes lending and loan management more efficient, cost-effective, and secure. The benefits of AI in the loan application process include faster decision-making, reduced human bias, and the ability to analyze a huge amount of data from various sources at once. Blending AI’s data processing capabilities with human judgment and using a wide range of data sources results in a more efficient, transparent, and applicant-friendly approach, which benefits both lenders and borrowers.