Understanding the Role of Artificial Intelligence in Lending

Prevalence of AI in Financial Institutions

Every day, small business owners and consumer borrowers apply for loans without knowing if credit decisions about their loans are being made by humans or machine learning models and artificial intelligence. In all likelihood, it’s automation methods or algorithms that are analyzing credit risk, finessing underwriting, calculating interest rates, and more. Financial services companies such as credit unions, traditional banks, and online fintech lenders have long understood the numerous advantages of using AI models to access and evaluate big data to increase their fair lending practices.

A recent survey found that both bank and non-bank lenders currently use machine learning underwriting models and that other institutions in the financial industry are close to adopting these models.

Specifically, the report found:

• Lenders primarily want to tap the potential of using artificial intelligence and machine learning models to improve credit scoring of potential borrowers, so they can minimize credit risk and optimize the entire lending process.
• Some financial service providers are already ahead of the curve. Credit card providers and unsecured personal loan lenders are typically more advanced and have already adopted the use of artificial intelligence in their decision-making processes.
• Loan providers concerned about the capability to properly use machine learning models can impose upfront controls on their AI models or produce supplemental diagrams to ensure transparency in black box models. (Black box models are software programs “designed for use in financial markets that analyze market data and produce a strategy for buying and selling based upon that analysis.” These programs are typically too complex for the human brain to comprehend, so the user of the black box model can understand the results of the analysis but cannot see the logic behind them.)
• Regulators also focus on how and where machine learning can improve the administration of fair lending practices. Financial services stakeholders are especially interested in how artificial intelligence affects the performance and fairness of credit decisions.

So although there is already a great deal of progress in implementing artificial intelligence as part of the loan decisioning process, there is clearly still a way to go in the proper use and control of these advanced techniques when it comes to business lending. Let’s look at how some of the most advanced are doing it.

Where Artificial Intelligence and Machine Learning Can Help in Lending

1. Onboarding for Borrowers

Lenders typically face three main onboarding challenges: 1) Capturing data in real-time, 2) Offering a good user experience, and 3) Complying with KYC (Know Your Customer) and AML (Anti-Money Laundering) guidelines. Since most of the documentation requirements occur during the onboarding stage of the lending process, enabling automation during that stage can help drastically reduce the time it takes to perform the verifications.

Lenders and borrowers both benefit from machine learning models. Borrowers grant secure temporary access to lenders for income verification and other data, eliminating the risk of sensitive paperwork falling into the wrong hands. Likewise, the confirmation of that information is automated, cutting hours off the time it typically took for the lender’s staff to do follow-up manually. Because the data is captured in real-time, lenders can more quickly make credit decisions about borrowers with little or no credit history. This helps to both broaden the pool of accessible borrowers and maintain credit quality standards at the same time.

2. Credit Decision Inclusivity

Maybe the most altruistic reason for lending business leaders to employ artificial intelligence and machine learning models is to increase credit access for millions of people, especially those who historically have experienced difficulty getting approved for credit, such as minorities and low-income consumers.

Historically, we know that minority groups often dealt with discrimination when applying for loans, resulting in higher interest rates and higher rejection rates. Artificial intelligence offers financial institutions solutions so they can practice inclusivity without escalating financial risk. By removing human bias in lending practices and instead basing credit decisions on objective data points, especially alternative data sources not traditionally considered in lending decisions, lenders can expand their loan approvals to a broader market.

Likewise, because the amount of time required to review applications, organize payments, and assess risk is significantly lessened, financial institutions can focus more of their time on personal interactions with potential borrowers and evaluating any risk not reflected in the data.

3. Real-Time Banking API

API integrations, or an “application programming interface,” allows separate systems to “talk” to each other without human communication. Banking APIs are specific to banking software systems and allow different software applications and financial institutions to share information securely for financial decision making, reducing the need for back and forth manual communication. Artificial intelligence and machine learning models enable bank systems to talk to one another whenever information is needed, which helps eliminate roadblocks and speed up credit decisions.

4. Fraud Detection in the Lending Process

Using AI models gives financial institutions a better chance of fighting fraud than using manual fraud detection did. As data sets get larger, AI models are better equipped to handle the immense amount of data provided and offer better insights into customer behaviors and fraud trends. In addition, artificial intelligence and machine learning models are better able to actually prevent fraudulent practices by recognizing the fraud earlier in the process so the lender can respond before any damage is done. AI algorithms can recognize and automatically reject a transaction if the data set warns of fraudulent behavior, even if such a case would have been missed by manual underwriting.

5. Post Loan Monitoring and Risk Management

Once loans are approved, lenders still have a lot of work ahead of them. During the life of the loan, which may last several years, the borrower’s financial stability is likely to change. Whether it’s the state of the economy in general or decisions made by the borrower, lenders must take on the risk of default before the loan is due.

AI models help by regularly monitoring the borrower’s account to ensure the lender’s investment is protected. Machine learning models can quickly detect red flags and warn the lender that the borrower’s financial status is at risk. In addition, financial regulators will request more data throughout the life of the loan to ensure the lender is meeting regulatory oversight, quantifying risk, and setting aside appropriate reserves.

AI in Business Lending: How Biz2X Works

Many financial institutions struggle with the process of evaluating creditworthiness for small business owners. Unlike with consumer lending, where there is already one simple credit benchmark, business lending is more complex. It’s often time-consuming and costly.

Biz2X developed an artificial intelligence module that allows banks to set up a credit evaluation algorithm to intake various kinds of raw financial transactions and then use those signals to issue credit decisions about business loans (or make recommendations to the underwriters who make the final decision).

Biz2X Risk Analytics incorporates these advanced digital techniques and bundles machine learning with advanced data analytics to produce lending results that business lenders can use in conjunction with their implementation of the Biz2X Platform. Other features of the module include:

• Review and monitoring of new-to-bank merchants to identify lending opportunities
• Borrower-level credit quality insights and historical trends
• Personalized feedback and suggestions for every user
• An easy-to-use dashboard for bankers
• Up-to-date, scalable, straight-through processing

Using the Biz2X Bank Statement Analyzer, a key component of the Biz2X Risk Analytics solution, eliminates time-wasting tasks such as costly manual underwriting. The Bank Statement Analyzer combines natural language processing and image recognition to read and automatically analyze information about businesses’ financial performance. This can then be layered into a lender’s credit policies to accelerate decision-making times with complete risk control. Lenders can equip credit risk teams with automation tools that speed up analysis and make straight-through loan processing possible.

Biz2X’s Bank Statement Analyzer uses machine learning models and artificial intelligence to examine the borrower’s operating cash flow statements, debt coverage ratio, and loan limits. In addition, it provides early warning alerts for post loan monitoring.

Financial institutions using Biz2X’s AI solutions can also reduce their risk of credit fraud and verify information in less than half the time it typically takes. With ongoing data collection, AI-based image classification, and Open APIs, Biz2X can identify and prevent fraud before it’s too late to stop it.

Learn More About Business Lending & AI

As techniques for improving business finance decisions continue to get more and more powerful, it is imperative that bankers find the right method of implementing these new technologies into their own process. Biz2X lending specialists can provide your institution with a tailored analysis of your lending process for business loans, offering recommendations for how to experiment properly with artificial intelligence, machine learning and other advanced solutions. Request a demo to speak to a specialist about your institution’s plans.