For decades, businesses used to rely on traditional lenders like banks for funds. However the process of securing a loan approval itself used to be an uphill battle for them. This is because of rigid and strict credit assessment systems that typically favor larger organizations. Besides that, industry-specific limitations and external economic factors complicated the approval processes. The application process used to be long and cumbersome too.

Such reasons made it harder for small businesses to get business loans easily but with the rise of advanced AI-powered credit algorithms, they have a fighting chance. Credit scoring algorithms of today use AI and data science to assess risk faster and accurately, approve loans and unlock more opportunities for entrepreneurs. While the benefits are many, are these algorithms actually the best thing for businesses or a new unforeseen obstacle? Let’s discuss.

What is Algorithmic Credit Scoring?

Algorithmic credit scoring is essentially the process of assigning a single quantitative scoring or measure to potential borrowers that show their creditworthiness and default risk. Such systems have been in practice since the 1950s usually in consumer lending like auto loans, mortgages and credit cards. Only recently it has been growing in popularity in business lending.

The Use of Credit Scoring Algorithms

Credit scoring algorithms have been created to use machine learning, artificial intelligence and statistics to analyze data sets fast. Using this technology can streamline lending decisions and make projections more accurate.

Lenders can assess loan applications with the help of these algorithms that can predict credit risk. However, faulty credit scoring algorithms can make bad choices by computing wrong data or disregarding some data. A well-designed algorithm can properly study multiple facts and predict debtor behavior more accurately.

In the context of loans for small businesses algorithmic credit scoring is slowly growing prominent due to the quick processing power it possesses. It can skip complicated bureaucratic steps and avoid human error. Plus for lenders it becomes easier to analyze large numbers of loan applications quickly and efficiently with these systems. They can compare information about their borrower’s past loan experiences and financial history. These systems can judge the credit risk of borrowers better making them an attractive mechanism for lenders to avoid possible defaulters.

Reasons It’s Good for SMB Lending

Here are some reasons why algorithmic credit scoring is suitable for checking creditworthiness with small businesses.

• They are more cost-effective. With algorithmic scoring systems there is no need to handle manual reviews or a lot of paperwork. That naturally reduces operational costs for the lenders making it a more cost-effective solution.
• Assures faster and more efficient performance. The AI-powered algorithms streamline the complex application and verification process which cuts down the loan approval timeline significantly.
• More access to credit. Traditional lenders like banks have stricter qualification criteria for business loans. This cut out a wide range of people from applying which is not an issue with credit scoring algorithms that analyze alternative data. So with these systems marginalized groups like people with non-traditional professions, low-income earners and immigrants can have the opportunity to apply for loans.
• Better risk assessment. The algorithmic credit scoring systems can provide a better and more nuanced analysis of the credit capacity of the applicants. They can process a lot of information quickly including various online transactions over a long period. These systems can somewhat accurately assess the borrower behavior based on past credit history using predictive analysis.
• Less human bias. In the conventional credit scoring systems there was the risk of biases occurring based on factors like gender, race, etc. The algorithms can be directly configured to make decisions based on strict financial indicators instead of any personal characteristics.
• Better risk categorization. AI can help group the borrowers into more detailed risk categories. So lenders can tailor their loan terms and interest rates with better precision.

Areas Where It Falls Short

Here are the likely drawbacks of algorithmic credit scoring systems.

• Concerns about data quality. Unlike humans, these systems cannot differentiate between what is correct data and which is incomplete or inaccurate. So it is possible that these credit scoring systems might make wrong assessments.
• Discrimination possibility. These algorithms use the past credit behavior of borrowers to determine whether they qualify for a loan or not. But if the historical data itself contains biases, the algorithm may adopt the same biases and reinforce them resulting in unfair or discriminatory lending decisions.
• Limited transparency. Proprietary credit scoring algorithms are typically confidential. So it can be difficult to understand what methodologies or criteria were taken into consideration when making credit decisions.
• Too much dependency on technology. In the traditional credit scoring process, those in charge of making the decisions could study and consider many qualitative factors. AI-powered systems cannot mimic this human judgement and that may impact the quality of the evaluations.

How Can Small Businesses Improve Their Loan Access Chances?

Smaller companies can take the following steps to improve their chances of securing business loans.

Prepare a solid business credit profile

Prioritize building and maintaining a strong credit profile for your company.

• Register your business with major business credit bureaus like Experian Business, Equifax Business and Dun & Bradstreet.
• Make sure to pay all your vendors, service providers and lenders on time. A consistent payment history contributes a lot of points to business credit scores.
• Maintain good financial habits like avoiding too many credit inquiries and keeping your debts manageable.
• Review your credit report consistently to prevent errors—raise a dispute with the respective bureau if any appear.  

Leverage alternative data

If the regular credit history is insufficient, you can show other evidence to prove that your company has consistent cash flow and is financially responsible. Show statements of utility bills, rent payments, etc.

Keep financial records up-to-date

Lenders prefer financial transparency when deciding whether to grant loans. So make sure to keep all the tax filing, bookkeeping and profit-and-loss statements up-to-date.

Bad financial management and missing information can make it very hard to secure business loans. In extreme cases, such habits can lead to high financial distress or even Bankruptcy Chapter 7. So try to maintain accurate records.

Manage cash flow better

Lenders check if a business can repay their loan to make their decision. This is why it is important to focus on managing the business cash flow efficiently to improve your approval chances. Do a proper audit and cut down on unnecessary expenses.

Consider other financing options

Loans are not necessarily the only available option for funding. You can also consider other revenue sources like fintech platforms, non-traditional lenders or crowdfunding. However these options come with risks of their own so do proper research first before deciding. Take the help of a financial advisor to know what option best suits your particular needs and situation.

Conclusion

AI-powered credit scoring systems have indeed transformed the lending system. It promises better efficiency and quicker decision-making when assessing borrower risk at almost half the cost. All evidence suggests that this technology is likely here to stay.

That being said, the credit scoring algorithms are not without their own flaws as well. It can make mistakes when handling a vast number of financial records of businesses because of programmed biases, data inaccuracies and algorithmic errors. Lenders should therefore take proper caution instead of relying blindly on such models to make critical financial decisions. They should continue refining their algorithms and maintain transparency. Borrowers should also make sure that the data they provide is up-to-date and accurate.

Indeed, AI is a powerhouse technology but much like humans, it has some shortcomings. Balancing both can help you make more informed decisions.