Credit scoring and analysis are crucial components of the lending process. In 2020, the global credit scoring market size was valued at $2.65 billion and is expected to reach $8.57 billion by 2027, growing at a CAGR of 16.2% from 2020 to 2027.
It is a more comprehensive process which includes evaluating the borrower’s financial statements, credit history, and other relevant information to determine the borrower’s ability to repay the loan and make informed decisions about whether or not to extend credit. With so many high-value credit applications to evaluate and qualify, credit professionals need to make sure they make quick and accurate decisions.
In recent years, automation has become increasingly important in credit scoring and analysis. One such platform that has gained popularity in this space is Bautomate, an AI powered platform that enables banks, lenders and other financial institutions to automate credit scoring processes.
Traditional Credit Scoring Process
Traditional credit scoring and analysis processes are time-consuming and typically involve manual data entry, analysis, and decision-making, which can be prone to errors and inconsistencies. As per a study conducted by a 2013 Federal Trade Commission (FTC), 26% of the individuals studied had errors in their credit reports, and 13% had score errors.
In addition, traditional credit scoring and analysis processes are often subjective. Different lenders may use different criteria and methods to evaluate creditworthiness, which can lead to uncertainties in lending decisions.
Automation in Credit Scoring System
Studies have suggested that automation can lead to a significant improvement in credit scoring, with one study by the Federal Reserve Bank of Philadelphia reporting that automated credit scoring models outperform traditional credit scoring methods by as much as 15%. Automation can help overcome many of the challenges of traditional credit scoring and analysis processes. Credit scoring automation has been shown to reduce bias in lending decisions, as it removes subjective human judgment and replaces it with objective data-driven analysis.
Moreover, automation can help lenders to take into account a wider range of factors when evaluating creditworthiness. For example, automated credit scoring models can incorporate data on a borrower’s job stability or income potential, which may be critical factors in determining the borrower’s ability to repay a loan. According to a study by the Federal Reserve Bank of New York, the use of credit scoring automation has led to an increase in access to credit for underserved populations, such as minorities and those with low credit scores.
Credit scoring automation can also be used for fraud detection and prevention, as it can identify unusual patterns or behaviors that may indicate fraudulent activity.
How Bautomate Help in Streamlining the Credit Scoring Process?
- Lowers acquisition costs as it enables automated screening of applications which further expedites decision making. It also screens large volumes of credit information for applications which can be approved with least credit analysis.
- Lenders can automate repetitive and time-consuming tasks such as customer onboarding, data entry, document processing, and scorecard calculation. This can free up valuable resources and enable lenders to make faster decisions, potentially resulting in increased profits.
- It uses a combination of artificial intelligence and machine learning technologies to analyze an applicant’s financial data, such as their income, debt-to-income ratio, and other financial metrics. By taking into account all the relevant factors, Bautomate can accurately predict the likelihood of the applicant repaying the loan.
- Lenders can automatically digitize paper documents by leveraging Optical Character Recognition to automatically extract financial data from customer documents in various formats to enable quick verification.
- With the platform’s built-in analytics feature, lenders can gain a deeper understanding of their borrowers’ creditworthiness, risk assessment and make more informed lending decisions. It has the ability to generate reports and insights quickly and easily.
Key Takeaway
Automated credit scoring is a powerful solution that can help businesses make better decisions when it comes to assessing the risk of potential customers. It can be used to quickly and accurately assess creditworthiness and identify more profitable customers. Businesses are able to access more accurate data in a fraction of the time it would take them to manually assess applications. Helps in making quicker decisions about applications, reducing the chance of fraud and providing better customer service.
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