The process of assessing credit decision-making is to evaluate the ability of a borrower to repay as well as creditworthiness before the approval of a loan. It is an important job for lenders because they decide whether they are able to provide credit to a borrower or not, and it also helps limit the risk they take.
But, companies often encounter difficulties when it comes to making decisions about credit due to its complexity, which requires evaluating multiple factors such as the credit history of the borrower as well as the amount of debt and income as well as other factors.
The advancements in analytics and modeling make the analysis of credit risks more precise and efficient. Companies can now make use of Artificial Intelligence (AI) and Machine Learning (ML) to automate the process of making credit decisions which allows them to analyze a variety of factors and patterns that might be difficult to recognize manually.
As AI becomes a standard in credit decision-making, will it replace credit analysts? This article examines this possibility.
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How does Automating Credit Decisions Helps Lenders?
Artificial Intelligence and Automation are transforming the lending industry in general. Automating credit analysis can help overcome many challenges and has numerous benefits, some of which are discussed in the following paragraphs.
1. Improved Accuracy
The AI powered credit models can help lower the risk for lenders through providing more accurate inputs than conventional models. They employ a wider number of data points for their analysis, and also include other data sources, providing an enhanced multi-dimensional view.
Increased accuracy in credit decision-making and filtration of clients at the initial stage aids lenders in reducing default risk.
2. Promotes Financial Inclusion
Machine learning models that make use of AI assist in making credit more accessible to those who are not able to access the traditional channels of banking. Utilizing alternative sources of data helps lenders determine whether applicants do not have a traditional credit history, but are suitable for loans. The use of AI models allows lenders to increase their customer base as increasing financial inclusion.
3. Customer Onboarding Is Faster
Automating credit decisions can accelerate the process of onboarding customers by using a an efficient credit assessment process. Automating eliminates the requirement to enter and process information manually, and also speeds up extraction of the needed data from applications forms, financial statements or other documents.
This bank statements analyser of Precisa assists in extracting information from bank statements, classifies and analyzes the data and assists in reducing the processing time by five times.
Automated processes can also identify the missing data and then follow up with the applicants to get the required information.
4. Boosts Revenue
Automated decision-making for credit can boost revenue for the lender through cutting down costs of purchase, boosting acceptance rates and improving customer satisfaction.
A study conducted by McKinsey discovered that credit appraisal using AI can help lenders cut the costs of loan origination by as much as 40 percent..
Automating credit decisions can reduce the processing time, allowing lenders to process more applications without impacting the quality of their portfolio.
Will AI Replace Credit Officers?
The previous discussion demonstrates the ways in which AI provides many benefits and enhances the risk assessment of credit for applicants.
In this situation the most frequently asked question across all areas where AI is used is: can it replace human beings? Does it mean that all credit-related decisions are made automatically, without the need of credit professionals?
Let’s explore the reasons despite the many benefits artificial intelligence can provide the credit officers is still crucial to the process of credit risk analysis.
1. AI Cannot Replace Human Judgement
AI models can provide accurate predictions based on the available information. But, they are unable to predict the future with greater depth or adapt to changes in the environment.
AI excels at quantitative analysis, but comprehending the non-quantitative components required for a deep research might be outside its realm. However, when credit officers meet with prospective clients, they are able to perform a range of judgments and observations from their interactions. This will help them gain an overall assessment of the character of the prospective client as well as other aspects that go that go beyond numbers.
2. AI Models Can Be Biased
AI models are only as good as the data they use to build them. If the data used is biased then the model too will be biased.
Recently, Forbes also addressed this concern, and the possibility of AI creating biases which can lead to discrimination or unjust behaviour that could lead the credit model to be biased and discriminate against certain sections of the borrowers.
Combining human-like intelligence and experience together with AI is vital to reducing the issue. In contrast to AI being the sole source of decision-making, it’s useful tool that can aid the process of making decisions. Credit officers can reduce the impact of these biases by reviewing credit decisions based on artificial intelligence.
3. Regulatory Requirements
The financial sector must follow a number of regulations that are set out by authorities such as those of the Reserve Bank of India and the Securities and Exchange Board of India.
Credit bureaus also must adhere to a variety of guidelines set by these authorities. They requires human oversight and intervention that AI can’t yet do.
4. AI Models Lack Transparency
AI credit risk modeling require greater transparency, which makes it easier to comprehend the process of decision-making. The applicant whose loan application is denied may wish to understand the reason for the rejection. The reason for the rejection may not be clear in the event that it is a process that automatizes credit appraisal and the borrower may not be able to find a convincing explanation.
If credit officers deny the loan application they are able to discuss the reason with the applicant. The borrower has the choice to rectify the issue and receive the loan approved after the problem is resolved.
The lack of transparency makes it difficult to determine the weaknesses in the model and eliminate the biases.
The Takeaway
Credit professionals might have a difficult time trying to cope with the plethora of data available to make decisions and may prove long-winded too.
These AI Credit Assessment models aid credit professionals and enhance the precision and speed of decision-making. However, they are not able to substitute human intelligence which is real intelligence.
The combination of human-intelligence and artificial intelligence could help lenders lower their risks and come to better decisions about credit as well as making the process more efficient and more user-friendly.
Precis’s bank statement analyzer is the ideal tool to harness the power and advantages of AI and provides credit managers with invaluable insight into the analysis of credit risks.