Traditional Credit Scoring Or AI-Based Credit Scoring: Which one is Better?

2 minutes, 33 seconds Read

In the financial industry, credit plays a very crucial role. Consumers, business organizations, and purchasers; they all are dependent on credit. The entire credit system works based on the credit score: availability of credit, loan approval, buying or renting an apartment, etc. Hence, having a higher credit score is highly beneficial. Financial organizations have been relying on traditional credit scoring methods for ages. But is it the best way to determine someone’s credit score? Artificial intelligence has improved credit scoring systems drastically. Several companies, like Genify, have developed AI-based credit-scoring applications for banks and financial enterprises. These applications offer API for credit score. Let us look at how AI-based credit-scoring systems are; better than traditional credit-scoring systems.

  • Underbanked individuals: For those individuals, who are underbanked, which means; they are deemed undeserving by traditional finance due to a low credit score, they have no choice other than turning to high-fee financial solutions. AI-based credit scoring models provide a more detailed analysis of the data. It could make the traditional credit score obsolete, providing the low or wrongly-scored individuals eligible for credit. It helps underserved individuals, gain access to traditional finance systems. In the long run, it helps in breaking the cycle of poverty.
  • Predictive process: Traditional credit scoring methods rely mainly upon payment history data and make assumptions of future creditworthiness. In fact, in today’s economic environment, assumptions; based on traditional systems are outdated and irrelevant. Traditional methods don’t consider factors such as employment history and financial behavior, which are crucial in predicting the risks; involved in lending future credits. AI-based credit scoring systems take into consideration the variables that would be overlooked by traditional credit scoring systems.
  • Quicker adaptation: Traditional credit scoring models become outdated in a short period due to rapid changes in the economic environment. Resources are; wasted in re-assessing the entire model and helping it to adapt to the changed situations. AI-based credit scoring systems are self-analyzing; it adjusts and improves according to the scenarios that are presented in the data. When alternative data is provided to the system, it can be trained to; create newer and advanced models.
  • Speed and accuracy: In finance, even the slightest mistakes can cause devastating results. According to reports, Asia-Pacific Bank lost nearly $4 million because of data-entry errors. But, artificial intelligence does not allow room for human errors. AI-based credit scoring systems analyze a wide array of data quickly. It identifies non-obvious or hidden patterns in the borrower’s financial behavior and provides lenders with accurate solutions. 

The traditional credit scoring system is a problematic situation across the globe. Many individuals don’t get recognition since they don’t have bank accounts or credit cards. AI-based credit scoring systems help the “credit invisible” people get recognized so that they don’t face hardships while applying for a loan or a credit card. Artificial intelligence in credit scoring systems is very useful in determining the potential risks involved and in turn saving the bank’s revenue. Thus, using API for credit score developed by organizations like Genify can help banks and financial organizations improve borrower-lender relationships. 

Similar Posts