A strong credit risk management system in combination with AI and ML technologies can not only mitigate financial risks but also level up the effectiveness of decision-making processes, increasing a company’s profit.
Statista, the number of only FDIC-insured commercial banks in the U.S. over the last 20 years reduced by half. It suggests that there is strong competition in the market. Banks and lending organizations need to check every loan application in detail to
gain the greatest extent of possible profitable borrowers and minimize credit risks. However, not all organizations have access to top-notch AI software.
Credit risk refers to all possible risks that banks take lending out money. Analyzing the economic ecosystem for a few months, a credit risk manager might predict economic shifts resulting in unpaid credit. Besides, a payment history review of every applicant
allows reducing non-performing loans.
Utilizing outdated software, lenders risk losing their business. To avoid negative consequences, let’s consider the disadvantages of traditional credit and risk management software:
The long time lag between loan application and issuance. The process might take up to three weeks.
A high risk of false predictions due to the appliance of unscientific credit models.
Small database of credit scoring factors leading to incorrect predictions concerning certain borrowers.
The price/performance ratio of the majority of credit scoring models is questionable.
Lack of dedicated risk assessment solutions and experience in detailed credit scoring results in the incorrect inference concerning a borrower's abilities to pay back a loan. Traditional scorecards are no longer enough for either banks or small lending organizations
to measure loan reserves and distinguish bad borrowers among the others.
AI and ML in Credit Risk Management Tools
Chatbots, automated management systems, and self-driving vehicles - AI along with ML made these possible. Utilizing them in online credit scoring, users can examine various data points on borrowers, including their payment history and economic behavior.
Not mentioning the fact that such software improves the accuracy of banking operations and shortens the decision-making process.
Reduction in loan management time. Financial organizations and banks take up to three weeks to physically verify each application. Scoring software powered with AI cut the time to minutes. For instance, modern solutions like GiniMachine
verify 1,000 applications in ten seconds.
Decline in credit losses. Prediction of delinquencies before they actually occur is one of the main goals of risk management software. As was mentioned above, traditional solutions can make predictions for several months. While AI
credit score software significantly increases the prediction time for a year in advance.
Accuracy in predictions. Traditional risk management software functions under clearly defined parameters. Utilizing AI and ML, a user gets an intuitive solution that can analyze more databases and learn during the process. Eventually, AI
software comes up with more accurate predictions and declines scoring mistakes.
Wrapping it up
Credit scoring software powered with AI delivers a great market advantage. It’s aimed to solve all possible issues caused by outdated platforms empowering credit risk management in banks and financial institutions. As the business develops, the risks grow,
and AI can provide better control over credit scoring and business processes.