Credit scoring plays an important role in the financial world, benefiting both lenders and borrowers alike. It serves as an essential tool that helps lenders make informed decisions regarding lending money, while at the same time assisting borrowers in securing the loans they require.
Credit scoring is experiencing a major evolution in the era of AI, primarily driven by the growing integration of machine learning and data analytics into the lending process. AI is still in its early ages of development and is mainly based on machine learning. Machine learning algorithms are trained by large sums of information. The AI scoring algorithms are already making an impact in assessing creditworthiness of many individuals. While traditional credit scoring models, like the well-established FICO scores, have long been the go-to standard for evaluating a borrower’s creditworthiness, they come with inherent limitations.
Machine learning models can look at more types of information than just regular credit history. They can analyze transactions, what you do on social media, and more. In addition, AI helps improve risk assessment. Machine learning programs can do a better job at identifying who might have trouble paying back a loan and who’s likely to pay it back. Which helps reduce risks for the lenders. These models look at a lot of different things and find connections that regular credit scores might miss. Let’s take a look at some of the characteristics of AI-powered scoring system:
- Machine learning can help make better predictions when the information is scarce, by leveraging other information sources. Consequently, lenders can extend credit to a larger amount of demographics, such as younger borrowers or immigrants with no established credit histories.
- One more advantage that comes with AI-powered credit scoring systems is speed and efficiency. Access to fast credit is also super important for the borrowers.
- AI-driven credit scoring is more equitable, eradicating bias, shielding lenders from inconsistent human judgments, and decreasing a company’s reliance on human decision-making. This allows avoiding discrimination against protected groups or personal biases that certain credit officers might have.
- Machine learning can personalize decision making by taking into account unique financial situations, needs, and risk profiles. The personalized approach can lead to better loan terms and increased customer satisfaction.
- AI models can be used to monitor borrower’s profile real-time, allowing lenders to adapt to changing circumstances. This process decreases credit risks.
- Machine learning algorithms can improve fraud detection. While analyzing large amounts of information, humans are limited and might miss unusual or suspicious behavior patterns, which is never the case for algorithms.
- AI systems can help automate many parts of the lending process.
However, it should be mentioned that AI participation in credit scoring also increases risks. Many are concerned that our privacy, and security can be compromised. It’s important to ensure that our personal information processed by the lenders, complies with data protection and regulations.
There are a number of steps AI systems use for evaluating a lender’s profile. Tons of data is analyzed by algorithms in real-time to give highly accurate predictions about a person’s creditworthiness.
- The first step is the collection of data to get a complete picture of the individual or entity being evaluated. The algorithm includes various information into their analysis, including: credit history, level of income, employment status, payment history, and more.
- All the collected information is manually filtered by credit officers. All the irrelevant or duplicated data is removed. And the cleaned information is transferred to the AI-powered scoring algorithms.
- The AI models use cleaned up information to make predictions. These models use machine learning, and are trained by analyzing many profiles.
On the other hand, it should be mentioned that these systems are not problem-free. Machine learning is largely based on models that are built by old information. While the economic conditions change. Customers need new types of loans and to satisfy their requirements, creditor companies change their offerings as well. Machine learning algorithms need time to adapt to the new circumstances, and often require adjustments from programmers.
AI is automating a lot of work these days. With the advancements of AI technology, it’s inevitable that AI-powered credit scoring algorithms will become a new standard for many companies. It should be mentioned that algorithm based systems come with certain advantages, such as enhanced decision making capabilities when the information is scarce, improved speed and efficiency, better judgment and eradication of discrimination, making personalized decisions, fraud detection, and improved real-time monitoring to limit the risks.