Credit scores have long been the metric by which banks or lenders assess an individual’s ability to pay down debt or make rent. Credit scoring models are primarily based on a person’s financial history, considering such data as the length of credit history, credit-to-debt ratio, payment history, credit mix, or frequency of credit. This data is then analyzed to assign a 3-digit score that identifies a person’s creditworthiness or risk. The score is used in the approval decisions and factors into things such as loan amount and interest rates. While this has been the standard thus far, current scoring models are criticized for being too narrow in assessing creditworthiness. That’s because credit scores alone may not accurately reflect an individual’s ability to repay a loan. Because one’s score requires a history of responsible credit use, access to credit often excludes certain groups of people, such as those without a credit history or those with a history of financial hardship.
Artificial intelligence (AI) is transforming how credit and loans are awarded to individuals. AI-powered credit scoring algorithms can provide a more comprehensive and accurate assessment of an individual’s creditworthiness by factoring data points that typical scoring models don’t, such as utility payments, bank account balances, or phone bills. These AI models can help to identify patterns and trends that traditional credit scoring methods may miss or not consider. Because AI-powered algorithms can process and analyze larger datasets, lenders will receive a more holistic view of a person’s financial situation. AI could create opportunities for those historically underserved by the credit system to access lines of credit.
The use of AI in credit scoring has the potential to offer many benefits over traditional methods. The top arguments for implementing advanced AI models into credit assessment processes include the following:
AI-powered credit scoring algorithms have the potential to revolutionize the way credit and loans are provided. By taking into account a wide range of data points and using machine learning techniques, these algorithms can provide a more comprehensive and accurate assessment of an individual’s creditworthiness. AI can help to provide access to credit and loans to those who otherwise would not qualify based on credit scores alone and can also help to reduce the risks and costs associated with traditional credit scoring methods. As AI technology continues to advance, we will likely see an increasing number of lenders adopt these types of algorithms to assess creditworthiness and make loan decisions.
AI needs big data to provide actionable insights. So, where is this data coming from? Data providers offer a variety of data sets, including financial transactions data, credit history, consumer demographic and behavioral data, digital footprint, and social media data. Some of the data used in these algorithms may be purchased from third-party data providers, while other data may be collected from internal sources such as the lender’s own database. Some data providers have specific agreements with lenders and credit bureaus that restrict the use of the data, and this data is usually not open-source.
On the other hand, some data used in AI-powered credit scoring algorithms may be open-source. For example, publicly available data such as property records, court records, and government data may be used in the algorithms. Additionally, some lenders may use open-source machine learning libraries and frameworks to build their algorithms.
It is important to note that these examples are not exhaustive, and some of them may not be used by all the companies or lenders. The specific data points used by an AI-powered credit scoring algorithm will vary depending on the lender and the type of loan being offered. It’s also worth noting that the use of open-source data in AI-powered credit scoring algorithms raises some privacy concerns, as it may include individuals’ sensitive personal information. Therefore, lenders and data providers must comply with data protection regulations in their region, ensuring that individuals’ privacy rights are protected.
As mentioned previously, one of the criticisms of the current credit system is its exclusion of populations with no or thin credit history. According to the Consumer Financial Protection Bureau, this equates to 45 million adults which includes low-income consumers, immigrants, minorities, or young adults. AI does have the capability to eliminate bias in consumer lending by using algorithms trained on a diverse and representative dataset and techniques such as fair feature engineering and fair model selection to ensure that the algorithm does not perpetuate existing biases. Additionally, transparency and interpretability of the AI model can be implemented to allow for human oversight and ensure that any potential biases are identified and addressed. However, AI itself is not inherently unbiased, and the bias can be introduced during the data collection, annotation, and feature selection process. Thus, it’s crucial to have a diverse team and ethical oversight to build and monitor the model. If the data used to train the AI includes biases, it can lead the AI to make biased decisions. To put it simply, AI is a computer system that must be trained to make decisions. That training requires ethical human intelligence.
AI-powered credit scoring is transforming how the lending industry operates and provides a wide range of opportunities for lenders and borrowers alike. By considering a wide range of data points and using machine learning techniques, these algorithms can provide a more comprehensive and accurate assessment of an individual’s creditworthiness. This holistic view can provide access to credit and loans to those who otherwise would not qualify based on credit scores alone and can also help to reduce the risks and costs associated with traditional credit scoring methods. As AI technology continues to advance, we will likely see an increasing number of lenders adopt these types of algorithms to assess creditworthiness and make loan decisions. All told, using AI in credit scoring can lead to more efficient, accurate, and fair lending decisions, improving the overall lending process for both borrowers and lenders.