Credit Sesame traces the evolution of credit scoring from handshake deals to algorithm-based models, with machine learning playing a growing role in how risk is evaluated.
Early lending was all about personal judgment
Before credit scores existed, borrowing decisions were based on trust. Lenders evaluated risk by relying on relationships, reputation, and perceived character. If a borrower seemed reliable or had a known employer, they might get approved. If not, they were often denied.
This approach was deeply subjective and inconsistent. It worked in tight-knit communities, but as lending expanded and populations grew more mobile, the need for standardized decision-making became urgent.
Credit reporting took shape before scoring existed
In the mid-to-late 1800s, lenders in the United States began relying on more than just personal impressions. Local merchants and banks formed credit registries to share written records about customers’ borrowing and repayment habits. These early files were manually kept and often included narrative descriptions of a person’s trustworthiness, reliability, or employment history.
In 1899, the Retail Credit Company was founded in Atlanta to centralize this kind of information. The company, which later became Equifax, sold consumer credit reports to lenders and insurers. These early reports did not include numerical scores or statistical models, but they marked a turning point, moving credit evaluation from word-of-mouth to documented records and setting the stage for future scoring systems.
Credit bureaus and scoring models emerged in the 20th century
In 1956, engineer Bill Fair and mathematician Earl Isaac founded Fair, Isaac and Company, the firm that would later become FICO, with the goal of using statistical modeling to support better business decisions.
By the mid-1900s, credit reporting companies like Equifax were expanding their data collection operations. In 1968, TransUnion entered the market and quickly became a national player. These bureaus enabled lenders to access structured records on account types, balances, and repayment behavior. This shift laid the groundwork for algorithmic scoring and brought more consistency to credit decisions.
In 1989, FICO launched the first widely used general-purpose credit score for consumer lending. Their model applied a consistent algorithm to credit report data to predict how likely someone was to repay a loan.
This scoring model allowed lenders to move beyond subjective judgment and make faster, more consistent decisions. Over time, the FICO score became the industry standard for evaluating consumer credit risk and remains one of the most widely used scoring systems in the United States.
Standardized scores become mainstream
In the 1990s and early 2000s, credit scoring became central to nearly every consumer lending decision. Mortgage lenders, credit card companies, and auto financiers began using scores as part of their automated underwriting processes.
FICO developed industry-specific versions of its scoring model, allowing lenders to tailor decisions to different types of credit. TransUnion expanded its role in the consumer credit landscape during this time, growing alongside Equifax as a major source of credit data. In 1996, the U.S. credit bureau operations of TRW, a major credit reporting company active since the 1960s, were acquired and rebranded as Experian, completing the trio of national credit bureaus that dominate the industry today.
In 2006, the three major credit bureaus Equifax, TransUnion and Experian introduced VantageScore to offer a consistent scoring model across all three databases. It was also designed to include consumers with limited credit history who might be overlooked by traditional models.
By this point, credit scoring was not just a tool but a cornerstone of modern lending. Scores were algorithmic, rules-based and standardized, marking a major departure from the personal assessments of the past.
Modern credit scoring is based on algorithms
Today’s credit scores are still built using algorithms. These models apply defined rules to credit data to generate a numerical score that reflects a person’s credit risk. Common scoring factors include:
- Payment history. Whether bills are paid on time.
- Credit utilization. How much of available credit is used.
- Length of credit history. How long accounts have been open.
- Credit mix. The variety of credit types held.
- Recent credit applications. How often new credit is sought.
Most scoring models use statistical methods such as logistic regression. Their structure is designed to be explainable so that both lenders and consumers can understand how decisions are made.
Machine learning enters the picture
Over the past decade, some lenders and credit scoring developers have begun integrating machine learning into their processes. Unlike traditional models, machine learning can identify patterns in large datasets that were previously too complex to detect.
Rather than replacing algorithms, machine learning is often layered on top to enhance accuracy. It can improve predictions, allow the use of alternative data like rent or utility payments, and adapt more easily to shifting borrower behavior.
Some lenders have built their entire risk models using machine learning, while others use it for specific tasks such as fraud detection. However, because these models are less transparent, they raise concerns about explainability and fairness.
Fintechs diversify credit access and management
Fintech (financial technology)companies are reshaping how credit is understood and accessed. Some focus on developing alternative scoring models, using data such as rent payments, utility bills, bank transactions or subscription histories to evaluate financial behavior. These models aim to assess risk for consumers who may not have traditional credit files.
Other fintechs provide tools that help consumers monitor their credit, track changes in their credit reports, receive alerts about suspicious activity and learn how specific actions may affect their scores. Some offer credit-building products, like secured cards or reporting services for on-time bill payments, to help users establish or improve their credit standing.
Fintech solutions are expanding access to credit insights, offering faster and more flexible evaluations, and helping more people engage with their credit health.
Credit scoring continues to evolve
Credit scoring has moved from reputation to rules, and now toward real-time adaptation. Currently, traditional models still dominate, but machine learning and alternative data are pushing the boundaries of how risk is assessed.
Credit scoring continues to evolve alongside data, technology, and regulation. The challenge is to maintain fairness, accuracy, and transparency as models grow more complex and influential. What began as a handshake has become an algorithmic formula, which continues to change with new data and shifting standards.
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Disclaimer: The article and information provided here are for informational purposes only and are not intended as a substitute for professional advice.
