Last weekend, the New York Knicks defeated the San Antonio Spurs 94-90 in Game 5 of the NBA Finals, clinching the championship with a 4-1 series victory. This marks the Knicks' first title in 53 years. Founded in 1946 alongside the NBA, the Knicks have historically profited from high media fees and ticket sales due to their location in the lucrative New York market, despite having won the championship only twice. Their last Finals appearance was in 1999, making this victory a significant milestone after half a century.
This championship cannot be attributed solely to the performance of a few star players. After repeatedly investing heavily in players based on past performance and reputation without satisfactory results, the Knicks re-evaluated their player assessment methods. Moving away from traditional reliance on names and historical records, they redesigned their player selection and operational strategies based on data analysis and scientific statistics, breaking a long-standing cycle of disappointment. This approach mirrors the strategy used by the Oakland Athletics in the film "Moneyball," where they utilized sabermetrics to discover undervalued players and challenge conventional wisdom in Major League Baseball.
Interestingly, a similar transformation is occurring in the South Korean financial sector. For years, the industry has used credit scores as a simple metric, akin to a player's average points per game. A high score indicated a low-risk borrower, while a low score suggested high risk, directly influencing interest rates and lending limits. However, in modern NBA terms, two players averaging 20 points may not contribute equally to their team's success. One player might accumulate points through a high volume of low-percentage shots, while another could significantly enhance team performance through efficient offense and defense. Consequently, today's NBA evaluates players beyond traditional metrics like points and rebounds, evolving to analyze scoring efficiency, defensive contributions, and overall impact on team victories.
The same principle applies to finance. Two individuals with identical credit scores may have vastly different repayment capabilities, cash flows, and potential for future defaults. Ultimately, the score itself is less important than the context behind it. Just as the Knicks achieved their championship through data-driven player evaluation and strategy, AI credit assessment technologies are beginning to differentiate actual repayment abilities within the same credit score range. Just as players with similar statistics can have different contributions to their teams, borrowers with the same credit score can present varying levels of risk.
As a result, changes are emerging in the long-ignored space between first-tier and second-tier financial institutions. Borrowers previously classified as high-risk in the second-tier market are now being more accurately assessed through AI credit evaluations, allowing many who once faced uniformly high interest rates to secure financing at more reasonable rates. In fact, the fintech company I am involved with is combining savings bank capital with AI risk management technology to introduce a new 1.5-tier financial model. This model offers mid-tier loans at around 11% interest to borrowers with average credit scores in the 700s while maintaining soundness, achieved not by eliminating risk but by measuring it more precisely.
This is not merely a success story of reducing interest rates by a few percentage points. It represents a new financial ladder for mid-tier borrowers who have long struggled to access traditional banking services or faced burdensome high-interest loans. In other words, it is a change that enables more rational capital allocation by interpreting credit more accurately rather than simply lowering the credit threshold.
The story of the New York Knicks is remarkable for this reason. They did not suddenly acquire superior players; they merely changed their perspective on evaluating talent. By trusting the actual value revealed by data rather than relying on names and reputations, they reached a pinnacle that had eluded them for half a century.
In finance, innovation begins not by changing people but by changing the way we view them. If AI credit assessment technology is beginning to uncover the true value of mid-tier borrowers, the changes we are witnessing may signify the dawn of a new "Moneyball" era in finance.
* This article has been translated by AI.
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