AI in lending uses machine learning to analyze customer behavior patterns (such as mobile wallet usage frequency, velocity, and variety) to predict loan repayment likelihood, continuously improving its predictive accuracy by learning from each loan outcome in real-time, unlike static rule-based systems.
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Real AI vs. Rules: How AI is Transforming Lending #shortsAdded:
Real artificial intelligence, on the other hand, is like Spotify recommending songs. It learns from your behavior and improves over time. It doesn't just follow rules, it predicts and adapts. In lending, that means analyzing thousands of customers' behaviors and deciding, "Hmm, people who use their mobile wallets with a certain level of frequency, velocity, and variety, or spend airtime in a certain way, usually repay well." So, a machine learning model will, one, look at the patterns that led to successful repayment, two, flag behaviors associated with defaults, and three, adjust the way it scores new applicants all on its own.
So, every time someone takes a loan and repays a loan, or doesn't repay the loan, the model improves. That's the power of learning in real time. So, machine learning models don't just use static formulas, they learn from the data.
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