Aspect Traditional Credit Scoring AI-Powered Credit Scoring
Data Used Relies heavily on credit history and past payment records AI algorithms can analyze various data sources, including financial data, social media interactions, credit behavior, and transaction history.
Predictive Accuracy Comparatively, it is low due to manual errors and inaccuracies High due to advanced algorithms and continuous learning
Focus Primarily on past financial behavior and pre-defined rules Considers financial history as well as alternative data sources along with future potential
Credit Invisibility Can overlook individuals with limited credit history Includes individuals who are new to credit or have non-traditional financial data

Bias and Fairness Prone to biases, unfairness, and inequalities

Reduce biases and inaccuracies with comprehensive data analysis
Personalization Follows a rule-based, one-size-fits-all approach Prioritize personalization by analyzing individual behaviors
Operational Efficiency Manual processes lead to inefficiencies and delays Automated processes enable faster, more efficient, and more accurate credit assessments