Through utilizing machine learning and artificial intelligence, AI-based credit scoring is a complex and modernized way to measure an individual's creditworthiness. However, this concept is not without its concerns.
, discovering patterns within the data. In credit scoring, an unsupervised model may cluster individuals based on transaction data or browsing behavior. These clusters can infer individuals’ creditworthiness. Hybrid models combine both supervised and unsupervised learning techniques. They use unsupervised learning to discover new data features or relationships, and supervised learning for predictions.
” problem can complicate regulatory compliance, especially in places where lenders must explain credit decisions to consumers. AI models may perpetuate biases if the training data is skewed or unrepresentative. This can lead to unfair credit decisions affecting specific individuals or groups. Using non-traditional data sources like social media or browsing behavior introduces privacy and data security risks.
However, such integration also introduces new challenges. For instance, implementing blockchain technology can be complex and costly, and there are also potential privacy concerns due to the transparent nature of blockchain transactions.
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