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1. data high quality and availability: Credit score optimization depends on correct and well timed knowledge on buyer behavior, credit danger, market conditions, and regulatory necessities. Nonetheless, information high quality and availability can vary across completely different sources and regions, and could also be incomplete, outdated, or inconsistent. This will result in suboptimal or erroneous credit decisions, and expose the lender to operational and reputational risks. To beat this problem, lenders have to put money into data governance and management, and make sure that they've reliable and standardized information sources and processes. They also have to leverage advanced analytics and artificial intelligence to extract insights and value from their knowledge, and to observe and replace their credit score models and insurance policies.

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