Advances in AI and predictive analytics are using consumer scores to automate business decisions to predict things like risk and fraud. But concerns over fairness mean companies need to make scores transparent to consumers.
How are novel AI, predictive analytics, and algorithmic decision-making being used to classify and score consumers?
Early applications of consumer scoring and predictive analytics across industries include customer lifetime value, segmentation and modeling, risk assessment, fraud detection, and even tenant and hiring screening.
What are some challenges and pitfalls companies face when implementing predictive analytics and consumer scoring tools?
AI introduces new challenges that highlight the importance of finding the right model fit for a company’s use case, closely auditing systems for errors and bias, and supporting transparency to justify practices when automated decisions adversely impact consumers.
How can we expect consumer scores to be regulated, and what can be learned from the history of credit reporting and recent consumer data protection measures?
Consumer scoring applications have the potential to limit access to opportunities and enable discriminatory practices. Transparency is key. Scoring data and decisions should be made available to consumers, even if regulation does not yet explicitly require it for a given industry. Scores should be displayable, disputable, and correctable.
WHAT’S IN THIS REPORT? This report examines new consumer scoring applications across industries and describes challenges companies face with AI decision-making. It includes best practices for fair and accountable predictive analytics.
KEY STAT: Only half of adults worldwide are comfortable with companies using AI to access personal data for an improved customer experience.
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