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Predictive Analytics and Consumer Scoring

Automated Decision-Making with AI Requires Transparency

About This Report
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.
Table of Contents

Executive Summary

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.

Here’s what’s in the full report


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Table of Contents

  1. Executive Summary
  2. AI Advances Predictive Analytics and Consumer Scoring
  3. Consumer Scoring Applications Across Industries
  4. AI Introduces New Adoption Challenges
  1. Regulatory Landscape for Consumer Scoring
  2. Best Practices for Predictive Analytics and Consumer Scoring
  3. Key Takeaways
  4. Insider Intelligence Interviews
  1. Read Next
  2. Sources
  3. Media Gallery

Charts in This Report


Sara M. Watson


Ryan Arnold
Rahul Chadha
Maria Elm
Chris Keating
Research Director
Ann Marie Kerwin
Executive Editor, Content Strategist
Lucy Koch
Junior Analyst
Stephanie Taglianetti
Yoram Wurmser
Principal Analyst
Alicia Young
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