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AI-Driven Decision Making 2022: The Future of the Data-Driven Workplace

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  • 17 Pages
  • July 2022
  • Region: Global
  • Arizent
  • ID: 5715062
Artificial intelligence (AI) is long past the days of being regarded as a novel business tool and today is a part of every company’s playbook. Regardless of which firms are using the technology and which organizations are standing on the side-lines, AI’s impact on data-driven decision making across every financial services sector is undeniable.

The technology, along with machine learning (ML), is used for functions as simple as scheduling and planning to as complex as underwriting and fraud detection. The tools have allowed some firms to jump ahead of their competitors, utilizing hardware and software to improve efficiency, reduce costs, manage risk and in a more stark realization, eliminate the need for once-vital personnel.

The implementation of AI and analytics in data-driven decision making hasn’t gone without its share of controversy. The use of AI in lending decisions, for example, has come under scrutiny by federal oversight agencies like the Consumer Financial Protection Bureau, which says algorithms can’t always issue fair lending decisions because the algorithms can never be free of bias and could lead to digital redlining and robo-discrimination.

Still, business leaders push forward in AI implementation for its power in risk management and other industry-specific objectives. Companies not yet utilizing AI cite a lack of talent, unreliable datasets and high costs to get programs running. On the contrary, those using AI say its power to both cut costs and generate new areas of revenue give them strong forward-looking building blocks.

This study dives into the AI playbooks of financial leaders and describes their achievements, shortcomings, concerns and projections for further AI integration.

Table of Contents

  • Introduction
  • Key Findings
  • Methodology
  • Optimism in their company’s efforts
  • AI provides solace in security
  • The major factors inhibiting AI use
  • AI’s lasting impact on the bottom line
  • Conclusion