• Algorithm Integrity Matters: for Financial Services leaders, to enhance fairness and accuracy in data processing

  • By: Risk Insights: Yusuf Moolla
  • Podcast

Algorithm Integrity Matters: for Financial Services leaders, to enhance fairness and accuracy in data processing

By: Risk Insights: Yusuf Moolla
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  • Summary

  • Insights for financial services leaders who want to enhance fairness and accuracy in their use of data, algorithms, and AI.

    Each episode explores existing and emerging challenges and solutions related to algorithmic integrity, including discussions on navigating independent audits.

    The goal of this podcast is to give leaders the knowledge they need to ensure their data practices benefit customers and other stakeholders, reducing the potential for harm and upholding industry standards.
    © 2024 Risk Insights Pty. Ltd.
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Episodes
  • 15. Algorithm Integrity Documentation - Getting Started

    15. Algorithm Integrity Documentation - Getting Started
    Nov 19 2024

    Spoken (by a human) version of this article.

    Documentation makes it easier to consistently maintain algorithm integrity.

    This is well known.

    But there are lots of types of documents to prepare, and often the first hurdle is just thinking about where to start.

    So this simple guide is meant to help do exactly that – get going.

    About this podcast

    A podcast for Financial Services leaders, where we discuss fairness and accuracy in the use of data, algorithms, and AI.

    Hosted by Yusuf Moolla.
    Produced by Risk Insights (riskinsights.com.au).
    Show more Show less
    5 mins
  • 14. External data - use with care

    14. External data - use with care
    Nov 12 2024

    Spoken (by a human) version of this article.

    Banks and insurers are increasingly using external data; using them beyond their intended purpose can be risky (e.g. discriminatory).

    Emerging regulations and regulatory guidance emphasise the need for active oversight by boards, senior management to ensure responsible use of external data.

    Keeping the customer top of mind, asking the right questions, and focusing on the intended purpose of the data, can help reduce the risk.

    Law and guideline mentioned in the article:

    • Colorado's External Consumer Data and Information Sources (ECDIS) law
    • New York's proposed circular letter.

    About this podcast

    A podcast for Financial Services leaders, where we discuss fairness and accuracy in the use of data, algorithms, and AI.

    Hosted by Yusuf Moolla.
    Produced by Risk Insights (riskinsights.com.au).
    Show more Show less
    7 mins
  • 13. Bridging the purpose-risk gap: Customer-first algorithmic risk assessments

    13. Bridging the purpose-risk gap: Customer-first algorithmic risk assessments
    Nov 5 2024

    Spoken (by a human) version of this article.

    Banks and insurers sometimes lose sight of their customer-centric purpose when assessing AI/algorithm risks, focusing instead on regular business risks and regulatory concerns.

    Regulators are noticing this disconnect.

    This article aims to outline why the disconnect happens and how we can fix it.

    Report mentioned in the article: ASIC, REP 798 Beware the gap: Governance arrangements in the face of AI innovation.

    About this podcast

    A podcast for Financial Services leaders, where we discuss fairness and accuracy in the use of data, algorithms, and AI.

    Hosted by Yusuf Moolla.
    Produced by Risk Insights (riskinsights.com.au).
    Show more Show less
    7 mins

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