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  • About us
  • Our work
    • Model development
      • Credit risk management
      • Market risk, counterparty credit risk, and liquidity risk
      • Operational risk management
      • Stress testing and balance sheet management
    • Model risk management
    • Model validation
    • De-risking analytics
    • Artificial Intelligence and Machine Learning
    • Risk data
    • Timely industry and macroeconomic information
  • Our insights
  • Our people
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Our insights

  • Model risk management: Time to rethink the model landscape and model life cycle

    n a post-COVID-19 world, proactive model risk management by all lines of defense is needed now – not only to meet new regulatory expectations, but also to strengthen institutional resiliency.

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  • Business as usual?: What precrisis analytics models taught us

    The COVID-19 pandemic has revealed deep flaws in some widely used advanced analytics techniques. One of the most widely used advanced analytics techniques, machine learning, relies on historical patterns of data to predict future behavior.

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  • Changing the rules of engagement with AI: Derisking with scale

    Businesses across every industry will need to adopt AI in order to remain competitive in the current market but implementation can be fraught with risk.

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  • Adjusting banking models in a post-COVID-19 world

    The COVID-19 pandemic has revealed unexpected flaws in the business models that banks rely upon. How can they best address this challenge?

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  • Ensuring digital trust: The integration of cybersecurity with financial crime and fraud

    Banks have traditionally considered financial crime and fraud as two distinct categories of risk. But as these crimes become increasingly sophisticated, the traditional boundaries between them are becoming blurred.

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  • Technological social responsibility: Now is the time to embrace AI

    Welfare economics seeks to understand the value of both the positives and negatives of technology adoption. 

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  • As model risk management evolves, so does its value

    As models becomes more embedded across every aspect of banks’ business operations, the drive towards an increasingly efficient and value-driven approach to Model Risk Management (MRM) is growing.

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  • A pre-crisis view of model-risk management: Insights from Asia, Europe and North America

    The impact of the global pandemic on banking business operations has uncovered some unexpected flaws in the models that institutions rely on to operate their businesses.

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  • EBA report on Big Data and Advanced Analytics – establishing a basis for analytics governance

    The European Banking Authority’s (EBA) January 2020 report on the increasing use  of Big Data and Advanced Analytics (BD&AA) in the banking sector provides guidance for banks on improving controls in their BD&AA implementation.

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  • Derisking machine learning and artificial intelligence

    The added risk brought on by the complexity of machine-learning models can be mitigated by making well-targeted modifications to existing validation frameworks.

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