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  • About us
  • Our work
    • Artificial intelligence and machine learning
    • De-risking analytics
    • Model analytics transformation
    • 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
    • Risk data
    • Timely industry and macroeconomic information
  • Our insights
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Our insights

  • Navigating Climate Analytics through Effective Model Risk Management

    Managing the risks and complexities associated with climate models requires a specialized approach for model risk managers to effectively address and ensure their readiness.

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  • Strengthening competitive positioning and addressing inflation risks through analytic

    Company leaders can gain competitive advantage and defend their organizations via analytics-aided strategies.

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  • Banking solutions for cybersecurity and model risk management

    Time is of the essence. In an ever-evolving industry, banks must pivot more effective approaches to cybersecurity solutions and model risk management. The sooner banks start their journey, the faster they can establish controls and manage risk.

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  • Revisioning lending to small and medium-sized banking enterprises

    Through modernizing business-lending procedures, banks can harness new SME opportunities and achieve forecasted growth gains.

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  • IFRS9 models new realities and lessons

    The International Financial Reporting Standard (IFRS) 9 models need to evolve quickly. There is no doubt the pandemic’s impact on the models and framework generated stressors in unforeseen ways, creating significant challenges to banks’ loan-loss provisioning levels.

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  • Model risk management evolves in response to uncertain times

    By enhancing MRM framework capabilities—organizations are upgrading validation resources. Risk culture, standards, and procedures rank high on the overall MRM 2.0 agenda.

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  • Innovating credit-decisioning models to meet future challenges

    Banks need to implement more automated credit-decisioning models that can tap new data sources, understand customer behaviors more precisely, open new segments, and react faster to changes in the business environment. These best practices can help any bank elevate its credit model.

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  • Balancing intelligent automation with risk management

    Organizations need a better way to maximize benefits of new technologies while minimizing risks, as adoption of automation and artificial intelligence grows.

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  • Managing bias: Advanced risk analytics in public services

    There are many applications that can help public agencies improve outcomes and streamline processes to make the best use of limited resources. New technologies, including AI and ML do come with challenges, and understanding these risks, including bias, is crucial for organizations to increase positive outcomes.

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  • The evolution of model-risk management: Six best practices for banks

    In the decade since US banking regulators published SR 11-7, model-risk management development has continued to evolve, challenged even more so by the COVID-19 pandemic. Six best practices may help banks to rebuild better after the crisis and avoid undesirable trade-offs between cost, timelines and quality.

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  • Unlocking the benefits of model life-cycle management

    Given the complexities of the global marketplace, it is critical that FIs improve the management of their model life cycle to improve efficiencies and controls. By taking a more integrated, strategic approach to the management of the model life cycle, banks can unlock massive model development and validation potential.

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  • Nowcasting, dealing with structural breaks

    Traditional nowcasting has served its purpose well, but the COVID-19 crisis proved a challenge for typical nowcasting models. Today’s next-generation nowcasting approach reduces the number of variables for more accurate outcomes and making it easier to interpret estimates, understand structural breaks, and provide up-to-the-moment information.

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  • Understanding and prioritizing the risks and AI

    Organizations must adopt concrete, dynamic frameworks to manage AI risks. This pinpointing, prioritization and management of AI risks now should be part of a holistic, long-term strategy to create value for the future.

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  • The new opportunities of a post-pandemic credit cycle

    As markets slowly resume normal activity, a new credit cycle will being, offering innovative leaders a rare opportunity to expand into credit markets and win market share

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  • Moving model risk management to the next level

    COVID-19 has amplified the scope and use of model risk management based on advanced analytics

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

    In 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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  • Opening up machine learning’s potential in capital markets

    The financial markets are still experiencing the aftershocks of the pandemic, with volatility remaining above long-term average rates.

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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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  • 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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  • 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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  • 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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  • 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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