How might we create continuous, transparent, and real-time visibility into third-party and supply chain risks across the financial sector to proactively mitigate disruptions?​

Background: Financial institutions engage with a complex web of third-party vendors, exposing them to risks like financial instability, cyber threats, compliance issues, and operational disruptions. Existing risk monitoring tools often lack real-time visibility, making it difficult to detect and respond to emerging risks before they affect operations.​

We are interested in: Solutions that offer continuous, real-time monitoring of third-party and supply chain risks. These should enable quick identification of potential issues (cyber threats, compliance failures, financial instability, etc.), provide actionable insights, and integrate easily into existing risk management systems within the financial sector.​

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How could dynamic, data-driven simulation and scenario planning platforms be designed to enable financial institutions to model the impact of operational disruptions, test response strategies, and enhance decision-making under uncertainty?​

Background: Financial institutions face growing risks from economic shifts, cyber threats, and other disruptions. Traditional risk management methods often fail to predict or effectively respond to these events. A dynamic simulation platform could help institutions model potential disruptions, test response strategies, and enhance decision-making during crises.​

We are interested in: Simulation platforms that allow financial institutions to model disruptions, test strategies, and improve decision-making under uncertainty. The solution should integrate with existing risk management frameworks and be adaptable to different operational scenarios.​

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How might solutions enable the continuous monitoring, validation, and improvement of data quality and accuracy across complex financial ecosystems, ensuring high integrity and reliability of critical datasets for operational resilience and risk mitigation?​

Background: Accurate data is crucial for decision-making, compliance, and risk management in the financial sector. However, ensuring data quality across interconnected systems and third parties is challenging. Inaccurate data can lead to disruptions and increased risk, there maintaining high data integrity is vital for operational resilience.​

We are interested in: Solutions that enable real-time monitoring, validation, and correction of data quality across financial ecosystems, ensuring reliable, consistent data for decision-making, compliance, and risk mitigation.​

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How can we explore using tech, data and AI to support automated and scalable processes that enable financial institutions and their suppliers to baseline operational resilience requirements across multiple jurisdictions, map them to internal business processes, and deliver a clear, actionable gap analysis?​

Background: Financial institutions face complexity in ensuring compliance and operational resilience across different jurisdictions with varying regulatory requirements. Traditional gap analysis is often slow and error-prone. Leveraging tech, data, and AI can automate and streamline this process, improving efficiency and accuracy.​

We are interested in: Solutions that use AI and automation to baseline resilience requirements across regions, map them to business processes, and provide actionable gap analysis to ensure compliance and enhance global operational resilience.​

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How can we help to promote a strong business resilience culture, based on clear accountability and responsibility and the cultivation of “common cause” in building operational resilience?​

Background: Financial institutions face complexity in ensuring everyone understands the part they play in ensuring operational resilience. Concerns are often siloed with IT or compliance functions, with risk ownership poorly understood, leading to fragmentation of responsibility. Awareness of compliance and risk factors and how they relate to individuals across an institution can be a complex undertaking, but being unable to engage the whole organisation reduces incident preparedness, causes inconsistent response coordination, and can lead to organisations underestimating non-technology threats such as communication breakdowns, decision reluctance/ paralysis, and third-party concentration risk.​

We are interested in: Solutions that use data and AI to provide decision support, increase preparedness for non-technology operational resilience risks, provide deeper stakeholder awareness of business readiness, and that generate dynamic user education and engagement content that help to build a resilience culture across an organisation.​

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How might we better understand where our technology infrastructure risks reside, and better manage the concentration of control, particularly in Hyperscale Public Cloud contexts?​

Background: Financial institutions have been seeking to simplify and rationalise digital and IT infrastructure to improve scalability and ease manageability. With the maturation of public and hybrid cloud architectures, most organisations operate a hybrid infrastructure across cloud and on-premise infrastructure. There is a challenge with maintaining visibility of the risks across and within these environments and accurately measuring the level of risk and dependency they originate, particularly when it comes to shared or abstracted control, which reduces visibility, increases the chance of misconfiguration across dependent infrastructure, and makes incident resolutions paths more difficult to trace during disruption. ​

We are interested in: Solutions that use AI and automation to map an organisation’s technology infrastructure risk profile across a hybrid cloud architecture and use it to create an intelligent observability platform that combines real-time telemetry, dependency mapping automation, and AI risk modelling with the goal of increasing understanding of business-critical function reliance on cloud and enhance root cause analysis and compliance reporting.​

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