Nasdaq Integrates AI to Revolutionize Portfolio Risk Calculations for Banks and Insurers

Nasdaq integrates AI for portfolio risk calculations

This content is provided by an external author without editing by Finextra. It expresses the views and opinions of the author. The functionality will be integrated into Nasdaq's Calypso platform, which is used by banks, insurers, and other financial institutions globally to access capital markets, process front-to-back office treasury workflows, manage risk, and meet regulatory reporting obligations. XVA is a family of Value Adjustments made to derivative values to reflect the impact of risk, funding, capital, and other costs associated with trading OTC derivatives. A well-known example is a Credit Valuation Adjustment where changes are made to reflect counterparty credit risk inherent in bilateral transactions. This process has been critical to help banks manage risks since the Global Financial Crisis of 2007-8. Alongside the development of structured products, financial engineering has led to highly complex derivative pricing models, demanding more sophisticated internal risk modelling alongside greater regulatory oversight. Collectively, this has placed a substantial and costly computational requirement on the industry. Nasdaq's machine learning technology is combined with a sophisticated form of mathematical modelling that can significantly improve the efficiency of conducting the most complex trading and regulatory risk calculations. It transforms the time taken to price financial instruments across millions of scenarios, processing the most complex products up to 100 times faster whilst maintaining high levels of accuracy. It can also significantly reduce the amount of physical infrastructure required to run those calculations. Gil Guillaumey, Senior Vice President and Head of Capital Markets Technology at Nasdaq, said: "All financial institutions trading OTC derivatives are required to perform increasingly complex calculations to meet internal risk controls and regulatory mandates. Maintaining the necessary infrastructure and systems can be outrageously expensive, inefficient, and increasingly impractical regardless of cloud elasticity strategies. The sheer scale of computing power required to meet the most demanding regulations, alongside the strategic benefits of more accurate real-time analytics, is driving a profound rethink about how we can leverage AI to reduce the cost of compliance." Industry-wide rise in risk analytics The ability to accurately model risk across asset classes is essential for optimal trading decisions, accurate accounting of risk profiles, and calculating capital requirements. Risk functions within financial institutions are therefore consistently enhancing their own internal framework, while regulators also recognize the benefits of increasing the volume and frequency of risk calculations such as XVA and Value-at-Risk. For example, The Standardized Approach for XVA under Basel III Endgame introduces a more complex and granular series of calculations across firms' trading portfolios. The regulation is aligned to best practice risk management controls, with some banks already performing intraday calculations; however, others do not have the system capability and have to accept the costs charged by their counterparty. Today, a typical Credit Value Adjustment computation involves millions of Monte Carlo simulations over a series of points in time to produce up to 10 billion revaluations. Firms are also increasingly required to run sensitivity analysis on those calculations for risk management purposes, which can result in up to 1 trillion calculations per day for a typical portfolio, requiring a huge amount of computational power and physical infrastructure. Nasdaq's XVA Accelerator Called the XVA Accelerator, Nasdaq's innovative technology uses a mathematical approach known as Chebyshev Tensors, drawing on a patented technique and modelling expertise from MoCaX Intelligence. It incorporates a breakthrough mathematical theorem by Sergei Bernstein that allows users to identify groups of scenarios that are highly likely to converge at an exponential speed toward the target result. This will allow very accurate approximations for an extensive range of scenarios, whilst requiring substantially fewer calculations than the original method. The Chebyshev Tensors in the XVA Accelerator are calibrated in a dynamic manner each time an XVA calculation is launched. As a result, it adapts immediately to changing market conditions, which can prove particularly valuable in moments of market disruption. With this technology, the Nasdaq Calypso risk analytics suite can rapidly adjust during periods of heightened volatility, or fluctuating interest rates, by identifying a smaller number of 'smart' scenarios to provide more timely risk analytics. The model can transparently detail how it has arrived at each assumption and lower the energy requirements, or carbon footprint, associated with conducting computationally intensive calculations. Ultimately, this approach can significantly improve execution times, reduce costs, and empower financial institutions to more effectively manage risk. As a scaled platform partner, Nasdaq draws on deep industry experience, technology expertise and cloud managed service services to help 3,500+ banks, brokers, regulators, financial infrastructure operators, and buy-side firms solve their toughest operational challenges while advancing industrywide modernization.

Nasdaq applies machine learning to investmentment portfolio risk calculations

This content is provided by an external author without editing by Finextra. It expresses the views and opinions of the author. The functionality will be integrated into Nasdaq's Calypso platform, which is used by banks, insurers, and other financial institutions globally to access capital markets, process front-to-back office treasury workflows, manage risk, and meet regulatory reporting obligations. XVA is a family of Value Adjustments made to derivative values to reflect the impact of risk, funding, capital, and other costs associated with trading OTC derivatives. A well-known example is a Credit Valuation Adjustment where changes are made to reflect counterparty credit risk inherent in bilateral transactions. This process has been critical to help banks manage risks since the Global Financial Crisis of 2007-8. Alongside the development of structured products, financial engineering has led to highly complex derivative pricing models, demanding more sophisticated internal risk modelling alongside greater regulatory oversight. Collectively, this has placed a substantial and costly computational requirement on the industry. Nasdaq's machine learning technology is combined with a sophisticated form of mathematical modelling that can significantly improve the efficiency of conducting the most complex trading and regulatory risk calculations. It transforms the time taken to price financial instruments across millions of scenarios, processing the most complex products up to 100 times faster whilst maintaining high levels of accuracy. It can also significantly reduce the amount of physical infrastructure required to run those calculations. Gil Guillaumey, Senior Vice President and Head of Capital Markets Technology at Nasdaq, said: "All financial institutions trading OTC derivatives are required to perform increasingly complex calculations to meet internal risk controls and regulatory mandates. Maintaining the necessary infrastructure and systems can be outrageously expensive, inefficient, and increasingly impractical regardless of cloud elasticity strategies. The sheer scale of computing power required to meet the most demanding regulations, alongside the strategic benefits of more accurate real-time analytics, is driving a profound rethink about how we can leverage AI to reduce the cost of compliance." Industry-wide rise in risk analytics The ability to accurately model risk across asset classes is essential for optimal trading decisions, accurate accounting of risk profiles, and calculating capital requirements. Risk functions within financial institutions are therefore consistently enhancing their own internal framework, while regulators also recognize the benefits of increasing the volume and frequency of risk calculations such as XVA and Value-at-Risk. For example, The Standardized Approach for XVA under Basel III Endgame introduces a more complex and granular series of calculations across firms' trading portfolios. The regulation is aligned to best practice risk management controls, with some banks already performing intraday calculations; however, others do not have the system capability and have to accept the costs charged by their counterparty. Today, a typical Credit Value Adjustment computation involves millions of Monte Carlo simulations over a series of points in time to produce up to 10 billion revaluations. Firms are also increasingly required to run sensitivity analysis on those calculations for risk management purposes, which can result in up to 1 trillion calculations per day for a typical portfolio, requiring a huge amount of computational power and physical infrastructure. Nasdaq's XVA Accelerator Called the XVA Accelerator, Nasdaq's innovative technology uses a mathematical approach known as Chebyshev Tensors, drawing on a patented technique and modelling expertise from MoCaX Intelligence. It incorporates a breakthrough mathematical theorem by Sergei Bernstein that allows users to identify groups of scenarios that are highly likely to converge at an exponential speed toward the target result. This will allow very accurate approximations for an extensive range of scenarios, whilst requiring substantially fewer calculations than the original method. The Chebyshev Tensors in the XVA Accelerator are calibrated in a dynamic manner each time an XVA calculation is launched. As a result, it adapts immediately to changing market conditions, which can prove particularly valuable in moments of market disruption. With this technology, the Nasdaq Calypso risk analytics suite can rapidly adjust during periods of heightened volatility, or fluctuating interest rates, by identifying a smaller number of 'smart' scenarios to provide more timely risk analytics. The model can transparently detail how it has arrived at each assumption and lower the energy requirements, or carbon footprint, associated with conducting computationally intensive calculations. Ultimately, this approach can significantly improve execution times, reduce costs, and empower financial institutions to more effectively manage risk. As a scaled platform partner, Nasdaq draws on deep industry experience, technology expertise and cloud managed service services to help 3,500+ banks, brokers, regulators, financial infrastructure operators, and buy-side firms solve their toughest operational challenges while advancing industrywide modernization.

Nasdaq Integrates AI to Simplify and Accelerate Bank and Insurance Risk Calculations By Investing.com

Market volatility and regulatory requirements are driving increasingly complex and computationally intensive risk calculations XVA sensitivity analysis can require over 1 trillion calculations per day, requiring substantial physical infrastructure Nasdaq incorporates AI-based machine learning to process risk calculations up to 100 times faster NEW YORK, Oct. 17, 2024 (GLOBE NEWSWIRE) -- Nasdaq (Nasdaq: NDAQ) today announced it has developed an innovative new methodology to conduct investment portfolio risk calculations and produce predictive analytics, based on advanced machine learning capability. The functionality will be integrated into Nasdaq's Calypso platform, which is used by banks, insurers, and other financial institutions globally to access capital markets, process front-to-back office treasury workflows, manage risk, and meet regulatory reporting obligations. XVA is a family of Value Adjustments made to derivative values to reflect the impact of risk, funding, capital, and other costs associated with trading OTC derivatives. A well-known example is a Credit Valuation Adjustment where changes are made to reflect counterparty credit risk inherent in bilateral transactions. This process has been critical to help banks manage risks since the Global Financial Crisis of 2007-8. Alongside the development of structured products, financial engineering has led to highly complex derivative pricing models, demanding more sophisticated internal risk modelling alongside greater regulatory oversight. Collectively, this has placed a substantial and costly computational requirement on the industry. Nasdaq's machine learning technology is combined with a sophisticated form of mathematical modelling that can significantly improve the efficiency of conducting the most complex trading and regulatory risk calculations. It transforms the time taken to price financial instruments across millions of scenarios, processing the most complex products up to 100 times faster whilst maintaining high levels of accuracy. It can also significantly reduce the amount of physical infrastructure required to run those calculations. Gil Guillaumey, Senior Vice President and Head of Capital Markets Technology at Nasdaq, said: All financial institutions trading OTC derivatives are required to perform increasingly complex calculations to meet internal risk controls and regulatory mandates. Maintaining the necessary infrastructure and systems can be outrageously expensive, inefficient, and increasingly impractical regardless of cloud elasticity strategies. The sheer scale of computing power required to meet the most demanding regulations, alongside the strategic benefits of more accurate real-time analytics, is driving a profound rethink about how we can leverage AI to reduce the cost of compliance. Industry-wide rise in risk analytics The ability to accurately model risk across asset classes is essential for optimal trading decisions, accurate accounting of risk profiles, and calculating capital requirements. Risk functions within financial institutions are therefore consistently enhancing their own internal framework, while regulators also recognize the benefits of increasing the volume and frequency of risk calculations such as XVA and Value-at-Risk. For example, The Standardized Approach for XVA under Basel III Endgame introduces a more complex and granular series of calculations across firms' trading portfolios. The regulation is aligned to best practice risk management controls, with some banks already performing intraday calculations; however, others do not have the system capability and have to accept the costs charged by their counterparty. Today, a typical Credit Value Adjustment computation involves millions of Monte Carlo simulations over a series of points in time to produce up to 10 billion revaluations. Firms are also increasingly required to run sensitivity analysis on those calculations for risk management purposes, which can result in up to 1 trillion calculations per day for a typical portfolio, requiring a huge amount of computational power and physical infrastructure. Nasdaq's XVA Accelerator Called the XVA Accelerator, Nasdaq's innovative technology uses a mathematical approach known as Chebyshev Tensors, drawing on a patented technique and modelling expertise from MoCaX Intelligence. It incorporates a breakthrough mathematical theorem by Sergei Bernstein that allows users to identify groups of scenarios that are highly likely to converge at an exponential speed toward the target result. This will allow very accurate approximations for an extensive range of scenarios, whilst requiring substantially fewer calculations than the original method. The Chebyshev Tensors in the XVA Accelerator are calibrated in a dynamic manner each time an XVA calculation is launched. As a result, it adapts immediately to changing market conditions, which can prove particularly valuable in moments of market disruption. With this technology, the Nasdaq Calypso risk analytics suite can rapidly adjust during periods of heightened volatility, or fluctuating interest rates, by identifying a smaller number of ~smart' scenarios to provide more timely risk analytics. The model can transparently detail how it has arrived at each assumption and lower the energy requirements, or carbon footprint, associated with conducting computationally intensive calculations. Ultimately, this approach can significantly improve execution times, reduce costs, and empower financial institutions to more effectively manage risk. As a scaled platform partner, Nasdaq draws on deep industry experience, technology expertise and cloud managed service services to help 3,500+ banks, brokers, regulators, financial infrastructure operators, and buy-side firms solve their toughest operational challenges while advancing industrywide modernization. About Nasdaq Nasdaq (Nasdaq: NDAQ) is a leading global technology company serving corporate clients, investment managers, banks, brokers, and exchange operators as they navigate and interact with the global capital markets and the broader financial system. We aspire to deliver world-leading platforms that improve the liquidity, transparency, and integrity of the global economy. Our diverse offering of data, analytics, software, exchange capabilities, and client-centric services enables clients to optimize and execute their business vision with confidence. To learn more about the company, technology solutions, and career opportunities, visit us on LinkedIn, on X @Nasdaq, or at www.nasdaq.com. Cautionary Note Regarding Forward-Looking Statements:¯ Information set forth in this press release contains forward-looking statements that involve a number of risks and uncertainties. Nasdaq cautions readers that any forward-looking information is not a guarantee of future performance and that actual results could differ materially from those contained in the forward-looking information. Forward-looking statements can be identified by words such as can, will, and other words and terms of similar meaning. Such forward-looking statements include, but are not limited to, statements related to the benefits of AI within Nasdaq's Calypso solution. Forward-looking statements involve a number of risks, uncertainties or other factors beyond Nasdaq's control. These risks and uncertainties are detailed in Nasdaq's filings with the U.S. Securities and Exchange Commission, including its annual reports on Form 10-K and quarterly reports on Form 10-Q which are available on Nasdaq's investor relations website at http://ir.nasdaq.com and the SEC's website at www.sec.gov. Nasdaq undertakes no obligation to publicly update any forward-looking statement, whether as a result of new information, future events or otherwise.¯

Nasdaq Integrates AI to Simplify and Accelerate Bank and Insurance Risk Calculations - Nasdaq (NASDAQ:NDAQ)

Market volatility and regulatory requirements are driving increasingly complex and computationally intensive risk calculations XVA sensitivity analysis can require over 1 trillion calculations per day, requiring substantial physical infrastructure Nasdaq incorporates AI-based machine learning to process risk calculations up to 100 times faster NEW YORK, Oct. 17, 2024 (GLOBE NEWSWIRE) -- Nasdaq NDAQ today announced it has developed an innovative new methodology to conduct investment portfolio risk calculations and produce predictive analytics, based on advanced machine learning capability. The functionality will be integrated into Nasdaq's Calypso platform, which is used by banks, insurers, and other financial institutions globally to access capital markets, process front-to-back office treasury workflows, manage risk, and meet regulatory reporting obligations. XVA is a family of Value Adjustments made to derivative values to reflect the impact of risk, funding, capital, and other costs associated with trading OTC derivatives. A well-known example is a Credit Valuation Adjustment where changes are made to reflect counterparty credit risk inherent in bilateral transactions. This process has been critical to help banks manage risks since the Global Financial Crisis of 2007-8. Alongside the development of structured products, financial engineering has led to highly complex derivative pricing models, demanding more sophisticated internal risk modelling alongside greater regulatory oversight. Collectively, this has placed a substantial and costly computational requirement on the industry. Nasdaq's machine learning technology is combined with a sophisticated form of mathematical modelling that can significantly improve the efficiency of conducting the most complex trading and regulatory risk calculations. It transforms the time taken to price financial instruments across millions of scenarios, processing the most complex products up to 100 times faster whilst maintaining high levels of accuracy. It can also significantly reduce the amount of physical infrastructure required to run those calculations. Gil Guillaumey, Senior Vice President and Head of Capital Markets Technology at Nasdaq, said: "All financial institutions trading OTC derivatives are required to perform increasingly complex calculations to meet internal risk controls and regulatory mandates. Maintaining the necessary infrastructure and systems can be outrageously expensive, inefficient, and increasingly impractical regardless of cloud elasticity strategies. The sheer scale of computing power required to meet the most demanding regulations, alongside the strategic benefits of more accurate real-time analytics, is driving a profound rethink about how we can leverage AI to reduce the cost of compliance." Industry-wide rise in risk analytics The ability to accurately model risk across asset classes is essential for optimal trading decisions, accurate accounting of risk profiles, and calculating capital requirements. Risk functions within financial institutions are therefore consistently enhancing their own internal framework, while regulators also recognize the benefits of increasing the volume and frequency of risk calculations such as XVA and Value-at-Risk. For example, The Standardized Approach for XVA under Basel III Endgame introduces a more complex and granular series of calculations across firms' trading portfolios. The regulation is aligned to best practice risk management controls, with some banks already performing intraday calculations; however, others do not have the system capability and have to accept the costs charged by their counterparty. Today, a typical Credit Value Adjustment computation involves millions of Monte Carlo simulations over a series of points in time to produce up to 10 billion revaluations. Firms are also increasingly required to run sensitivity analysis on those calculations for risk management purposes, which can result in up to 1 trillion calculations per day for a typical portfolio, requiring a huge amount of computational power and physical infrastructure. Nasdaq's XVA Accelerator Called the XVA Accelerator, Nasdaq's innovative technology uses a mathematical approach known as Chebyshev Tensors, drawing on a patented technique and modelling expertise from MoCaX Intelligence. It incorporates a breakthrough mathematical theorem by Sergei Bernstein that allows users to identify groups of scenarios that are highly likely to converge at an exponential speed toward the target result. This will allow very accurate approximations for an extensive range of scenarios, whilst requiring substantially fewer calculations than the original method. The Chebyshev Tensors in the XVA Accelerator are calibrated in a dynamic manner each time an XVA calculation is launched. As a result, it adapts immediately to changing market conditions, which can prove particularly valuable in moments of market disruption. With this technology, the Nasdaq Calypso risk analytics suite can rapidly adjust during periods of heightened volatility, or fluctuating interest rates, by identifying a smaller number of 'smart' scenarios to provide more timely risk analytics. The model can transparently detail how it has arrived at each assumption and lower the energy requirements, or carbon footprint, associated with conducting computationally intensive calculations. Ultimately, this approach can significantly improve execution times, reduce costs, and empower financial institutions to more effectively manage risk. As a scaled platform partner, Nasdaq draws on deep industry experience, technology expertise and cloud managed service services to help 3,500+ banks, brokers, regulators, financial infrastructure operators, and buy-side firms solve their toughest operational challenges while advancing industrywide modernization. About Nasdaq Nasdaq NDAQ is a leading global technology company serving corporate clients, investment managers, banks, brokers, and exchange operators as they navigate and interact with the global capital markets and the broader financial system. We aspire to deliver world-leading platforms that improve the liquidity, transparency, and integrity of the global economy. Our diverse offering of data, analytics, software, exchange capabilities, and client-centric services enables clients to optimize and execute their business vision with confidence. To learn more about the company, technology solutions, and career opportunities, visit us on LinkedIn, on X @Nasdaq, or at www.nasdaq.com. Andrew Hughes +44 (0)7443 100896 [email protected] Camille Stafford +1 (234) 934 9513 [email protected] Cautionary Note Regarding Forward-Looking Statements: Information set forth in this press release contains forward-looking statements that involve a number of risks and uncertainties. Nasdaq cautions readers that any forward-looking information is not a guarantee of future performance and that actual results could differ materially from those contained in the forward-looking information. Forward-looking statements can be identified by words such as "can", "will", and other words and terms of similar meaning. Such forward-looking statements include, but are not limited to, statements related to the benefits of AI within Nasdaq's Calypso solution. Forward-looking statements involve a number of risks, uncertainties or other factors beyond Nasdaq's control. These risks and uncertainties are detailed in Nasdaq's filings with the U.S. Securities and Exchange Commission, including its annual reports on Form 10-K and quarterly reports on Form 10-Q which are available on Nasdaq's investor relations website at http://ir.nasdaq.com and the SEC's website at www.sec.gov. Nasdaq undertakes no obligation to publicly update any forward-looking statement, whether as a result of new information, future events or otherwise. NDAQG Market News and Data brought to you by Benzinga APIs

Nasdaq Turns to AI to Improve Bank and Insurance Risk Assessment | PYMNTS.com

Nasdaq has introduced technology to calculate investment portfolio risk using machine learning. The new functionality, announced Thursday (Oct. 17), will be woven into Nasdaq's Calypso platform, used by banks, insurers and other financial institutions to do things like access capital markets, manage risk and adhere to regulatory reporting obligations. "Nasdaq's machine learning technology is combined with a sophisticated form of mathematical modeling that can significantly improve the efficiency of conducting the most complex trading and regulatory risk calculations," the organization said in a news release. "It transforms the time taken to price financial instruments across millions of scenarios, processing the most complex products up to 100 times faster whilst maintaining high levels of accuracy. It can also significantly reduce the amount of physical infrastructure required to run those calculations." Gil Guillaumey, who heads Nasdaq's capital markets technology, notes that financial institutions that trade over-the-counter (OTC) derivatives must carry out increasingly complicated calculations to meet internal risk controls and regulatory mandates. "Maintaining the necessary infrastructure and systems can be outrageously expensive, inefficient, and increasingly impractical regardless of cloud elasticity strategies," he said. "The sheer scale of computing power required to meet the most demanding regulations, alongside the strategic benefits of more accurate real-time analytics, is driving a profound rethink about how we can leverage AI to reduce the cost of compliance." The news comes as banks are increasingly turning to artificial intelligence (AI) to streamline compliance and reduce risk, as PYMNTS wrote earlier this week. Industry experts argue this technology can make regulatory testing and monitoring significantly more efficient and accurate. "The regulatory burden and cost to comply is only growing, which leaves banks doing more testing and monitoring with the same amount of resources," Leslie Watson-Stracener, managing director and regulatory compliance capability leader at Grant Thornton Advisors LLC, wrote in a recent blog post. She cautioned that increasing pressure on compliance teams "can lead to stress, burnout and human error." Wes Luckock, senior manager of advisory services at Grant Thornton, predicts a broader impact beyond just regulatory work: "Across the business cycle, AI will be coming into play in an end-to-end manner. It's not just going to be a couple tasks throughout the cycle -- it's going to be the entire cycle." Still, experts warn that human oversight remains critical. Watson-Stracener advises, "Always make sure your board has oversight of your AI practices. And test your results."