BNP Paribas Launches New AI System for Enhanced Trade Processing Services
- Smart Chaser will reduce the incidences of manual intervention in the trading process.
BNP Paribas Securities Services has launched 'Smart Chaser', a new artificial intelligence tool to boost its trade processing services and increase efficiency in the trading system. Smart Chaser is a trade matching and predictive analysis tool that identifies trades which may require manual intervention, based on historical data.
Trade matching is a comparison of trade details between client, broker, and middle office. The matching must be performed in a timely manner or a trade will require manual intervention.
The new technology will predict the likelihood of delayed trade matching. It will ascertain the cause of the delay and suggest a pre-designed email template to be sent to clients by the middle office operation team. This process will further automate middle and back office services.
Thomas Durif, Global Head of Middle Office Products, said: “We estimate that up to 30% of the trades processed on behalf of asset managers require manual intervention in order to complete. This is an industry-wide challenge which is often caused by counterparties holding mismatching data for the same trade.
Using predictive analysis, Smart Chaser will analyse historical data to identify patterns in trades that have required manual intervention in the past and proactively warn clients and their brokers on their live trading activity so they can take action promptly. We are already making good progress, having reached around 98% prediction accuracy."
Global Head of Investment Services and Analytics Analytics Analytics may be defined as the detection, analysis, and relay of consequential patterns in data. Analytics also seeks to explain or accurately reflect the relationship between data and effective decision making. In the trading space, analytics are applied in a predictive manner in an attempt to more accurately forecast the price. This predictive model of analytics generally involves the analysis of historical price patterns that are used in an attempt to determine certain price outcomes. Analytics may also be structured with a descriptive model, where readers attempt to draw a correlation and better understanding as to how and why traders react to a particular set of variables. Traders sometimes implement technical indicators such as moving averages, Bollinger Bands, and breakpoints which are built upon historical data and are used to predict future price movements. How Analytics Relates to Algo TradingAnalytics are relied upon in the concept of algorithmic trading where software is programmed to autonomously signal and/or execute buy and sell orders based upon a series of predetermined factors. In the institutional space, Algo-trading has become vastly competitive over the years as trading institutions seek to outperform competitors through automated systems and the virtual application of trading strategies.The digestion and computation of analytics are also seen in the emerging field of high-frequency trading, where supercomputers are used to analyze multiple markets simultaneously to make near-instantaneous automated trading decisions. Platforms that support HFT have the capability to significantly outperform human traders.This is due to the innate ability to be able to comprehensively analyze big data sets while taking under do consideration an innumerable sum of factors that humans are incapable of comprehending in such speed. Additionally, analytics are seen with backtesting. Backtesting is used by traders to test the consistency and effectiveness of trading strategies and software-based trading solutions against historical price data. Backtesting also serves as an ideal playground for the further development of high-frequency trading as well as evaluating the performance of manual or automated trades. Analytics will continue to have an increasingly significant role in trading as emerging technologies and the advancement of trading applications progress beyond human capability. Analytics may be defined as the detection, analysis, and relay of consequential patterns in data. Analytics also seeks to explain or accurately reflect the relationship between data and effective decision making. In the trading space, analytics are applied in a predictive manner in an attempt to more accurately forecast the price. This predictive model of analytics generally involves the analysis of historical price patterns that are used in an attempt to determine certain price outcomes. Analytics may also be structured with a descriptive model, where readers attempt to draw a correlation and better understanding as to how and why traders react to a particular set of variables. Traders sometimes implement technical indicators such as moving averages, Bollinger Bands, and breakpoints which are built upon historical data and are used to predict future price movements. How Analytics Relates to Algo TradingAnalytics are relied upon in the concept of algorithmic trading where software is programmed to autonomously signal and/or execute buy and sell orders based upon a series of predetermined factors. In the institutional space, Algo-trading has become vastly competitive over the years as trading institutions seek to outperform competitors through automated systems and the virtual application of trading strategies.The digestion and computation of analytics are also seen in the emerging field of high-frequency trading, where supercomputers are used to analyze multiple markets simultaneously to make near-instantaneous automated trading decisions. Platforms that support HFT have the capability to significantly outperform human traders.This is due to the innate ability to be able to comprehensively analyze big data sets while taking under do consideration an innumerable sum of factors that humans are incapable of comprehending in such speed. Additionally, analytics are seen with backtesting. Backtesting is used by traders to test the consistency and effectiveness of trading strategies and software-based trading solutions against historical price data. Backtesting also serves as an ideal playground for the further development of high-frequency trading as well as evaluating the performance of manual or automated trades. Analytics will continue to have an increasingly significant role in trading as emerging technologies and the advancement of trading applications progress beyond human capability. Read this Term, Maxime Boyer-Chammard, added: "We are delighted to be working on this new tool. Smart Chaser is a great example of how we aim to transform our middle office technology and make straight through processing a reality thanks to Big Data Big Data Big data refers to the collection of data that is too complex and too large for processing by standard database tools. There is no specific quantity of data, which is set as a minimum level to be considered Big data. Image the data collected on global credit card transactions. Many governments used Big data analysis to study the recent pandemic spread. The term Big data was first introduced in 1980 by Charles Tilly.The term Big data was primarily used in computer science, statistics, and econometrics and was made famous in Silicon Valley in the mid-1990s. What Big Data Can Do for YouBig data is the massive amount of data collected over time that are difficult to analyze and handle because the data sets are so enormous. The records are analyzed for marketing trends in business as well as in the fields of manufacturing, medicine, and science. The types of data include business transactions, e-mail messages, photos, surveillance videos, activity logs, and unstructured text from blogs and social media, as well as the vast amounts of data that can be collected from sensors of all varieties. Big data can also refer to the analytical challenge in deriving meaningful information from data in petabyte and exabyte volumes. For example, big data analytics breaks down the data sets into smaller chunks for efficient processing and employs parallel computing to derive intelligence for effective decision-making.Big data is used in a wide range of industries, sectors, or applications. This includes benefits for governments, healthcare, finance, education, media, internet of things (IoT), information technology, and others. Big data refers to the collection of data that is too complex and too large for processing by standard database tools. There is no specific quantity of data, which is set as a minimum level to be considered Big data. Image the data collected on global credit card transactions. Many governments used Big data analysis to study the recent pandemic spread. The term Big data was first introduced in 1980 by Charles Tilly.The term Big data was primarily used in computer science, statistics, and econometrics and was made famous in Silicon Valley in the mid-1990s. What Big Data Can Do for YouBig data is the massive amount of data collected over time that are difficult to analyze and handle because the data sets are so enormous. The records are analyzed for marketing trends in business as well as in the fields of manufacturing, medicine, and science. The types of data include business transactions, e-mail messages, photos, surveillance videos, activity logs, and unstructured text from blogs and social media, as well as the vast amounts of data that can be collected from sensors of all varieties. Big data can also refer to the analytical challenge in deriving meaningful information from data in petabyte and exabyte volumes. For example, big data analytics breaks down the data sets into smaller chunks for efficient processing and employs parallel computing to derive intelligence for effective decision-making.Big data is used in a wide range of industries, sectors, or applications. This includes benefits for governments, healthcare, finance, education, media, internet of things (IoT), information technology, and others. Read this Term and predictive analytics, to the ultimate benefit of our clients."
Artificial intelligence is taking a central place in the investment and financial world and BNP Paribas is investing big in developing its system around this new technology. Recently, its asset management division took a majority stake in Belgian robo-adviser firm Gambit, which provides automated advice solutions to retail= and wealth management networks.
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BNP Paribas Securities Services has launched 'Smart Chaser', a new artificial intelligence tool to boost its trade processing services and increase efficiency in the trading system. Smart Chaser is a trade matching and predictive analysis tool that identifies trades which may require manual intervention, based on historical data.
Trade matching is a comparison of trade details between client, broker, and middle office. The matching must be performed in a timely manner or a trade will require manual intervention.
The new technology will predict the likelihood of delayed trade matching. It will ascertain the cause of the delay and suggest a pre-designed email template to be sent to clients by the middle office operation team. This process will further automate middle and back office services.
Thomas Durif, Global Head of Middle Office Products, said: “We estimate that up to 30% of the trades processed on behalf of asset managers require manual intervention in order to complete. This is an industry-wide challenge which is often caused by counterparties holding mismatching data for the same trade.
Using predictive analysis, Smart Chaser will analyse historical data to identify patterns in trades that have required manual intervention in the past and proactively warn clients and their brokers on their live trading activity so they can take action promptly. We are already making good progress, having reached around 98% prediction accuracy."
Global Head of Investment Services and Analytics Analytics Analytics may be defined as the detection, analysis, and relay of consequential patterns in data. Analytics also seeks to explain or accurately reflect the relationship between data and effective decision making. In the trading space, analytics are applied in a predictive manner in an attempt to more accurately forecast the price. This predictive model of analytics generally involves the analysis of historical price patterns that are used in an attempt to determine certain price outcomes. Analytics may also be structured with a descriptive model, where readers attempt to draw a correlation and better understanding as to how and why traders react to a particular set of variables. Traders sometimes implement technical indicators such as moving averages, Bollinger Bands, and breakpoints which are built upon historical data and are used to predict future price movements. How Analytics Relates to Algo TradingAnalytics are relied upon in the concept of algorithmic trading where software is programmed to autonomously signal and/or execute buy and sell orders based upon a series of predetermined factors. In the institutional space, Algo-trading has become vastly competitive over the years as trading institutions seek to outperform competitors through automated systems and the virtual application of trading strategies.The digestion and computation of analytics are also seen in the emerging field of high-frequency trading, where supercomputers are used to analyze multiple markets simultaneously to make near-instantaneous automated trading decisions. Platforms that support HFT have the capability to significantly outperform human traders.This is due to the innate ability to be able to comprehensively analyze big data sets while taking under do consideration an innumerable sum of factors that humans are incapable of comprehending in such speed. Additionally, analytics are seen with backtesting. Backtesting is used by traders to test the consistency and effectiveness of trading strategies and software-based trading solutions against historical price data. Backtesting also serves as an ideal playground for the further development of high-frequency trading as well as evaluating the performance of manual or automated trades. Analytics will continue to have an increasingly significant role in trading as emerging technologies and the advancement of trading applications progress beyond human capability. Analytics may be defined as the detection, analysis, and relay of consequential patterns in data. Analytics also seeks to explain or accurately reflect the relationship between data and effective decision making. In the trading space, analytics are applied in a predictive manner in an attempt to more accurately forecast the price. This predictive model of analytics generally involves the analysis of historical price patterns that are used in an attempt to determine certain price outcomes. Analytics may also be structured with a descriptive model, where readers attempt to draw a correlation and better understanding as to how and why traders react to a particular set of variables. Traders sometimes implement technical indicators such as moving averages, Bollinger Bands, and breakpoints which are built upon historical data and are used to predict future price movements. How Analytics Relates to Algo TradingAnalytics are relied upon in the concept of algorithmic trading where software is programmed to autonomously signal and/or execute buy and sell orders based upon a series of predetermined factors. In the institutional space, Algo-trading has become vastly competitive over the years as trading institutions seek to outperform competitors through automated systems and the virtual application of trading strategies.The digestion and computation of analytics are also seen in the emerging field of high-frequency trading, where supercomputers are used to analyze multiple markets simultaneously to make near-instantaneous automated trading decisions. Platforms that support HFT have the capability to significantly outperform human traders.This is due to the innate ability to be able to comprehensively analyze big data sets while taking under do consideration an innumerable sum of factors that humans are incapable of comprehending in such speed. Additionally, analytics are seen with backtesting. Backtesting is used by traders to test the consistency and effectiveness of trading strategies and software-based trading solutions against historical price data. Backtesting also serves as an ideal playground for the further development of high-frequency trading as well as evaluating the performance of manual or automated trades. Analytics will continue to have an increasingly significant role in trading as emerging technologies and the advancement of trading applications progress beyond human capability. Read this Term, Maxime Boyer-Chammard, added: "We are delighted to be working on this new tool. Smart Chaser is a great example of how we aim to transform our middle office technology and make straight through processing a reality thanks to Big Data Big Data Big data refers to the collection of data that is too complex and too large for processing by standard database tools. There is no specific quantity of data, which is set as a minimum level to be considered Big data. Image the data collected on global credit card transactions. Many governments used Big data analysis to study the recent pandemic spread. The term Big data was first introduced in 1980 by Charles Tilly.The term Big data was primarily used in computer science, statistics, and econometrics and was made famous in Silicon Valley in the mid-1990s. What Big Data Can Do for YouBig data is the massive amount of data collected over time that are difficult to analyze and handle because the data sets are so enormous. The records are analyzed for marketing trends in business as well as in the fields of manufacturing, medicine, and science. The types of data include business transactions, e-mail messages, photos, surveillance videos, activity logs, and unstructured text from blogs and social media, as well as the vast amounts of data that can be collected from sensors of all varieties. Big data can also refer to the analytical challenge in deriving meaningful information from data in petabyte and exabyte volumes. For example, big data analytics breaks down the data sets into smaller chunks for efficient processing and employs parallel computing to derive intelligence for effective decision-making.Big data is used in a wide range of industries, sectors, or applications. This includes benefits for governments, healthcare, finance, education, media, internet of things (IoT), information technology, and others. Big data refers to the collection of data that is too complex and too large for processing by standard database tools. There is no specific quantity of data, which is set as a minimum level to be considered Big data. Image the data collected on global credit card transactions. Many governments used Big data analysis to study the recent pandemic spread. The term Big data was first introduced in 1980 by Charles Tilly.The term Big data was primarily used in computer science, statistics, and econometrics and was made famous in Silicon Valley in the mid-1990s. What Big Data Can Do for YouBig data is the massive amount of data collected over time that are difficult to analyze and handle because the data sets are so enormous. The records are analyzed for marketing trends in business as well as in the fields of manufacturing, medicine, and science. The types of data include business transactions, e-mail messages, photos, surveillance videos, activity logs, and unstructured text from blogs and social media, as well as the vast amounts of data that can be collected from sensors of all varieties. Big data can also refer to the analytical challenge in deriving meaningful information from data in petabyte and exabyte volumes. For example, big data analytics breaks down the data sets into smaller chunks for efficient processing and employs parallel computing to derive intelligence for effective decision-making.Big data is used in a wide range of industries, sectors, or applications. This includes benefits for governments, healthcare, finance, education, media, internet of things (IoT), information technology, and others. Read this Term and predictive analytics, to the ultimate benefit of our clients."
Artificial intelligence is taking a central place in the investment and financial world and BNP Paribas is investing big in developing its system around this new technology. Recently, its asset management division took a majority stake in Belgian robo-adviser firm Gambit, which provides automated advice solutions to retail= and wealth management networks.
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