Can recent technological advances in artificial intelligence be used improve your risk management processes?
This article was written by Ashley Aberneithy, CEO of Analytic AI.
It’s commonplace for risk managers to group their portfolio into risk books. The process of isolating risk within their portfolio involves risk managers creating distinct groups for different types of trader. The risk manager might place large size traders into one group, whereas high frequency traders may go into another group.
The risk manager then hedges each group independently, i.e. the high frequency traders might be best served by an automated hedging process (STP) whereas certain traders might just be shut down or flagged as fraudulent. How accurately these groupings reflect the characteristics of the firm's portfolio directly impacts a firm's bottom line and financial institutions dedicate a large amount of time into analyzing traders and building these classification groupings.
Has a trader exhibited certain characteristics within their past trading behavior?
Many risk managers ask the question ‘has a trader exhibited certain characteristics within their past trading behavior that can be used to predict the best classification for them going forwards?’ but the main problem with this approach is that (a) the accuracy of this method is questionable (b) this analysis is very time consuming and (c) due to the large number of inputs the analysis highly subjective and prone to error.
In many cases risk managers might just not even have enough data about the trader to make these decisions.
Artificial intelligence solutions aim to ‘quantitatively assess’ common heuristics and behavioural traits which exist amongst financial market traders
Machine learning and AI based algorithms are more effective at modeling risk groupings compared with their human counterparts, however they are not a silver bullet; these algorithms are extremely complex to build and it’s common for them to work on historical data, but fail when faced with new data. Building these solutions also requires specialist skillsets in machine learning and quantitative Risk Management; skills, which are in short supply and high, demand.
When I speak to firms it’s not uncommon for them to complain about spending months, even years, with varying degrees of success, researching these automated solutions for optimizing their risk groups. There is hope however! We are starting to see an emergence of cost effective artificial intelligence within the financial space and some specialist vendors providing both out-of-the-box solutions as well as more customized solutions to fit the needs of specific firms.
How do you know using this type of automated solution is right for your firm? Many of these firms offer backtesting solutions which allow firms to forecast how these algorithms can perform on your data going forwards. The numbers provided might be enough for you decide to make the switch, or at least augment your existing classification process with an AI based solution.
The other main driver is Scalability, if you need a solution which can scale no matter the trade volumes, 24 hours a day, then moving to an algorithmic solution could be a good solution for your firm.
This article was written by Ashley Aberneithy, CEO of Analytic AI.
It’s commonplace for risk managers to group their portfolio into risk books. The process of isolating risk within their portfolio involves risk managers creating distinct groups for different types of trader. The risk manager might place large size traders into one group, whereas high frequency traders may go into another group.
The risk manager then hedges each group independently, i.e. the high frequency traders might be best served by an automated hedging process (STP) whereas certain traders might just be shut down or flagged as fraudulent. How accurately these groupings reflect the characteristics of the firm's portfolio directly impacts a firm's bottom line and financial institutions dedicate a large amount of time into analyzing traders and building these classification groupings.
Has a trader exhibited certain characteristics within their past trading behavior?
Many risk managers ask the question ‘has a trader exhibited certain characteristics within their past trading behavior that can be used to predict the best classification for them going forwards?’ but the main problem with this approach is that (a) the accuracy of this method is questionable (b) this analysis is very time consuming and (c) due to the large number of inputs the analysis highly subjective and prone to error.
In many cases risk managers might just not even have enough data about the trader to make these decisions.
Artificial intelligence solutions aim to ‘quantitatively assess’ common heuristics and behavioural traits which exist amongst financial market traders
Machine learning and AI based algorithms are more effective at modeling risk groupings compared with their human counterparts, however they are not a silver bullet; these algorithms are extremely complex to build and it’s common for them to work on historical data, but fail when faced with new data. Building these solutions also requires specialist skillsets in machine learning and quantitative Risk Management; skills, which are in short supply and high, demand.
When I speak to firms it’s not uncommon for them to complain about spending months, even years, with varying degrees of success, researching these automated solutions for optimizing their risk groups. There is hope however! We are starting to see an emergence of cost effective artificial intelligence within the financial space and some specialist vendors providing both out-of-the-box solutions as well as more customized solutions to fit the needs of specific firms.
How do you know using this type of automated solution is right for your firm? Many of these firms offer backtesting solutions which allow firms to forecast how these algorithms can perform on your data going forwards. The numbers provided might be enough for you decide to make the switch, or at least augment your existing classification process with an AI based solution.
The other main driver is Scalability, if you need a solution which can scale no matter the trade volumes, 24 hours a day, then moving to an algorithmic solution could be a good solution for your firm.
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FMLS:24 | Shaping the Next Era of Financial Evolution
FMLS:24 | Shaping the Next Era of Financial Evolution
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Welcome to FMLS:24 – the premier event where influential brands and leaders in trading, payments, fintech, and digital assets come together!
Join over 2,500 industry professionals, engage with 150+ expert speakers, and discover endless opportunities with 70+ top exhibitors. FMLS:24 is where senior executives and decision-makers gather to close deals, forge new partnerships, and strengthen connections with long-term clients.
Whether you’re in finance, technology, or payments, this summit is your gateway to future growth, meaningful collaborations, and industry-leading insights.
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FMLS:24 | Shaping the Next Era of Financial Evolution
FMLS:24 | Shaping the Next Era of Financial Evolution
Welcome to FMLS:24 – the premier event where influential brands and leaders in trading, payments, fintech, and digital assets come together!
Join over 2,500 industry professionals, engage with 150+ expert speakers, and discover endless opportunities with 70+ top exhibitors. FMLS:24 is where senior executives and decision-makers gather to close deals, forge new partnerships, and strengthen connections with long-term clients.
Whether you’re in finance, technology, or payments, this summit is your gateway to future growth, meaningful collaborations, and industry-leading insights.
👉 Don't miss out – secure your ticket now at https://events.financemagnates.com/ZQEYy0?utm_source=youtube&utm_campaign=fmls24-awareness&utm_medium=video&RefId=MLS%3A24+Video+Promo
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Welcome to FMLS:24 – the premier event where influential brands and leaders in trading, payments, fintech, and digital assets come together!
Join over 2,500 industry professionals, engage with 150+ expert speakers, and discover endless opportunities with 70+ top exhibitors. FMLS:24 is where senior executives and decision-makers gather to close deals, forge new partnerships, and strengthen connections with long-term clients.
Whether you’re in finance, technology, or payments, this summit is your gateway to future growth, meaningful collaborations, and industry-leading insights.
👉 Don't miss out – secure your ticket now at https://events.financemagnates.com/ZQEYy0?utm_source=youtube&utm_campaign=fmls24-awareness&utm_medium=video&RefId=MLS%3A24+Video+Promo
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FM's Andrea Badiola Mateos at LSEG's Cyprus event
FM's Andrea Badiola Mateos at LSEG's Cyprus event
FM's Andrea Badiola Mateos at speaking in a panel discussion at LSEG's Cyprus event
FM's Andrea Badiola Mateos at speaking in a panel discussion at LSEG's Cyprus event
The Role of PAMM, MAM & Copy Trading in Business Growth Strategies | Webinar
The Role of PAMM, MAM & Copy Trading in Business Growth Strategies | Webinar
The copy trading market is projected to double in size, growing from $2.2 billion to $4 billion by the end of this decade. In light of this, brokers and financial institutions are increasingly adopting PAMM, MAM, and Copy Trading solutions to scale operations and drive profitability. In this insightful webinar, Sergey Ryzhavin, Product Owner at B2COPY, outlines the advanced features of the B2COPY platform, showcasing how it enhances Copy Trading, PAMM, and MAM performance. Sergey also explores strategies for using these tools to attract new clients, improve customer engagement, and create additional revenue streams.
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The copy trading market is projected to double in size, growing from $2.2 billion to $4 billion by the end of this decade. In light of this, brokers and financial institutions are increasingly adopting PAMM, MAM, and Copy Trading solutions to scale operations and drive profitability. In this insightful webinar, Sergey Ryzhavin, Product Owner at B2COPY, outlines the advanced features of the B2COPY platform, showcasing how it enhances Copy Trading, PAMM, and MAM performance. Sergey also explores strategies for using these tools to attract new clients, improve customer engagement, and create additional revenue streams.
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Join us at FMLS:24 to connect with global institutional brokers. Secure your spot today! #fmls24
Join us at FMLS:24 to connect with global institutional brokers. Secure your spot today! #fmls24