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Artificial intelligence for Hedge Funds: How Can Machine Learning and Code Optimisation Generate Greater Alpha?

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By Dr Leslie Kanthan, CEO and Co-Founder of TurinTech

 

The applications of AI in the financial sector are multi-faceted. In this article, we explore some key use cases of AI for hedge funds. We also look at potential challenges in implementing AI-based solutions, and how hedge funds can circumvent these challenges.

Dr Leslie Kanthan

In an analysis done in 2020, consulting and research firm Cerulli claims that there is increasingly strong evidence for hedge funds to use AI technologies. Cerulli’s findings note, “The cumulative return of AI-led hedge funds was almost three times higher than that of the overall hedge fund universe during this [2013 – 2019] period: 33.9% compared to 12.1%.”

AI-driven hedge funds currently use machine learning for tasks such as analysing data, making stock trades and calculating payouts. It is worthwhile discussing some of these applications in detail.

 

Applications of AI in hedge funds

Here are five ways in which we think AI can help hedge funds maximise their trading outcomes:

 

  1. Algorithmic trading: Trading involves considering a range of independent variables, impacting the value of assets and making investment decisions that lead to higher returns. Human traders and traditional computational modeling may not be able to sift through large bodies of information efficiently enough to make timely trading decisions.

With AI-based algorithmic trading, numerous machine learning models can be easily utilised to conduct automatic trading, harnessing new insights which were not attainable before.

AI-based algorithmic trading models also facilitate independent trading with minimal intervention from human traders. For example, based on historical and predicted asset pricing, a model can be developed and trained to make a trade at a given time point.

Compared to traditional trading techniques, this model will process a larger body of data at an expedited rate, making the trading process more accurate and efficient.

 

  1. Volatility forecasting: Market uncertainties make accurate volatility predictions crucial in fund management. Needless to say, a better understanding and prediction of volatility can lead to improved trading decisions and higher returns.

Conventional approaches and econometric modeling can predict volatility, but often they are unable to map complex and nonlinear relationships between factors that contribute to volatility.

However, machine learning-based approaches are able to make much more precise predictions of volatility. Through taking more flexible approaches to understanding variance (an underlying measure of volatility), machine learning models are able to increase the accuracy of volatility predictions.

 

  1. Signal monitoring: Studies have shown that alpha on new trades decays in about 12 months on average. Since trading decisions are made based on predictive relationships and signals, it is essential that funds monitor and retrieve high-quality signals. Signal overcrowding can be particularly concerning, leading to overlapping trading positions and alpha decay.

However, with machine learning-enabled technologies, hedge funds are able to identify diverse and hitherto uncommon signals, allowing them to avoid trading based on overcrowded signals.

Natural Language Processing (NLP), a branch of AI that looks at analysing and deriving insights from large bodies of text data is particularly useful for hedge funds to derive foresight and signals from unstructured textual data from a wide variety of sources such as news, social media, blogs, and transactions.

 

  1. Generating alpha factors: Alpha measures the performance of a fund in comparison to an appropriate benchmark, and is an indicator of the value a fund manager adds or subtracts from a portfolio.

Signals that lead to greater alpha than the returns of the benchmark index are considered alpha factors. Alpha factors are used to explain the behaviour of factors affecting the market and they also capture market risk. Feature engineering, a key component of machine learning, can be used in trading to supplement research into alpha factors.

In AI-based trading, by leveraging feature engineering, factors that better capture the risks embodied by the return drivers are generated from original data and they are manipulated to derive more impactful features. These features become the alpha factors which can then be used to generate greater alpha.

 

  1. Causal inference: There are many factors (features) that can help explain financial phenomena which are of interest. Unfortunately, as is the case most of the time, we do not know which factors are directly affecting (causing) each other. AI-based solutions are able to shed more light on understanding the inter-relationships among features and selecting the most appropriate subset of features relevant for a specific machine learning task. Understanding the causal direction will help hedge fund managers ask more informative questions and construct better trading decisions.

 

Challenges in adopting AI-based solutions

While the use of AI in hedge funds seems rather appealing, it is more so conceptually than in practice.

  • Time-sensitive investment decisions need to be made fast before market conditions change, and this is particularly crucial for hedge funds. However, building a custom AI solution or a machine learning model for a given investment task can be extremely time-consuming. Based on data in the Algorithmia 2020 State of Enterprise Machine Learning report, companies can take from 8 to 90 or more days to deploy a single machine learning model. Hedge funds often do not have the liberty to spend so much time building, evaluating, and deploying a machine learning model.
  • Developing even the most basic version of a machine learning model can be costly, which includes but is not limited to model infrastructure, data support, and engineering costs. You can expect to spend around $60K over the first five years for the model.
  • The model building process would require subject matter experts, but it is often difficult and costly to find and hire AI experts.

 

The bottom line

In order to successfully integrate AI into the investment decision-making process, hedge funds need to explore technology solutions that can function with minimal financial, time, and computational demands. These AI systems should also be able to efficiently generate models that are both explainable and not overfitting or underfitting.

In trading, efficiency is money. It is estimated that a 1-millisecond advantage in trading can be worth $100 million a year. Code optimisation reduces inefficiencies at the source code level, reducing latency and improving model performance by over 50%, ultimately leading to faster and more profitable trading outcomes.

 

Business

Know Your Business (KYB): Exceeding KYC

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Victor Fredung, CEO at Shufti Pro

 

Money laundering costs the UK more than £100 billion pounds a year, according to the National Crime Agency, emphasising the need for stringent ID verification of individuals and businesses.

ID verification, however, remains a moving target. The UK’s fraud prevention community CIFAS has warned of surging ID theft. The National Fraud Database increased by 11% in the first six months of 2021, with almost 180,000 instances of fraudulent conduct filed in the first six months of the year. This reflected the aftermath of the 2008 financial crisis, which recorded a 32% increase in identity fraud the following year. CIFAS is warning UK businesses and consumers to expect a continuation of the steep rise in identity fraud for 2021 and 2022 as criminals exploit businesses under pressure.

Businesses can respond with resilient Know Your Customer (KYC) software and protocols. KYC establishes customer identity; understands customers’ activities; qualifies the legitimacy of funding sources; and assesses money laundering risks associated with customers. To date, almost 6,000 financial institutions are using the SWIFT KYC Registry to publish their KYC data and receive data from their correspondent banks.

KYC regulations and procedures are appropriate when the customer or consumer is a named individual.  However, it’s not enough to verify the identity of individuals. It is also important to verify the identity of businesses.  Know Your Business (KYB) tools and regulations are designed for cases where the customer is a business or corporate entity. KYB is particularly important as criminals seek to exploit crypto currencies which can thwart verification techniques, such as anti-money laundering (AML) and KYC.

KYB verifies businesses by obtaining official commercial register data via APIs. By using the registration numbers and jurisdiction code of a business, a digital KYB service can collect confirmable information for the business. This enables corporate organisations to determine if they are dealing with authentic businesses or fake shell companies. KYB services particularly help financial institutions handling the funds of a large customer base and corporate entities.  During this process businesses must improve the customer digital enrolment and authentication experience. End-users resist proving their identity through for example, showing scans of their bank account statements and may abandon service providers whose online enrolment processes increase friction.

Usefully, KYB uses access to automated commercial registers through a data-powered business verification service, expedites due diligence and eliminates errors.  With advances in digital technologies and virtual data sets, KYB compliance and verification tools can mark businesses involved in undercover activities, gathering background data on the company including the registered address, status, company type, ultimate beneficial ownership structures, previous names and trademark registration. A financial summary of the company’s operational accounts is also provided by the authentication service, to help validate its authenticity.

Here, Artificial Intelligence (AI) can come into its own, determining the identity of individuals and the financial risk attached to that person with AML Compliance solutions. AML services can check the involvement of an individual company in any watchlist or financial risk database, at scale. Machine learning algorithms can detect forged documents or disguised ownership structures. Nationality verification and geolocation targeting can determine the true country of origin of international clients and the jurisdiction of the company.

However, adoption of KYB processes has been sluggish: last year research undertaken by kompany indicated only 5% of financial institutions (FIs) have an automated B2B or corporate banking onboarding process, with 75% of FIs still relying on Google searches to identify Ultimate Beneficial Owners (UBOs), annual filings and financial accounts. Financial services organisations also struggle to manage the complexity of KYB, and the siloed approach to managing information within an FI can make KYB adoption more challenging.

A further challenge for KYB compliance lies in accessing beneficial ownership information, especially in jurisdictions that do not require companies to submit relevant documentation. A lack of shareholder information makes it harder to investigate money trails and business authenticity. Timely availability of data, across international borders in the right format, is another hindrance, especially as company structures and management change over time. This is why geography and industry specific vendors will be of value to businesses needing to conduct ID checks. It is also why businesses must find the right vendors who can be a one stop shop to manage their KYB adoption and must prioritise the user-experience for frictionless onboarding and regulatory compliance.

Banks have experienced difficulties with KYC verification for their customer onboarding, transaction authentication, and remote banking services. This why they may find it hard to trust a KYB service provider. However, FIs and businesses face a pressing need to determine the ultimate beneficial ownership structure of the corporations they are dealing with. The need for a credible, cross-border KYB provider has rarely been more pressing, and according to Forrester, Know-your-business IDV will ‘make or break Identity Verification players.

Know-your-business IDV can make critical difference in identity verification.  With the increase in B2B commerce it has become more urgent to verify both individuals and organisations and their representatives.

The cost of not adopting KYB technology is dwarfed by the prospect of data breaches, fraud and reputational damage. For financial institutions, legitimacy and verification of the business is key for growth. The software solutions exist and are ready to be implemented.  he National Fraud Database increased by 11% in the first six months of 2021, with almost 180,000 instances of fraudulent conduct filed in the first six months of the year. This reflected the aftermath of the 2008 financial crisis, which recorded a 32% increase in identity fraud the following year. CIFAS is warning UK businesses and consumers to expect a continuation of the steep rise in identity fraud for 2021 and 2022 as criminals exploit businesses under pressure.

Businesses can respond with resilient Know Your Customer (KYC) software and protocols. KYC establishes customer identity; understands customers’ activities; qualifies the legitimacy of funding sources; and assesses money laundering risks associated with customers. To date, almost 6,000 financial institutions are using the SWIFT KYC Registry to publish their KYC data and receive data from their correspondent banks.

KYC regulations and procedures are appropriate when the customer or consumer is a named individual.  However, it’s not enough to verify the identity of individuals. It is also important to verify the identity of businesses.  Know Your Business (KYB) tools and regulations are designed for cases where the customer is a business or corporate entity. KYB is particularly important as criminals seek to exploit crypto currencies which can thwart verification techniques, such as anti-money laundering (AML) and KYC.

KYB verifies businesses by obtaining official commercial register data via APIs. By using the registration numbers and jurisdiction code of a business, a digital KYB service can collect confirmable information for the business. This enables corporate organisations to determine if they are dealing with authentic businesses or fake shell companies. KYB services particularly help financial institutions handling the funds of a large customer base and corporate entities.  During this process businesses must improve the customer digital enrolment and authentication experience. End-users resist proving their identity through for example, showing scans of their bank account statements and may abandon service providers whose online enrolment processes increase friction.

Usefully, KYB uses access to automated commercial registers through a data-powered business verification service, expedites due diligence and eliminates errors.  With advances in digital technologies and virtual data sets, KYB compliance and verification tools can mark businesses involved in undercover activities, gathering background data on the company including the registered address, status, company type, ultimate beneficial ownership structures, previous names and trademark registration. A financial summary of the company’s operational accounts is also provided by the authentication service, to help validate its authenticity.

Here, Artificial Intelligence (AI) can come into its own, determining the identity of individuals and the financial risk attached to that person with AML Compliance solutions. AML services can check the involvement of an individual company in any watchlist or financial risk database, at scale. Machine learning algorithms can detect forged documents or disguised ownership structures. Nationality verification and geolocation targeting can determine the true country of origin of international clients and the off shore status of a company.

However, adoption of KYB processes has been sluggish: last year research undertaken by kompany indicated only 5% of financial institutions (FIs) have an automated B2B or corporate banking onboarding process, with 75% of FIs still relying on Google searches to identify Ultimate Beneficial Owners (UBOs), annual filings and financial accounts. Financial services organisations also struggle to manage the complexity of KYB, and the siloed approach to managing information within an FI can make KYB adoption more challenging.

A further challenge for KYB compliance lies in accessing beneficial ownership information, especially in jurisdictions that do not require companies to submit relevant documentation. A lack of shareholder information makes it harder to investigate money trails and business authenticity. Timely availability of data, across international borders in the right format, is another hindrance, especially as company structures and management change over time. This is why geography and industry specific vendors will be of value to businesses needing to conduct ID checks. It is also why businesses must find the right vendors who can be a one stop shop to manage their KYB adoption and must prioritise the user-experience for frictionless onboarding and regulatory compliance.

Banks have experienced difficulties with KYC verification for their customer onboarding, transaction authentication, and remote banking services. This why they may find it hard to trust a KYB service provider. However, FIs and businesses face a pressing need to determine the ultimate beneficial ownership structure of the corporations they are dealing with. The need for a credible, cross-border KYB provider has rarely been more pressing, and according to Forrester, Know-your-business IDV will ‘make or break Identity Verification players.

Know-your-business IDV can make critical difference in identity verification.  With the increase in B2B commerce it has become more urgent to verify both individuals and organisations and their representatives.

The cost of not adopting KYB technology is dwarfed by the prospect of data breaches, fraud and reputational damage. For financial institutions, legitimacy and verification of the business is key for growth. The software solutions exist and are ready to be implemented.

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Addressing the ongoing global pilot shortage issue

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By Bhanu Choudhrie, Founder of Alpha Aviation

 

The Covid-19 pandemic brought the aviation industry to a halt, causing vast market disruption and putting the future of many key players at risk. Now, just as airlines were getting back on track, staffing shortages are causing new complications – and part of this issue is a growing pilot recruitment problem.

So, where does the sector go from here and what steps need to be taken to mitigate pilot shortages?

The root of the issue

Even before the pandemic, there was a global shortage of pilots, with people flying more due to a rise in more affordable airlines and falling fuel costs. In fact, the 2020-2029 CAE Pilot Demand Outlook suggested that the global civil aviation industry will require more than 260,000 pilots by the end of the decade.

However, when demand for air travel dropped across the globe, airlines were quick to offer early retirement packages to reduce immediate outgoings. Whilst this approach helped some airlines stay afloat during the slowdown, a wave of early retirements has left them on the back foot.

Bhanu Choudhrie

Now demand is coming back much faster than expected. In the US alone, the Bureau of Labor Statistics is expecting 14,500 openings for commercial and airline pilots each year until 2030, and this imbalance is already having a detrimental impact on the aviation industry. With flights being cancelled, travellers left stranded, and some airports losing service altogether, it is crucial that the larger aviation ecosystem comes together to work out a solution to effectively address this pilot shortage crisis, so that it can once again meet capacity demands.

Re-directing efforts to rebuild pilot pools

With vast swathes of pilots put on furlough during the pandemic – and therefore unable to maintain their license requirements, the damage isn’t just in the ongoing pilot shortage, but also in the decades of experience the industry has lost. In response to this narrative, last month a Senator in the US introduced legislation to raise the mandatory retirement age of commercial airline pilots from 65 to 67 – and the US are not alone in this shift. Last week, Air India announced that it will be raising their retirement age for pilots from 58 to 65. Now we need to see other countries and airlines follow suit to help retain the talent that can help guide and mentor the next generation of cadets.

Moreover, training schools and airlines will need to work together to challenge industry stereotypes and empower more women to pursue a career in the cockpit. Currently, just 5.1 per cent of the world’s commercial pilots are women. This means that for every twenty flights taken, only one of them will be piloted by a woman. Unfortunately, this gender imbalance has become a long-established trend within the aviation industry and, stereotypically, pursuing a career as a pilot has been considered a male occupation, with women type cast to cabin crew instead. Therefore, if we are to make proactive strides towards addressing the current pilot shortfall, finding a way to shift that percentage is essential.

The cost of training to be a pilot is also a key barrier the industry needs to address, and at pace. On average, the cost to train as an air transport pilot can exceed $100,000 – making a career in the cockpit unattainable to many. One way for the industry to help narrow the gap and mitigate what is often seen as a considerable financial risk, is to make bursaries more accessible. There are already a number of programmes in place, to support both aspiring pilots and those who need to maintain their licenses, however, now the industry needs to work on championing and expanding these support systems.

The industry also needs to start to embrace alternative approaches to alleviate this substantial outlay. For example, at Alpha Aviation, we have started running the the Multi-Crew Pilot License (MPL). This is a shorter, more simulator-focused way of training that not only opens up opportunities for prospective cadets from less privileged backgrounds, but also offers a more flexible training programme and quicker route to qualification – reducing the financial expenses for cadets to cover.

Technological innovations can also play a crucial role in advancing the training process to help support a consistent employee base. For example, e-learning programmes can enable airlines to expand cadet class sizes. No longer restricted by the physical capacity of training centres, e-learning programmes have the potential to significantly open up access to becoming an aviator and will ensure airlines can recruit the best talent, irrespective of locality. In addition to this, pilots still need to clock up over 1,500 flying hours to receive their ATP certificate. Therefore, investing in simulator training facilities is now pivotal in supporting cadets to keep on top of the legal requirements and improve their skills set at a significantly quicker pace, alongside supporting existing pilots to retrain on new aircrafts when necessary.

Looking ahead

The pressure on the aviation industry shows no signs of abating any time soon. Therefore, while it is great to see passenger numbers returning to near pre-pandemic levels, the industry needs to take this as a significant wakeup call and re-assess its pilot recruitment process.

At the end of the day, there is no quick fix – training top of their class pilots takes time, investment and enthusiasm. However, addressing the ongoing chaos and driving the sector out of this turbulent period is essential to the economic revival of the nation. Therefore, decisive action is needed – and it is needed now.

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