The international study among senior leaders and senior compliance and risk executives at alternative fund manager firms which collectively manage around USD132.25 billion AUM, found that of those who already use AI as part of its risk and compliance procedures one in 10 (11 per cent) started doing so more than two years ago. Over half (55 per cent) started two years ago, and 24 per cent started between one and two years ago. 

Ocorian’s global study reveals that out of those who don’t already, 71 per cent say that they intend to start using AI within its risk and compliance functions within the next six months. 

There are a number of areas in which alternative fund managers think AI can be used to enhance risk and compliance procedures. The top area given was in transaction monitoring (28 per cent), followed by staff filings (21 per cent) and internal capital and liquidity monitoring (20 per cent). AI can also be used to monitor communications (19 per cent) and financial promotions (13 per cent).

Joe French, Managing Director and Head of Financial Crime at Ocorian, says: “AI is revolutionising almost every aspect of financial services, and our survey results show that the majority of alternative fund managers have already been using AI within their compliance and risk procedures for around two years. When used for the right type of tasks, AI can transform the ability of over-stretched, under-resourced compliance teams. 

“AI promises to create transformational changes in our industry but human input is going remain critical – take algorithmic trading; the FCA require humans to intervene in machine processes for vital checks and to stop runaway errors potentially taking down global markets. 

“The potential dangers are industry shattering. And thinking of the risks, just as the industry incorporates the new technology, so do criminals. Bad actors are using technology including AI to target consumers and firms. In recent years they have been able to circumvent banking controls by using sophisticated social engineering techniques to trick victims, making detection much more challenging. 

“Firms must ensure that systems and controls keep up with the increasing sophistication of criminal groups and should use the advances in technologies to help prevent financial crime. They have to calibrate their use of technology to individual requirements to be as effective as possible and keep fine tuning their response to combat the rapidly evolving threat.”

Hilton Goudriaan, Head of Systems, Regulatory & Compliance at Ocorian, says: “AI tools can process and analyse large datasets, which can assist with aiding alternative fund managers in meeting reporting standards, analysing market trends, portfolio risks, and identifying potential compliance issues. AI can be developed to streamline due diligence processes. Automation tools, such as our comprehensive compliance solution, the Gateway, go the extra mile and support teams with fully inclusive diarised risk-based compliance monitoring programmes, online training, manuals, and guides. It’s clear to us that early adopters and first movers in this space will keep the edge when it comes to regulatory developments and evolving global risks.”

A staggering 90 per cent of the world’s data has been generated in the past two years, with the volume of digital data set to grow exponentially thanks to surging internet usage and digital content production, the adoption of AI, and widespread digitalisation across industries.

Amidst this growth, Digital Asset Management (DAM) providers are becoming indispensable, offering a centralised, secure, and efficient way to manage digital assets. Propelled by Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), and computer vision integrations, the DAM market is predicted to quadruple from USD4 billion today to over USD12 billion by 2030.This growth trajectory surpasses the rapid expansion of the broader digital transformation market, underscoring the growing importance of effective digital asset management in an increasingly data-centric world.

Key benefits of DAM

Digital Asset Management systems have transformed beyond their original function as simple storage spaces for marketing materials, emerging as vital tools for enhancing organisational efficiency. With the integration of AI, these platforms have been significantly upgraded, unlocking a host of advancements and capabilities that redefine their utility and impact.

Global scalability and integration

DAM systems are adaptable and designed for global scalability. They offer seamless integration capabilities with various organisational tools, encompassing content management systems, marketing platforms, and design applications. Such integrations facilitate the effortless transfer and management of digital assets across different platforms, minimising the need for manual intervention and enhancing organisational productivity.

Centralised asset repository

Digital Asset Management platforms offer a unified solution for organising and accessing a variety of digital content, ranging from images and videos to text documents and audio files. This centralisation is pivotal in eliminating the inefficiencies associated with scattered storage systems, ensuring consistent brand representation. Advanced search functionalities and metadata tagging capabilities expedite asset retrieval, streamlining workflows and boosting productivity.

Security

Leading DAM platforms prioritise digital security with advanced features such as Single Sign-On (SSO), strict access controls, and multi-factor authentication (MFA). 

Analytics and reporting

As the central hub of enterprise data management, DAMs can serve as a powerful data analytics and reporting tool that improves over time with ML capabilities.

AI technologies unlock new possibilities for the Digital Asset Management market

AI integration in DAM is poised to revolutionise digital asset management, bringing forth a new era of increased efficiency and user-friendliness, contributing to industry growth and evolution.

AI’s capacity to analyse user behaviour, preferences, and past interactions enables the delivery of tailored content suggestions, a feature invaluable to marketing and content creation teams for facilitating the discovery of critical assets with greater efficiency.

Beyond analysing user behaviour and preferences, advanced AI technologies such as computer vision automate the analysis of images and videos, facilitating effortless categorisation and retrieval of visual content.

Security and compliance are other vital areas where AI can considerably impact. The technology allows for active monitoring of access patterns, detects irregularities, and blocks unauthorised entries. AI’s ability to adhere to complex copyright and licensing rules makes DAM systems increasingly relevant in highly regulated sectors such as finance, healthcare, and legal services.

The DAM market is already consolidating

The recent surge in AI-driven innovation has catalysed significant consolidation within the DAM industry. Over the past three years, there’s been a marked increase in M&As, representing 60 per cent of the total Digital Asset Management M&A activity in the last decade, with acquirers evenly distributed between private equity-backed strategic players and Private Equity firms.

Recent M&As in this sector over the past three years suggest a shift in priorities. Company size seems less of a deciding factor in M&A decisions. Instead, there is an emphasis on technological innovation and the strength of SaaS business models.

Further consolidation is coming

Looking ahead, we anticipate that recent technological advancements and the availability of significant investment capital will continue to catalyse consolidation in the Digital Asset Management industry. While market consolidation has intensified over the past three years, driven by factors such as regional leadership and technological innovation, several indicators suggest this trend is far from over.

The DAM market is notably fragmented, with numerous global competitors valued below USD100 million, setting a clear path for further market consolidation. Despite challenges such as high transaction costs and growing regulatory scrutiny, particularly in the US, substantial dry powder reserves amounting to USD2.49 trillion as of mid-2023 provide a fertile environment for continued consolidation activities.

Given these factors, we anticipate a highly competitive M&A environment in the coming months. Competition will be fuelled by the scarcity of high-quality DAM solutions, as highlighted by a Gartner report citing only 19 significant DAM platforms. The rate of M&As will hinge on players’ appetite to establish market dominance and integrate relevant technologies into their solutions. That aside, an anticipated acceleration in these activities is likely when interest rates drop, fostering an advantageous climate for leveraged buyouts.

The Invesco Global Systematic Investing Study is an evolution of the Invesco Global Factor Investing Study, published annually since 2016. The firm writes that the reposition this year reflects the changes within the quantitative investing world, and the use of quantitative methods beyond just factors. The study, which is based on the views of 130 institutional and wholesale investors that collectively manage USD22.5 trillion in assets, also finds a growing consensus that the systematic toolkit can help investors navigate key challenges, such as volatile markets and imperfect data.

The report found that half of systematic investors have already integrated artificial intelligence (AI) into their investment process and reveals a widespread expectation that AI tools will transform portfolio management in the years to come. The majority (62 per cent) anticipate that, within a decade, AI will be as important as traditional investment analysis and 13 per cent expect it to become more important.

The AI revolution already underway

Systematic investors are already using AI across a range of core functions.

Respondents reported harnessing AI to better understand the market environment and identify macroeconomic turning points: (46 per cent) are using AI to identify patterns in market behaviour, and (38 per cent) are using it for portfolio allocations and risk management. Investors appreciate AI’s ability to help mitigate human biases and forecast the unexpected.

Investors expect the use of AI to grow significantly in the coming years. While a significant minority (29 per cent) already use it to develop and test investment strategies, the vast majority (76 per cent) anticipate doing this in future, and while (20 per cent) currently use it to monitor and adjust investments positions in real-time, more than half (55 per cent) expect to do so moving forward.

Wholesale investors identified improved risk management as the main benefit of AI, cited by (76 per cent) of respondents, followed by the flexibility to adapt to changing market conditions (65 per cent). However, challenges remain: wholesale respondents cited the cost of implementation (64 per cent) and the complexity and interpretability of AI models (61 per cent) as the main obstacles to adoption.

“Among wholesale investors, we found a concern around the potential for AI-driven portfolio strategies to overshadow traditional models”, says Bernhard Langer, CIO, Quantitative Strategies at Invesco. “There is a sense that AI-driven models will be attractive to investors moving forward, particularly younger ones, meaning firms must adapt quickly.”

Institutional investors instead see accurate and timely insights (78 per cent) as the most compelling benefit of AI, followed by improved risk management (74 per cent) and increased efficiency and automation (68 per cent). Their primary concerns are complexity (78 per cent) and data quality and completeness (51 per cent).

“The key challenge for institutional investors is stakeholder management. Investors need to be able to explain and justify the use of AI models as their stakeholders are wary of ‘black box’ solutions”, says Langer. “The regulatory landscape surrounding the use of AI and decision accountability also remains ambiguous.”

The rise of natural language processing tools

Investors have embraced natural language processing (NLP) tools, which have been harnessed for a range of operations, such as summarising and digesting whitepapers, converting recommendations into accessible language for sales teams, and modifying communication tonality for different client groups.

NLP models have also been deployed in the investment process. (41 per cent) of respondents are using NLP for sentiment analytics, and around three-quarters (73 per cent) expect to do so in the future. Several investors reported searching online social channels to uncover prevailing market narratives around firms, measuring frequency of mentions and context, providing valuable insight for assessing risks and making short-term trading decisions.

APAC and North America lead the way

However, Invesco’s study found significant regional variations in attitudes towards AI and NLP, with investors in EMEA markedly more sceptical than their APAC and North America counterparts.

The majority (51 per cent) of EMEA investors believe that AI will still be less important than traditional analysis methods in ten years’ time, versus just (10 per cent) in North America and (7 per cent) in APAC. Conversely, just (4 per cent) of EMEA investors believe AI will supplant traditional analysis methods in that period, with much higher numbers observed in both North America (19 per cent) and APAC (20 per cent).

Moreover, North America and APAC investors are currently far more likely to be using AI in the investment process. APAC investors are twice as likely as EMEA investors to be using AI to identify patterns in market behaviour, and more than three times as likely to be using AI to adjust investment positions in real time. EMEA investors trail North America and APAC investors in each aspect of AI adoption. 

The growing systematic toolkit helps investors tame markets

Factor investing has historically been the cornerstone of systematic investing, but Invesco’s study reveals a far larger toolkit of systematic strategies that have helped investors navigate the key challenges of recent years.

Tools to decipher the macroeconomic environment have become especially important, and the ability of systematic approaches to help mitigate market risks was a key theme in this year’s study: the majority (63 per cent) of investors agreed that systematic strategies helped them manage market volatility in the past year. Moreover, nearly (60 per cent) of respondents said that the new higher inflation market regime was supportive of the systematic approach, with only (6 per cent) of institutional investors and (10 per cent) of wholesale investors disagreeing. 

For three-quarters of respondents, dynamic asset allocation has become a core component of their approach, helping them to rebalance and adjust their portfolios in response to the market environment. Systematic tools have helped investors identify and characterise the underlying macroeconomic regime, allowing them to make inferences about its impact on different asset classes, factors, regions, and sectors.

“Recent challenges have prompted investors to question how they navigate unexpected obstacles”, said Langer. “Respondents spoke of expanding beyond factors to better understand markets and identify when certain asset classes tend to outperform others”.

Bridging the ESG data gap

However, the usefulness of systematic approaches is not limited to the macroeconomic picture; respondents have commended systematic strategies as an antidote to the challenges around ESG, particularly bridging the ‘data gap’.

Invesco’s study found around two-thirds of respondents are using systematic strategies to incorporate ESG into their portfolios, and systematic tools have become useful for helping investors decode ESG variables and metrics, which can have a meaningful impact on performance.

Around half of respondents agree that systematic investing can help to apply ESG when data is scarce, and many noted that they were using systematic tools to reconcile the inconsistencies between ratings agencies and develop company scores from raw data.

“There is a low correlation between different ESG ratings agencies, which is of course a much less mature market than credit ratings. So we found investors were turning to systematic models to boost the quality of available data”, says Langer.

Beyond traditional asset classes and factors

Invesco’s study also found a growing consensus that the systematic approach can be applied across a broader range of asset classes than previously thought.

Systematic models are now well-embedded within fixed income and equities, but higher yields, coupled with a shift from quantitative easing, has meant that conventional macroeconomic considerations have returned to the fore in determining returns across various countries and sectors. This has boosted the appeal of systematic strategies for commodities and currencies: while only a quarter currently target commodities this way, (59 per cent) view this as a focal point moving forward.

The new macroeconomic environment has also prompted investors to rethink conventional wisdom about what constitutes a factor.

Notably, four in five respondents now recognise ‘growth’ as a standalone factor, challenging traditional academic views which contended that ‘growth’ was difficult to define precisely. Investors do not see growth as the opposite of value, or vice versa; rather, as distinct and in some cases complementary factors, as evidenced by the rise of nuanced and blended factors like ‘growth at a reasonable price’.

The analysts asked ChatGPT to create a portfolio of 38 stocks from high-quality businesses with criteria taken from the leading 10 funds against which it was competing. 

Finder.com says that over the 40 days to 23 March 2023, the portfolio of 38 stocks has risen 4.93 per cent, against an average loss of 0.78 per cent for the 10 most popular funds which included some from Vanguard, HSBC and Fidelity. 

Whether this convinces investors that AI is a better – and ultimately cheaper – alternative to the human touch remains to be seen, but it certainly forms part of a compelling argument.

Another business hoping to convince investors of the virtues of AI as a competent asset manager is Castle Ridge Asset Management, whose CEO, Adrian de Valois Franklin has built an “evolutionary computing-based artificial intelligence system for active dynamic portfolio management”.

WALLACE as the system is known – named after British scientist Sir Alfred Wallace who co-discovered the theory of natural selection – “is a proprietary Artificial Intelligence technology designed to create, maintain, and evolve investment portfolios consisted of highly
diverse asset classes”.

WALLACE analyses a wide range of technical data covering thousands of securities at any one time and can include decades of historical data; capability that saw Castle Ridge up 6.3 per cent last year for a 1.9 Sharpe.

de Valois Franklin welcomes the buzz AI is creating in the investment industry, but says that the difference between systems such as ChatGPT and WALLACE is transparency.

“The conversation [Castle Ridge has] with institutional allocators has really changed around the question around transparency. So neural nets, deep learning, even GPT are fantastic tools, and you can discover great patterns that didn’t exist before. But they are still a Blackbox and you cannot necessarily explain what’s going on inside.”

He adds: “We have developed a system that’s completely transparent. WALLACE can tell us why it has chosen a particular security and flag the reason it is picking this stock for a portfolio.”

This transparency may well prove critical as regulators including the Competition and Markets Authority (CMA) in the UK and Federal Trade Commission in the US investigate the underlying technology and systems driving AI innovation.

These investigations follow the share price hammering taken by education providers including Pearson and Chegg, after they announced AI was harming their business. 

De Valois Franklin acknowledges there may be corresponding concerns across the asset management industry if AI-powered companies can manage billions of dollars of investment for a fraction of the cost of those run by human-fronted outfits. 

“It does take a lot of resource to run WALLACE, but it’s a lot less than if we had hundreds or thousands of portfolio managers running the same money. We do have a big competitive advantage.”

This January Grand View Research forecast the global AI in asset management market size toreach USD13.43 billion by 2027, expanding at a CAGR of 37.1 per cent from 2020 to 2027.

However, de Valois Franklin says that since Castle Ridge does not use AI to augment existing investment management process and is instead a “pure play AI hedge fund”, it has little to fear from increasing competition.

“We’ve been told by certain institutional clients when people are talking about AI hedge funds, they’re usually talking about Castle Ridge as the new breed of pureplay AI players, rather than the old guard which of course is trying to get up to speed.”

He adds: “We’re not taking anything away from them, but they’re using machine learning now to augment processes that already exist and that comes with some structural or institutional challenges. You’ve got very highly paid people whose motivation is to keep their job and so AI ends up being a tool rather than running the entire process.” 

Through its unique market position, Macquarie provides access to a range of specialised investment products, hedging and tailored financing solutions. Macquarie’s Quantitative Investment Strategies (QIS) team tailors systematic products, providing access to commodity, equity, FX and fixed income exposures.

Macquarie leveraged its pioneering technology platform, incorporating state-of-the-art machine learning models, to develop a RL algorithm. The algorithm sizes the number of options to be sold as part of the FX volatility carry strategy. Through these innovative RL techniques, Macquarie was able to improve the strategy’s downside risk profile and risk adjusted returns.

Arun Assumall, Head of Macquarie’s QIS team, says: “The RL model learns market behaviour through trial and error using feedback from its own trading actions during the model training period. The real skill of applying machine learning in this context lies in the training of the model, to ensure it takes the most relevant information and successfully encapsulates the current trading and market dynamics.”

Bob Champney, Managing Partner at Protean Capital, adds: “Protean have been working with Reinforcement Learning techniques to improve client portfolio returns for a number of years but had found it difficult to find a partner bank to provide concrete implementations. Macquarie were the only bank that provided a skill set and platform which enabled Protean to fully benefit from these techniques.”

According to Macquarie, careful application of machine learning and model training, ensures the algorithm is as effective as possible in generating the best trading decisions in live scenarios, and the resulting signals can be easily interpreted by the QIS team. The successful application of RL in volatility strategies paves the way for its wider use in other systematic investment strategies.
 

Insig Portfolio is a user-friendly web application which provides portfolio managers of all sizes and strategies with additional data driven insights to aid their investment decisions. Insig Portfolio was developed to alleviate the frustration of asset managers currently working with spreadsheets, and to remove the time-consuming effort of building data science applications in-house.

To address these issues, Insig Portfolio has a framework-based architecture that is easily tailored to a portfolio manager’s investment thesis and preferred sources of data. At the same time, Insig Portfolio’s analytics are underpinned by a robust data management process, freeing analysts and portfolio managers from the need to do time consuming manual data updates.

Furthermore, Insig Portfolio includes functionality to explore financial data, ESG metrics, identify important financial features, construct portfolios with machine learning or screening tools, measure and optimise risk exposures, and attribute portfolio performance to regions, sectors and other idiosyncratic characteristics.

Exponential growth in computing processing power has widened the information gap between asset managers with traditional, inefficient data systems and those with easily accessible, efficient data management systems. Insig Portfolio leverages its cloud infrastructure to reduce the constraint of limited computing power with access to multiple cloud-based machines.

Jaco Venter, Head of Asset Management and Data Science, Insig AI, says: “This second release of Insig Portfolio is a major push forward in both capability and usability, allowing investment analysts and portfolio managers to easily slice and dice financial data to enhance investment decision-making based on data exploration, visualisation and machine learning from targeted stock universes.”

Steve Cracknell, Chief Executive, Insig AI, adds: “In addition to financial metrics, we have also developed Insig Portfolio to leverage our ESG machine learning classifiers. This helps meet increasing client demand for more integrated ESG metrics to enable deeper analysis. The sophistication of our data platform makes the integration of multiple data sets easily accessible to our clients.

“We already have four potential clients currently trialling Insig Portfolio who have each agreed to upgrade to this new v2.0 and we are optimistic that further potential clients will wish to have meetings with us to understand the capabilities of Insig Portfolio 2.0 and benefits to their business.”

Developed by Arabesque AI, ‘AutoCIO’ enables asset managers and investment professionals to configure and build hyper-customised active strategies that can be tailored to each investor through more than a thousand different personalised investment options.
 
The launch comes as the asset management industry increasingly looks to leverage technologies like automation and AI for cost-efficient product development, alpha generation and delivering a customised and differentiated client experience.
 
With over USD400 million currently powered by Arabesque’s AutoCIO, the platform offers investors an unprecedented degree of customisation through a streamlined web app that can generate a vast range of bespoke strategies, with AI used to forecast stock performance across 25,000 equities daily.
 
Speaking about today’s announcement, Georg Kell, Chairman of the Arabesque Group, says: “Artificial intelligence will play a pivotal role in the customisation of active investing in the coming years, with pressure growing to innovate both in terms of technology and client centricity.

“Whilst the market is increasingly demanding sustainable products that align with the objectives and values of investors, asset managers are currently unable to offer customisable, active solutions at scale. Investment firms face a fast-changing landscape where many traditional products, tools and approaches are no longer as relevant as they once were.

“AutoCIO is a game-changing solution that can enable asset managers to deliver an enormous range of highly customised ESG investment strategies in a cost-efficient and scalable way. Sustainability issues are fast becoming a global priority, and new technologies like this will empower many more investors to participate.”

Powering AutoCIO is Arabesque’s proprietary AI Engine, which identifies and analyses patterns in data on a large scale to discover subtle relationships that can be translated into alpha opportunities.

The AI Engine is capable of processing billions of data points each day for its stock signal outputs, using the equivalent processing power of tens of thousands of computers, and is run on carbon-neutral Google Cloud infrastructure. As new data is inputted, the AI Engine re-learns what is driving stock returns and aims to improve over time, removing human biases and reducing the potential for errors.

Dr Yasin Rosowsky, CEO of Arabesque AI, says: “Asset managers today increasingly need to personalise products and services at scale to focus on customised strategies that incorporate investors’ sustainability objectives and values.

“We use the power of AI to build systems capable of handling the complexity of financial data and enable scalable investment process design for a wide variety of use cases in an efficient and cost-effective way. This is not a robo or passive investment solution, but fully active asset management, powered by AI.

“We are excited to bring AutoCIO to the market and provide asset managers and investment professionals with a scalable, digital tool to build actively managed, customised solutions that meet their clients’ sustainability goals.”

Catelas ingests all types of eComms, trade alerts, and voice calls to provide buy- and sell-side firms globally with a single view of potential high-risk activities and behaviours across their organisation.

The acquisition of Catelas further enhances the holistic surveillance capabilities of ACA’s RegTech platform. Catelas’ patented technology automates the mapping of how people connect and form groups within a firm, isolates collusion risk, and detects high-risk behaviours. In 2022, Catelas will integrate with ACA’s award-winning regulatory technology platform, ComplianceAlpha. This integration gives firms unique oversight of control room activities, trade and eComms surveillance, wall crossing, bribery, and other material risks through one central platform.

Danielle Tierney, Senior Analyst at Greenwich Associates, says: “The demand for integration throughout the trade surveillance alert process is driving innovation across the compliance landscape. Firms who take action and invest in technologies to conduct surveillance across trading and communications are taking important steps to protect their employees, markets, and stay on the on top of regulatory demands.”

Eddie Cogan, ACA Group Partner and founder of Catelas, says: “Electronic communications surveillance is critical to compliance and is becoming increasingly complex as firms move to Microsoft Teams, WeChat, Slack, Zoom, and other virtual communications tools. Globally, firms are at risk if they are not diligent about tracking and surveilling activity alongside personal and firm investments. We’re excited to join the ACA team and deliver Catelas’ advanced eComms analytics within ComplianceAlpha.”

Shvetank Shah, Chief Executive Officer, ACA Group, says: “ACA’s vision is to build a fully integrated, holistic regulatory risk and compliance management platform that provides a single view into all activities that could put firms at risk. Bringing the Catelas team into the ACA family helps firms create a more efficient compliance operating model, better protecting them from regulatory fines and reputational damage while decreasing the overall cost of compliance.”

ComplianceAlpha integrates risk and compliance activities, automated surveillance, ongoing monitoring, flexible workflows, enhanced analytics, and managed services delivery. Designed, developed, and supported by ACA’s experienced team that includes over 200 former regulators and compliance officers in addition to subject matter experts in cybersecurity and technology, the platform is used by over 1,000 leading financial services firms worldwide to build more scalable and resilient compliance programs.
 
Catelas was founded in 2007 and has locations in Boston, Chicago, and San Francisco. The firm’s surveillance platform is used by global banks, asset managers, and capital market firms to isolate the risks that could harm the business without overloading reviewers with false positives.
 

Read the full story at Hedgeweek…
 

Each month, industry experts and leading academics will discuss the future of investing. The first episode, entitled “AI: Delivering on the Sustainability Promise”, explored the implications of the recent boom in ESG investments by two professors from the Gothenburg University and UCSC Milan.

The second one, “AI in Portfolio Construction: Standing on the Shoulders of Giants” (17 June, 3:00 pm, UK), will look closer at the drivers behind the AI evolution, featuring the expert opinion of Brian Guck, former Chairman of the Economics department at Bryant University and Global Equity Portfolio Advisor at Wellington Management, Alessandro Sbuelz, Professor of Quantitative Finance at UCSC of Milan and Tommaso Migliore, CEO & Co-Founder of MDOTM. Yvonne Pichert, Senior Vice President of Business Development at MDOTM will moderate the panel.

Research has always been one of the pillars of MDOTM: since its founding, it has partnered with prestigious international universities and built up an investment research center — the MDOTM LAB — where more than 30 external researchers and PhD in Finance and Data Science collaborate to bridge the gap between economic theory and real-world practice.

“We launched Frontiers to share where the state-of-the-art research is being applied right now and why it matters for investors, asset managers, advisors, and private bankers,” says Tommaso Migliore, CEO & co-Founder of MDOTM. 

With a team of physicists, engineers, data scientists, and finance experts, MDOTM is among the first Fintechs to successfully develop investment strategies supported by adaptive, robust, and reliable Artificial Intelligence. In this sector, MDOTM has one of the longest track records in Europe, and its AI-Driven models support the investment process of banks, insurance companies, asset, and wealth managers.

“Over the last five years, we have worked to build the best AI-Driven technology to invest in the financial markets”, says Federico Mazzorin, Chief Scientist & Co-founder of MDOTM. “Our webinars will cover the latest research insights and provide an objective and independent view on how to navigate the ever-changing financial markets”.

MDOTM will host the events on its virtual channels. Among the topics covered: Artificial Intelligence, ESG investments, new techniques for achieving portfolio diversification, factor investing, open innovation, and partnerships between financial institutions and fintech.