INSIGHTS Opinion

Producing reliable environmental, social, and governance (ESG) data has become paramount for companies in the last few years. With it, they can demonstrate how they are reducing their carbon footprint, protecting their social capital, or complying with the booming disclosure landscape.

Reliable ESG data helps companies identify potential risks, manage resources, and remain compliant with regulation. But it can be much more than a backwards looking tool. The most ambitious companies that we work with at Arabesque are looking to transform ESG data into a competitive advantage, producing actionable business intelligence which will set them apart from their competitors.

The biggest hurdle to this is perhaps the most obvious. How can I ensure that I have access to reliable ESG data? What does it look like, and how can I gather it? These are the sort of questions which have, until recently, puzzled business leaders. They’re also the questions that we are answering at Arabesque.

Looking at the first frameworks to regulate ESG data disclosure is like looking at an early map of the first railways. A hodgepodge of private enterprises, many overlapping and intertwining, suffering from a lack of unification. Today, companies have a confusing mixture of compulsory and voluntary frameworks to report against. Even if they fulfil their obligations, most reporting companies do not disclose the same data in the same way. Some are simply unaware of their obligations.

Cutting through all this noise is crucial. Investors, underwriters, regulators (not to mention companies themselves) stand to benefit enormously from a market and supply chain greased with the oil of reliable data. But it is impossible for the vast majority of organisations to confidently gather the information required under their own steam.

This is why companies look to the likes of Arabesque to supply global markets with transparent, accessible and coherent ESG information; to help investors respond to customer values, enable corporates to meet a growing wave of disclosure requirements, and to provide the wider market with meaningful signals that enable an informed allocation of capital.

Our team prioritises Artificial Intelligence to help achieve this. We gather a vast amount of data; from traditional, more structured data, to a wide range of alternative sources of information such as the press, social media, or NGO activity. AI allows us to make sense of this highly complex mix of disclosed information and short-term signals. It can rationalise thousands of competing and overlapping sustainability metrics into a high-quality picture of a company’s ESG credentials.

As the volume of sustainability data increases – in the next eighteen months we will see a slew of new regulatory requirements – it will become increasingly important for investors to work confidently with AI to make truly sustainable investment decisions.

Customisation is now the next stage of the journey. Being able to take a detailed look at the underlying information which companies disclose, and filter through the thousands of data points to reach the specific insight which a business requires is swiftly becoming not a ‘nice to have’, but a business necessity for organisations looking to balance consumer demand with maximising returns. Arabesque’s ‘Temperature Score ’ is just one example of this.

Sustainability data will only become more important in years to come. The onus is now on companies to ready themselves with the tools needed to be leaders in using it.

To learn more about Arabesque’s customised data solutions and insights, register for the upcoming webinar ‘Using ESG and sustainability data in the cloud to drive business value’ hosted by our partners AWS Data Exchange.

INSIGHTS Opinion

By Julian Gaberle, 04/06/2021

Last month the Biden administration signed an Executive Order with the intention of reducing risks to financial stability and improving disclosure of climate related risks. A similar mandate was signed in the UK, where environmental and climate goals will now be explicitly considered by the Bank of England as part of monetary policy. Artificial Intelligence (AI) systems have been shown to have the potential to decouple economic growth from rising carbon emissions and environmental degradation; but what is the impact of the AI systems themselves on reaching a net-zero carbon economy?

AI systems have been used in a couple of ways to reduce carbon output. One method is to use AI in resource decoupling (reducing the resources used per unit of economic output), for example, by predicting crop yields or controlling heating and cooling of the built environment. Further, we have also seen impact decoupling. This decouples the economic output and environmental harm by using smarter planning systems. For example, AI systems have helped reduce economic emissions by forecasting energy usage, thus improving scheduling of renewable energy sources.

Figure 1: Demonstrating the increase in AI Model complexity over time. (Source: https://openai.com/)

Despite these advances, AI use cases have garnered significant notoriety for landmark developments in “state of the art” models such as GPT-3 (an AI model trained to understand language). The compute resources consumed to train a single model has been growing exponentially over the past decade (see Fig. 1) with a single training session of GPT-3 having an energy consumption equivalent to the yearly consumption of 126 Danish homes. Research and development of such models over multiple architectures and hyperparameters multiplies these costs by orders of magnitude, however, the true figure is hard to estimate due to lack of disclosure. Furthermore, such huge models require specialised data warehouses and vast arrays of compute chips, limiting the accessibility of such technologies to few tech companies.

At the same time, researchers are investigating more efficient models and are continuously improving existing ones (see Fig. 2). Using existing hardware, improvements can be made to data handling, parallel processing and code efficiency. From a hardware perspective, intense research is being done on custom processing chips (e.g., Cerebras’ wafer chips or Google’s TPUs) which are optimised for specific AI applications and thus reduce the resources required to develop and deploy a model.

Figure 2: Model efficiency over time. (Source: https://openai.com/)

Several factors influence the environmental impact of AI systems: the location of the compute resources used for development; the local energy supply; the size of the dataset; and the hardware used to train the models. Just 10 years ago, 79% of computing was done in smaller computer centres and company servers, but since then a dramatic shift to the cloud has taken place – by 2018 a paper in Science estimated that 89% of data centre computing was done in large data centres. These large data centres use tailored chips, high-density storage, so-called virtual-machine software, ultrafast networking and customized airflow systems. Furthermore, they can be built at more suitable sites for large compute clusters such as near lakes or in cooler regions for cheaper, more efficient cooling.

The intended carbon reduction (side-)effects of the migration from on-premise, small data centres (‘on-prem’) to larger, more efficient, cloud based solutions has so far had a relatively small net effect; having these resources available has allowed more, and more complex research to be done, increasing demand. If current development towards ever larger AI models continues, resource consumption of AI systems will outstrip any gains in efficiency.

How does Arabesque AI seek to solve these problems? As a business, one of our core values is Sustainability. This extends to our models as well. We have migrated to our Cloud partner Google Cloud Platform from a previously On-Prem system in order to reap efficiency benefits. We are working hard to increase the efficiency of our models; within our Research and Engineering teams, this is a key objective for 2021. While our models are exceptionally complex, they will only remain this way, and likely become more so. We feel there is more work we can do around efficiency of the compute processes we use, which will realise both environmental as well as obvious economic benefits. We encourage other Machine Learning and AI reliant businesses to focus on these goals as well.

INSIGHTS Opinion

By Julian Gaberle, 06/05/2021

Artificial Intelligence (“AI”) systems have seen widespread adoption – from online retailer’s recommendation systems, to smart navigation and gaming. AI, however, increasingly finds applications in less transparent areas such as defence and surveillance, or finance and credit scoring. While these applications can produce accurate results, they are often highly complex. This complexity has led researchers and policymakers to question: is it possible to understand how AI works, or is AI a ‘black box’?

On April 21^st, the European Commission published the ‘Proposal for Regulation laying down harmonised rules on artificial intelligence’, or the ‘Artificial Intelligence Act’ (AIA). The proposal aims to build a regulatory framework to allow AI innovation while mitigating potentially high risks associated with AI applications (see our previous blog post on AI regulation). A key theme in the published proposal focusses on ‘[..] addressing the opacity, complexity, bias […] of certain AI systems’. In a previous blog post, we have written about data and model bias, and here we will discuss how one may improve model transparency in better addressing the ‘explainability’ of AI.

What does ‘explainability’ mean in this context? The Royal Society has identified five key attributes that are desired when deploying AI systems:

Interpretable: implying some sense of understanding how the technology works.
Explainable: implying that a wider range of users can understand why, or how, a conclusion was reached.
Transparent: implying some level of accessibility to the data or algorithm.
Justifiable: implying there is an understanding of the case in support of a particular outcome.
Contestable: implying users have the information they need to argue against a decision or classification.

Why ‘Explainable AI’ (XAI):

A level of explainability or interpretability is necessary when considering deploying AI systems. This is in order to:

Give users confidence in the AI systems. Widespread AI adoption requires trust that the systems work well, and for the benefit of their users.
Monitor, manage or reduce bias. In almost all AI applications, bias has to be addressed in order to ensure the AI system performs as intended, or at minimum, is aware of the underlying implicit or explicit bias. For example, texts used for training natural language models often perpetrate outdated gender roles.
Adhere to regulatory standards or policy requirements. AI regulation such as the AIA will curtail the current wide-ranging freedom of AI development and will need to be adhered to.
Safeguard against vulnerabilities. Understanding the limitations of AI systems can help protect from detrimental decisions. For example, adversarial attacks on image classification systems can fool a system to produce an arbitrary outcome.

We believe the next generation of AI systems will have the ability to explain their “rationale”, characterize their strengths and weaknesses, and convey an understanding of how they will behave in the future. Our strategy for achieving that goal is to develop new or modified machine-learning techniques that will produce more explainable models.

We pursue a variety of techniques in order to generate a portfolio of methods that will provide a range of design options covering the performance-versus-explainability trade space. Two approaches we currently take are:

Perturbation correlation analysis:

For a given point of interest, e.g., the price prediction for a single stock at a given date, we examine the impact of small changes (perturbations) to the input features on the AI model’s output prediction. This will give insight into the sensitivity of the model to variations in the input data, which may include missing values, wrongly reported fundamental numbers, or previously unobserved market conditions.

Local surrogate model:

The relationship between inputs and outputs in AI models is often highly complex and non-linear. However, if we make the assumption that in the vicinity of a given point of interest the mapping between inputs and outputs can be reasonably approximated by a linear model, then we can fit a linear surrogate model, which allows us to examine the contribution of each feature to the model’s output at that point.

While essential to achieve explainability in AI models, understanding the limitations of AI applications and managing resulting effects is equally crucial to ensure safe and robust performance. Such systems need to consider the entire pipeline of AI development and implementation, including how objectives for the system are set, what data is used to train and evaluate the models, and what are the implications for the end user and wider society. In our opinion, it is only then can trust in autonomous AI decision makers be built – a crucial step in building sustainable AI deployments.

INSIGHTS Opinion

By Matthias Baetens, 01/04/2021

Over the past fifteen years we’ve witnessed a significant adoption of Cloud-based systems by corporations. We highlight below some of the advantages enjoyed by the financial services industry today.

In the broad sense of the word, the Cloud is the set of computing systems allowing one to use (potentially shared) resources on-demand, offered by third-party Cloud providers (e.g., Google Cloud Platform, Microsoft Azure, Amazon Web Services) over the public internet.

The drive to efficiently share computing resources has been around for decades and we can see cloud computing as its latest incarnation. The public cloud as we know it came to prominence when Amazon incorporated Amazon Web Services around fifteen years ago, and the industry has been growing ever since.

According Gartner end-user companies’ expenditure on cloud resources exceeded $250 billion in 2020 and growth of 18% is expected for 2021, with COVID-19 being an additional driver in adoption as more and more companies need to accommodate remote-working set-ups.

So how has the advent of cloud computing impacted the Financial Services Industry? Initial adoption in our industry mainly came from FinTech start-ups that were born “Cloud-Native” – having no information-technology infrastructure themselves. In more recent years, however, larger incumbents have moved dependencies from in-house computer resources and started to adopt the public cloud – amongst them for Deutsche Bank which will co-innovate alongside Google Cloud and HSBC which has adopted a multi-cloud strategy with the three major cloud providers.

What are some of the draws for companies to adopt cloud?

Increased reliability and resilience: By having multiple geographically distributed fail-over data centers with Cloud providers, the direct dependency on a company’s local set-up is removed, allowing for more seamless disaster recovery processes. Separately, when a company leverages Cloud infrastructure, they, in turn, are also able to offer a more reliable platform for their own clients.
Cost effectiveness: The Cloud provides for a reduction in up-front capital spending on physical hardware; it allows for a pay-as-needed contract structure. More specifically, businesses historically needed to anticipate peak usage and purchase physical hardware to meet anticipated needs (with human capital to support). With a migration to the Cloud, physical hardware on premise is no longer needed as this sits in remote data centers with Cloud providers; resource use can now be adjusted real-time and paired with more flexible pricing schemes.
Scalability: The Cloud allows for enhanced and faster processing capabilities allowing for the ability to scale business functions. As previously mentioned, the Cloud providers allow access to more hardware that in turn allows an asset manager for example, to run more (detailed) analyses on portfolios in a shorter amount of time.
Security: Cloud providers hold themselves to extremely high security standards. Data centers maintained by public cloud providers have dedicated teams securing the premises against invaders and are better designed to withstand the forces of natures. Furthermore, they have dedicated teams that actively look for vulnerabilities in their software and systems. A noteworthy example is the Meltdown vulnerability discovered by Google’s Project Zero.
Enabling innovation and technological transformation: The Cloud provides an avenue for easier experimentation built on the latest technologies, and more seamless (data) integration between different business units and systems.

Where there are many advantages to adopting the Cloud, there are also challenges and shortcomings with this technology that seemingly evolves daily. These include having less control as these functions are now outsourced, high switching costs between providers, and less customizability.

In light of these aforementioned challenges, for Arabesque, adopting the Cloud has been particularly helpful on multiple fronts. We have been able to scale our equity coverage universe, reduce expenditures by leveraging cloud service provider hardware, and optimize hiring and training through adoption of standardized “known” toolsets. For further information on how we leverage the cloud, see a joint case study our Arabesque AI research team conducted with our primary Cloud provider, Google Cloud.

INSIGHTS Opinion

The palm oil industry has grown steadily over the last two decades, with demand consistently outstripping supply, to become the world’s most produced and most consumed vegetable oil.

However, what are the problems associated with palm oil, and is there an environmentally sustainable way to produce it? If so, can we identify sustainable palm oil and reliably avoid investment in palm oil that is unsustainable?

These are some of the questions Dr Roan du Feu explores is his new blog on the sustainability of the palm oil industry.

Can Palm Oil Become A Sustainable Investment? By Dr Roan du Feu

INSIGHTS Opinion

Towards successful AI applications in the financial industry

Technological advancements within Artificial Intelligence (AI) continue to play a major role in shaping global competitiveness and productivity as AI becomes more mainstream. The presence of AI in pressing global social issues such as privacy issues and Covid-19 testing, has led to an increased focus by policy makers and the public. Examples include:

The European Union (EU) aims to establish a reputation for “trustworthy AI” that is “made in Europe” with a strong emphasis on ethical and human-centric approaches, aligned with human rights values and democratic principles.
In December 2020 the UK House of Lords Liaison Committee called on the government to better coordinate its AI policy and the use of data and technology by national and local governments.
UK regulators have set up the first meeting of the AI Public Private Forum with the aim to assess the impact AI has on financial services.
EU adoption of a new digital finance law last September.
SEC’s Strategic Hub for Innovation and Financial Technology (FinHub) is currently running virtual meetings with industry representatives to better understand how AI is used to, for example, build new products, offer new services, create efficiencies, and enhance regulatory compliance.

Due to AI’s self-learning nature, challenges around model explainability (i.e., the ability to discern the model’s reasoning), data integrity and privacy need to be addressed for successful applications of AI. Arabesque AI offers these solutions for consideration in an effort to narrow the knowledge gap between practitioners, policy makers, and the public.

Model Risk Management:

Challenge: Model explainability presents a unique challenge for AI applications, particularly in the space of Deep Learning, where non-linear dependencies can occur. For complex models, such non-linearities operate as black boxes making it difficult to explain model reasoning.

Proposed solution: In our opinion a comprehensive risk model should focus on model development, evaluation and validation as well as ongoing testing and monitoring; paired with transparency of research conducted to address the importance of explainability. AI models learn underlying distributions from the data used to train them and thus particular focus should be placed on tail-end events (e.g., unprecedented market conditions), in order to assess how well these models can generalise to data not seen or under-represented in the training process.

Data Integrity:

Challenge: Last month’s newsletter highlighted issues around data bias for successful AI applications that require large amounts of data to train and validate.

Proposed solution: It is our belief, continuous monitoring and evaluation of input data is imperative. Building models trained to detect data bias or abnormal data can help in building more robust AI pipelines. Furthermore, recent studies report that more diverse teams of researchers are better equipped to spot data bias, highlighting the need for some level of human oversight of AI applications.

Currently we are observing a trend to include alternative data sources into AI pipelines e.g., social media, news or geo-spatial data. Data source verification is a vital step in the data pipeline to guarantee reliability, especially as we are seeing a rise in media manipulation campaigns. In our belief, a robust data ingestion, verification and storage pipeline will help safeguard AI applications and lead to more robust AI deployments.

Privacy

Challenge: Recent publications highlight how AI can use customer data such as credit card transactions. As such data sources get incorporated into AI applications, data privacy and security considerations need to be addressed.

Proposed solution: In our opinion, the protection of personal data is a key responsibility of AI adopters and paramount to building trust in the technology. Various techniques exist within AI that safeguard personal data. This should be incorporated within all stages of model development, from idea generation to the public released of the product itself. Relevant legislation includes European General Data Protection Regulation (GDPR) that mandates strict conditions whilst using personally identifiable data.

While AI holds tremendous potential to radically transform the financial industry, it also presents unique challenges, risks and regulatory considerations. It is vital to continuously assess AI strategies on firm level as well as industry and national levels. Only by building trust in robust AI applications can the transformative power of AI be leveraged for good.

Arabesque AI

More on Arabesque AI:

Arabesque has been built on the two disruptors of finance, sustainability and Artificial Intelligence. Utilizing advancements in technology, as an organization, Arabesque seeks to deliver transparent, sustainable, innovative solutions for our clients; whether through our SRay® data services, investment solutions or, most recently, our AI research. Arabesque AI was established in late 2019, with a minority stake owned by the asset manager DWS, with the mission to build a world-leading, AI-driven, investment technology company that offers its clients high-performing, efficient and individually customizable investment strategies. The investment philosophy underpinning the use of AI, is that the discernible structure in financial markets is highly complex and varies over time, markets, and asset classes. AI can thus be used to build systems capable of handling this complexity and of enabling scalable investment process design for a wide variety of use cases in an efficient and cost-effective way.

INSIGHTS Opinion

By Matteo Maggiolo, 09/02/2021

Quantitative strategies have been an established part of investors’ toolboxes and are thought to address some of the limitations of traditional fundamental approaches. As we know in the most basic illustration, traditional approaches to investing generally involve one or more financial experts whose responsibilities include analyzing, discussing and ultimately selecting a group of assets for a portfolio. These decisions are based on the experts’ uniquely developed and aggregated insights which can be limiting. For one, as with any decision one makes, they are likely influenced by various cognitive biases.( Furthermore, the ability to scale the knowledge of one across more strategies and asset classes is a challenge (if not nearly impossible, in our opinion).

Quantitative approaches, rather, offer an alternative solution that enables reducing bias, providing scale and improving transparency:

Asset selection is based on mathematical models and precise rules thereby limiting certain biases.
Leveraging modular technology for parsing and interpreting data can allow the investment universes (and spectrum of investment solutions) to grow.
With the rules-based nature of quantitative models, it is easier to explain decisions made, as structured. This further allows for consistency across similar situations.

This all said, quant approaches similarly have their limitations. They can still be subject to certain biases as the pre-existing research, theory and market paradigms from which they draw might not align with the specific environment in which the investment decisions are taken. Furthermore, the process of adapting a quant approach to new market environments and asset classes can be challenging, thereby limiting scalability and preventing the swift integration of new data sources.

So, we turn to AI…AI methods have seen great advancements in recent years, claiming remarkable achievements in many different fields, including financial sector (as we discuss here). Tying these concepts to investments and our aforementioned points, we find AI methods can be successful due to:

They are data-driven in nature which can potentially reduce the bias introduced by pre-existing beliefs about the market.
The same methods can be scalably used across different asset classes, geographic regions, markets and sources of data.
Their effectiveness grows with the amount of data provided.

While the financial services community is still in early stages of applying AI and ML techniques to investments, we find the benefits are apparent. As advancements in these technologies are constantly evolving, and the derived models are becoming more complex, transparency is a challenge. With increased research into its application we believe that this will provide the ground for a new era in finance, removing biases further, and improving its effectiveness over time.

We look forward to sharing more with you in the coming months!

Arabesque AI

More on Arabesque AI:

INSIGHTS Opinion

Initially confined to specialized applications and niche markets, we are currently witnessing the widespread adoption of Artificial Intelligence (AI) and Machine Learning (ML) affecting our lives daily and, increasingly, in financial services. The finance sector has keenly observed the rise in AI applications and interest in all areas is growing – from asset management and quant trading, to fraud detection and personal banking.

Focusing within asset management specifically, strengths of its application include the ability to analyze complex data faster, more efficiently and arguably more accurately, as a means of enhancing the decision-making process. Scale and personalization advantages are also in focus.

AI methods, whilst still at an early stage of adoption, may include the following (source: CFA Institute (2019)):

respondents using linear regression (in investment strategy and process) outnumber those using AI/ML techniques by almost five to one;
only 3% of organizations had a technology team initiated with specific AI/ML capabilities;
the number of respondents using particular ML approaches (supervised learning, unsupervised learning, reinforcement learning) and ML models (Markov models, deep learning models, support vector machines) ranged from 6% to 15%; and
even though the use of unstructured data such as e.g. news was as high as 44%, the use of AI-based techniques to process these data such as natural language processing (NLP) was low (10%).

Critical to the success of the application of AI to asset management is data. Simply, Arabesque believes that AI methods perform optimally with large amounts of data. With data increasing exponentially, being available on public clouds and processed in real-time (see Figure A), it is anticipated that AI approaches will continuously improve as a result.

Figure A: The Global Datasphere 2025. Source: Bril et al. 2020

Furthermore, AI benefits from the increasingly available access to High Performance Computing (HPC) techniques such as the use of Graphical Processing Units (GPU). With continuous improvement of hardware (including the emergence of quantum computing McKinsey & Company (2020)), AI methods will continue to evolve and improve over time.

We look forward to sharing more developments and research from our AI team and its applications to asset management.

Happy New Year and thank you for your continued support.

Arabesque Asset Management

More on Arabesque AI

The investment philosophy underpinning the use of AI, is that the discernible structure in financial markets is highly complex and varies over time, markets, and asset classes. AI can thus be used to build systems capable of handling this complexity and of enabling scalable investment process design for a wide variety of use cases in an efficient and cost-effective way.

INSIGHTS Opinion

By Gabriel Karageorgiou and Dominic Selwood

Companies’ priorities are rooted in the culture of their times. The East India Company, for example, systematically asset-stripped the Indian subcontinent for more than 200 years — and was largely celebrated by its shareholders as a roaring success. Now we look back in horror at the bloodshed, slavery, drug money, corruption, and exploitation that were its daily currency, and we note that today’s leading companies (hopefully) have strikingly different definitions of success.

Today, most people would argue that successful companies are not just financially sound, but must also be socially responsible and environmentally sustainable, which can be assessed with various environmental, social, and governance (ESG) metrics. We propose, however, that ESG metrics are merely the observable result of a more fundamental set of values: a notion we call corporate philotimy.

Find a Greek friend and ask what philotimo (φιλότιμο) means. The reaction will probably be a starry-eyed smile from deep inside. Then ask for an English translation, and that smile will turn to a look of bewildered helplessness. While the word’s etymology is simple — philos, or friend/love, and timi, or honor — philotimo carries a universe of rich meanings. It is decency, dignity, honesty, altruism, and a dozen other ideals encapsulating what it means to live with integrity. It is greater than the individual, with a person’s act of philotimo reflecting positively on his or her family, community, organization, and society. It was first spoken of by the pagan poets of antiquity, and St. Paul — a native Greek speaker — included it in his letters numerous times, urging his readers in Thessaloniki to fill their lives with philotimo. It is a universal, transcendent good, an internal ethical compass of fairness, compassion, and justice.

In the context of an organization, corporate philotimy is the immutable DNA that determines how a company operates at the cellular level. It is the principle that guides a company’s sustainability behavior, which can then be quantified with ESG metrics.

Corporate Philotimy Creates Corporate Value

A recent PwC report identified investment in ESG as the “growth opportunity of the century.” Over the past 10 years, investment strategies focused on nonfinancial parameters (that is, strategies prioritizing companies with a strong sense of corporate philotimy) have exploded. And those strategies are paying off: Investors are finding that companies with a strong sense of philotimy consistently outperform less-virtuous companies. The reason is fourfold:

Companies with a culture rooted in sincere empathy treat their employees well. They are therefore able to attract and retain the best human capital, which enables them to create the most-innovative products and services.
These companies are mindful and respectful of the communities in which they operate. They acknowledge and address community concerns, increasing engagement and facilitating smooth interactions and collaborations.
Compassion for all stakeholders inspires these companies to take care of the resources — human, environmental, and others — on which they rely, making their success more sustainable.
These companies are managed with transparency and accountability, so all stakeholders understand their processes and key competencies and are able to make better-informed decisions.

Ultimately, a culture of corporate philotimy enables companies to build trusted brands, leading to loyal customers, engaged employees, and supportive shareholders. As a result, many companies are experiencing increased pressure from regulators, asset owners, and society to improve their ESG profiles, spurring a race to better performance. In this new arena, some companies have been accused of “greenwashing” — that is, of publishing shiny corporate social responsibility reports without living the values behind them. But although ESG metrics can be padded, corporate purpose is much harder to fake. Corporate philotimy requires strong virtues to be held as an end in and of themselves, not merely invoked for a quick win.

Building Corporate Philotimy Starts with Hiring People with Philotimo

How do you build a company with a strong sense of corporate philotimy? It starts with people. The notion that aggressive employees drive success is long dead. Research has definitively shown that productive teams are the direct result of positive work cultures — of deeply held corporate philotimy. In such environments, individuals feel a moral responsibility not to let their teams down. When they see colleagues struggling, they react with compassion. They give credit for collective achievements and avoid blaming others for failures. As a team, they forge a strong “we are in this together” bond, focused not on the bare minimum they are asked to do but on anything and everything they can do to contribute to the team’s success.

Both academic and anecdotal evidence support this. In 2012 Google launched a now-famous project to understand what makes a perfect team. The study — dubbed Project Aristotle, in honor of the philosopher’s well-known dictum that the whole is greater than the sum of its parts — found that cultures of empathy and kindness give teams a psychological safety net. This structural reassurance translates into greater levels of trust, respect, and engagement, enabling individuals to take initiative and share new ideas without fear of judgment. Other research confirms these findings, showing that demonstrating strong ethics and providing a sense of safety are among the most important competencies for leaders looking to build positive, productive workplaces.

How, then, do you hire people who will bring a strong sense of philotimy to your team? Virtue can be tricky to assess, but there are tells: candidates who use “we” rather than “I,” who share credit, own errors, and enjoy contributing to collective success. There are no easy answers or foolproof recruiting practices, but prioritizing individual philotimy in your hiring process is central to building organizational integrity.

How Investors Can Identify Corporate Philotimy

There’s another piece of the puzzle: ensuring that companies that demonstrate philotimy get the support they need to grow. Investing in sustainable companies is a sensible strategy both financially and ethically, but how can investors identify those opportunities? It’s challenging, because ESG disclosure is not yet universal, and many companies don’t voluntarily disclose or self-regulate their behavior. Moreover, even when companies do share some metrics, it can be hard to tell the difference between genuine, values-driven performance and a marketing stunt. But understanding a company’s true culture is not impossible.

To start, ask the following questions: Is the company’s core business opportunistic, or does it serve a wider social purpose? Does it prioritize short-term benefits over long-term opportunities? Are management and shareholder interests aligned? And what is the company’s track record on employee, customer, and stakeholder loyalty? Evaluating performance in these areas will provide an indication of the ethics and priorities driving the company’s actions.

Societies increasingly expect companies to be genuinely sustainable. They expect the businesses they support to look after their customers, their employees, their communities, and the planet. These characteristics — the hallmarks of corporate philotimy — demand a deeply ingrained sense of individual and organizational responsibility, and they create a positive work culture that inevitably translates into higher productivity. Companies that embrace philotimy don’t promise more than they can deliver, and they deliver more than they promise. To paraphrase Socrates, an undying sense of philotimy is what inspires individuals and organizations to behave as they would wish to be remembered.

Tackling the world’s most-pressing social and environmental issues will take authentically self-motivated, purpose-driven organizations working together to build a global business culture of corporate philotimy. In a world where more often than not, politicians and regulators fail to address these vital issues, companies must act with philotimo, embracing their moral obligation to serve as a force for good in their local communities and around the globe.

Gabriel Karageorgiou is a Partner at Arabesque Asset Management Ltd.

Dominic Selwood, PhD is a journalist, historian, author, barrister, and a co-founder and Partner at Arabesque Asset Management Ltd.

INSIGHTS Opinion

Georg Kell is the founding Executive Director of the United Nations Global Compact, the world’s largest voluntary corporate sustainability initiative, and Chairman of Arabesque.

Kell, along with Herman Bril and Andreas Rasche, are the co-editors of ‘Sustainable Investing: A Path to a New Horizon’, a unique combination of perspectives from academics and practitioners on how corporate sustainability and sustainable investing are converging and driving change in markets.

In a discussion with Martin Reeves, Chairman of the BCG Henderson Institute, Kell discusses the role of the corporate world and finance in making progress on sustainability, and shares his thoughts on how COVID-19 has impacted how we think about long-term challenges and resilience.

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