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In this article – originally written as part of the ESCP Business School’s “Better Business: Creating Sustainable Value” series, Professor Terence Tse from ESCP Business School discusses how the power of AI can lead to efficient extraction and interpretation of ESG-related data, and how making such data available in a timely fashion can give more credibility to and empower the various ESG indices to monitor and track companies better.

It is perhaps understandable as to why some people are skeptical about artificial intelligence (AI). First, media and research reports often illustrate how machines will be taking over our jobs, resulting in the elimination of the work positions currently held by many. Second, in many instances, AI remains a “blackbox”. Typically, in machine learning, we can only see the inputs and outputs but are clueless on how those inputs are being combined to reach the results. Put differently, machines turn the input into output in ways that are completely unobservable to us. Applying blackbox algorithms in various aspects of public lives such as justice will have deep social and ethical ramifications. The development of machine learning technologies is charging full steam ahead. Yet, the methods for monitoring and troubleshooting them are lagging behind.

Third, it appears that some companies, especially the technology giants, have doubled down on AI to increase their profitability, often at the expense of public interest. For example, Google is arguably extracting a staggering amount of data on users’ private lives. The company then uses such data to attain accurate predictions of future human behaviour, which can subsequently be sold to markets of business customers. Another (better known) example is how Facebook deployed AI through Cambridge Analytica to influence voters, hence interfering with the democratic processes.

Tech for good

Yet, we should not forget that AI can also create common benefits. There is no shortage of conversations on how companies can use technologies not just to do good but also to do well. For example, there is an increasing number of discussions on using AI to help reach circular economy goals. Yet, the progress for technology-driven pursuit of sustainability remains slow. One reason for such sluggishness is that there remains a lack of economic incentive for companies – and their investors – to make huge investments concerning social and environmental gains and benefits. This also provides an explanation as to why, despite years of discussions on the importance of the so-called “triple bottom line” – the need to care for not just profit but also people and the planet – has hardly become a mainstream practice among today’s businesses. Indeed, at the same time, there is an increasing number of socially and environmentally conscious investors.

Looking at it from this vantage point, two lessons are clear. The first is that unless investors are getting the satisfactory return, it will be difficult to get businesses to orientate themselves towards goals related to people and the planet. The second is that investors need to have timely and accurate information to make informed decisions. In this respect, AI presents a welcome and potentially extremely beneficial tool to help the latest idea in sustainability: environmental, social and governance (ESG).

A socially important asset class

Investments in ESG have fast become an important area of interest. One study points out that sustainable investments amounted to some $30 trillion in 2018, up by 34 percent from 2016. Indeed, investors (and our societies in general) are increasingly keen to understand whether and by what means businesses are being ESG-compliant. Simultaneously, boards and managements have become cognizant that ESG is crucial to the long-term survival of their companies. It is therefore unsurprising that as much as 90 percent of investors globally are estimated already to have in place, or to have plans to develop, specific ESG investment policies. To guide the selection of such investments, several ESG-based rating and index services such as MSCI, Bloomberg and Sustainalytics have proliferated in recent years.

BooHoo and ESG

Unfortunately, ESG investments are often easier said than done. Consider the example of the UK-based company, Boohoo. In June 2020, this pioneer of the ultra-fast-fashion retail phenomenon announced a £150m planned executive bonus. In 2019, the retailer waxed eloquent in its 2019 company report about its “zero-tolerance approach to modern slavery”. Yet, shortly after, the company was discovered to be sourcing from a factory in Leicester in which workers were being paid as little as £3.50 an hour (compared to the National Living Wage of £8.72). Just as bad was the fact that workers were not provided with proper protective equipment against Covid-19.

Yet, despite these malpractices, Boohoo had received a double A ESG rating from MSCI — its second-highest ranking — while being awarded a far-above industry average score on supply-chain labour standards in its ESG ranking. MSCI is not alone in ranking the fashion retailer highly, however. A review of nine other different ratings placed Boohoo in the top 25th percentile of more than 19,000 companies considered worldwide.

Ratings shortcomings

How could the rating companies have got it so wrong? The answer: information asymmetry. It appears that all parties involved face different challenges when obtaining and quality of information. To begin with, rating producers and indices deploy their own proprietary methodologies and data to analyse companies. The result of them using different ESG definitions, measurements and weightings for different indicators often lead to conclusions and verdicts that can be distinctly different from one index to another. Furthermore, they rely heavily on information provided by the companies being rated, essentially allowing the latter to feed only favorable data, potentially creating huge biases.

This, in turn, poses problems for the investors. First, without standardization across ratings, it is difficult for investors to compare across the indices created by different providers. Second, the fact that interpretation of data by rating companies can be vastly different, often leaving investors struggle to determine which rating or score would meet their own investment criteria or goals. Another key problem the investors face is that they rely on the rating and index producers to capture the latest information and news and to incorporate them into their ratings.

For the rated companies, even though they can select the information to be submitted, they often suffer from other problems. For example, as the rating criteria and dimensions are determined by the index producers, the companies that are keen to be seen as ESG-compliant are frequently left wondering how to improve their own ratings. There is also uncertainty over whether investors have enough information to recognize other positive – and negative – factors related to their competitors which are unaccounted for in the ratings.

In short, the problems emerge from a lack of clarity, consistency, and transparency of ESG ratings as well as information asymmetry and shortage.

AI to power ESG

One potential means of mitigating these issues is to consistently collect qualitative information quickly in order to reinforce the quantitative data already in use. Up-to-date qualitative data has the ability to not only help investors and rating producers to be much better informed but also such data can also be used to set up key inputs that could be used as the basis to form common minimum standards.

This is by no means merely a theoretical argument – a new and major initiative is underway in Asia to fashion AI into a tool to collect and process qualitative data. The AI-powered solution seeks to help stakeholders mine the vast amounts of qualitative, unstructured data through automation. Until now, gathering and gleaning insights from social media, daily local news, and freshly available reports has been a slow and labour-intensive activity, fraught with inaccurate results.

AI technology is a potential game-changer as it can act as a scalable solution that allows for speedy unearthing, collection and handling of vital information. Algorithm-driven systems can easily and effectively crawl the worldwide web and scrape unstructured data on companies from a range of sources. Subsequently, they can also swiftly parse and convert the excavated data into usable structured ones. This, in turn, allows for curated output that is valuable for all parties involved, thereby drastically mitigating the information asymmetry problem. In addition, using natural language processing technologies to perform analyses that capture sentimental, contextual and semantic factors embedded in the collected data, it will be possible to discern the tone of the information provided. Such analytical algorithms could be trained to go through a certain type of conversation and identify the tone by comparing the words used to a reference set of existing information.

Something for everyone

The benefits can be huge for all the parties in question. Investors can hence better comply with ESG requirements and make more informed decisions by incorporating ESG data into their investment strategies, for example by implementing negative/positive screening. On the other hand, rating producers are in a better position to identify and control ESG related issues and risks such as improving their company operations and supply chain due diligence. As for the rating and index providers, real-time signals can offer early warnings and timely indicators, enabling them to produce more accurate updates. Furthermore, these providers can expand the scope of analysis using AI-derived information to complement their current quantitative methods.

A chance to make a difference?

While the use of AI to drive ESG is still in its nascent stage and the results of the ongoing efforts in Asia remain to be seen, history is filled with examples of how technologies have helped attain social goals and create a better society. It is very much hoped that by investor empowerment, particularly those who are ESG-orientated, it will be possible for ESG to be a well-respected and common business practice instead of just another fad or slogan advocating sustainability. Just as with fire, AI can be a bad master but a very good servant. Using AI in the right way can no doubt help with our endeavor to raise ethical standards.



Four ways traders can manage risk



By Dáire Ferguson, CEO at AvaTrade


Understanding the markets in which you are trading is incredibly important to optimising profit, as well as manging risk and loss. While trading can be incredibly lucrative, it can often be difficult to judge which way the market will move – especially when executing shorter-term traders, where unknown factors can cause unexpected movements. Being aware of the risks is vital to avoid unnecessary losses and to optimise the trading experience.

Dáire Ferguson

There are several techniques that can be employed to make sure the risks associated with trading are controlled, rendering the trading experience smoother and more enjoyable. From beginners to experts, having these tactics in your arsenal will enable traders to be savvier, and more confident.


Understanding the risks

To really be able to manage risk, it is imperative to understand the two types of trading risks.



Leverage is where traders stake only a percentage of the value of the underlying asset they wish to trade on but accept exposure to the full value of the profit and loss that comes with the asset’s price changes. This enables traders to take sizeable positions for comparatively less trading capital, thus providing an opening for big wins and substantial rewards.

However, with this comes the risk of similarly significant losses. As an example, if a trader opens a £100 trade on an asset worth £1,000, using leverage of 10:1, this means that if the assets value increase by 10 per cent, the trader’s money will be doubled. But if it drops by just 10 per cent, the trader will lose all their stake. This balance of high risk and high reward necessitates careful management. Leveraging typically applies to purchasing and trading contracts for difference (CFDs).


Volatility is characterised by unexpected fluctuations in the prices of assets and is defined as the rate at which pricing rises or falls given a particular set of returns. Volatility applies to all assets, but the regularity and size of price changes differs hugely across different asset groups. In fact, in some markets, volatility is actually predictable. The cryptocurrency market is well known for its fluctuations, characterised by frequent and, often, significant changes in price.

There are scenarios in which volatility can be desirable for some traders as it fosters greater profit margins. However, it also sharply increases the potential for large losses. Nevertheless, there are a number of ways to spot incoming market fluctuations. These include economic volatility, geopolitical tensions, and changing policies.


Managing the risks


Choose the right broker

So, what can traders to do manage these risks? The first step is to choose the right broker. Having the right broker can go a long way to limiting the risks that come with trading, including managing counterparty risk. For example, when you purchase CFDs, you are purchasing a contract with a broker – not the asset itself. Therefore, traders must be 100 per cent certain in the knowledge that the broker they’ve chosen to operate with is capable of making good on the value of that contract.

Traders who are just starting out on their trading journey should look to open a trading account with an established name that is well regulated in a variety of jurisdictions. Higher-quality brokers will generally have a wider range of risk management tools and offer better features, which will allow traders to manage the buying and selling of assets in a better, more sophisticated manner.


Take out protection on riskier trades

For new traders, or those who are looking for extra support, it is worth considering taking out protection against losses for a set period of time. Certain brokers offer risk management tools that provide thorough protection against such losses. These tools generally require just a small fee, not unlike the premium on an insurance policy. These risk management tools allow users to stay in the trade, riding out any short-term drops in value and benefitting from a positive overall momentum of the position. Therefore, if the market moves in a different direction to what was originally expected, users only lose the cost of purchasing the protection and can recover their losses.


Set-up stop-loss orders

Another form of protection against losses is through a stop-loss order. This is an instruction that is executed automatically when certain conditions are met. Therefore, stopping losses from falling below a certain point, and setting a limit on how much an investor can lose on a trade. In the case of a stop-loss order, the position is sold at a predetermined rate – below the current market price for a long position, or above the current market price for a short position.

Stop-loss orders remove the user from the trade at a set price drop. In comparison, risk management tools allow the user to ride out any short-term drops in value, with the potential to benefit from a positive overall momentum of the position.


Manage the capital-to-trade ratio

One simple way traders can reduce the risk of accumulating excessive losses is to keep their capital-to-trade ratio under control. This is the amount of capital left exposed to losses in trades compared to the total amount of capital traders have available to themselves.

A sensible rule for traders to follow is to not exceed a capital-to-trade ratio of 10 per cent, and not to risk more than two per cent of the overall capital on a single trade. This doesn’t mean always taking very small positions – it means traders should hedge their risks on whatever positions they choose to take.

It is important that before traders even begin to trade, they make sure that they understand the risks they face. Once they have taken the time to do that, they can begin to contemplate these four ways to manage those risks and then start trading. This is an exciting time to be entering the world of trading, and these considerations should ensure that the trading experience is as enjoyable and profitable as possible.




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Out of office, home and away, moving up, moving on; when security goes AWOL




Steve Bradford, Senior Vice President EMEA, SailPoint 


The financial services industry has one of the highest rates of insider data breaches, costing on average $21.25 million in the past year alone. Whether it’s an employee acting with malicious intent, or through accidental data mishandling, staff have access to sensitive information and systems that make them a constant vulnerability. And this threat only escalates when staff go on the move.

With the summer holiday season upon us, thoughts will be turning to well-deserved time off, travel and downtime. However, for many, especially in the financial industry, the notion of waiting until the summer months to sample a new life was not feasible. In the period following Covid, the industry has suffered at the hands of the Great Resignation as burnt-out employees left for new roles. As a result, research from PwC suggests that financial services leaders have had to prioritise employee retention amid the swathes of staff exiting.

This exodus is not just a threat to the workforce itself. It also results in greater threats to resilience, security and compliance. Ensuring that the doors to the organisation’s data are appropriately locked behind them is vital whenever employees are on the move. When a staff member leaves a bank or financial institution, security leaders must ensure they have not inadvertently handed over the keys to the safe as a leaving present. Revoking any and all access and privileges to company data must be a priority.


Don’t leave the door ajar 

Disorganised, ill-managed and manually-processed access requirements and identity management protocols are an open invite for security breaches.

However, it is not just those leaving for good that pose a threat. Recently promoted your long-serving payroll manager to a longed-for role in financial oversight? That positive move could result in entitlement creep, where the permissions to data, apps, information and systems she enjoyed in payroll follow her to her new home.

Permission creepers are those staff who collect permissions and access rights as they go through their career, picking up credentials to systems and data as they go. Of course, to restrict the opportunities for hacking, insider threat or illegal or incompliant activity, permissions should only be granted when relevant and required for an individual’s job. However, too many companies allow permissions to creep by not taking a proactive approach to access. This can result in toxic permissions combinations, where employees are granted inappropriate access to the systems, making fraud and error far more likely.

Even a simple summer holiday can provide an open-door opportunity. We are all conscious about signaling to would-be home burglars that we are going away on holiday, and we will take steps to protect our property in our absence. The same principle applies to businesses with staff out of the office on vacation – potentially logging in from insecure locations or signaling to cybercriminals that their attention is elsewhere.

The results of leaving the door ajar are costly. According to the IBM Cost of a Data Breach Report 2021, the average cost of a data breach in the financial sector is $5.72 million.

Permissions creep, unrevoked access and unmanaged identity provide the perfect conditions for the insider threat to propagate. As Gaurav Deep Singh Johar, of the Information Systems Audit and Control Association explained, “While these challenges are present in any institution, insider threats pose a greater risk for banks. There is a big reputational impact, thanks in part to increasing regulatory oversight.”


Don’t let permissions security set sail into the sunset

Financial organisations are complex landscapes, with labyrinthine corporate structures and siloes that cast a dark shadow over access and identity visibility. However, identity security technology is moving fast. Now, automated systems powered by AI and machine learning mean that permissions can be automated and access granted on a need-to-know basis, based on individuals’ employment status, roles, and responsibilities.

An automated system will quickly track down and disable ex-employees’ accounts and automatically halt permissions creep as employees move about the organisation.

The same technology can now also be even more diligent than that, monitoring access requirements based on any change in the workforce, like people being out of the office.

The evolving variety and fluctuating workforce mean that the insider threat can only be met with automated, streamlined identity security that moves as quickly as employees themselves. Without intelligent, streamlined identity governance, banks cannot ensure they are in a state of compliance, nor ensure cybersecurity in real-time. They also miss out on opportunities to improve operational efficiency and reduce the risk of fraud and error. Automation also ensures the accuracy and completeness of data sets so critical for keeping on top of compliance and delivering critical services.

As financial workforces are on the move, home and away and to pastures new, now is the time for banks to give identity security its time in the sun. Do not let shifting sands collapse the walls around you. Wherever your employees are coming from and going to, robust security and sustained compliance start with automated identity management.


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