Machine learning can help investors tackle fast fashion ESG issues

Investor concern in fast fashion is rooted in the environmental impact the industry has, the involvement of modern slavery and exploitation, as well as the murky practices hidden deep within global supply chains.

For asset managers and institutional investors alike, considering the implications of fast-fashion holdings in a portfolio involves a strenuous consideration of multiple ESG factors.

However, a recent Pensions Expert Twitter poll indicates that such practices are not well established, and many do not know where to begin.

Machine learning and AI are proving they can support the transparency and scrutiny required to drive a rise in ESG standards across the board, including hard-to-assess industries such as fast fashion

Diana Rose, Insig ESG

Of the respondents, 60 per cent said they do not consider the consequences of fast-fashion investments, as they “do not know how”. Meanwhile, 20 per cent said they are creating a process to allow for the ESG implications of the fast-fashion industry to be considered, while the remaining 20 per cent were resolute in saying it is not needed.

A problematic industry

The common response of organisations lacking the knowledge, tools or capabilities to findings “does not surprise” Gordon Tveito-Duncan, founder of GaiaLens, a fintech aiming to improve ESG transparency through the use of machine learning.

Tveito-Duncan says the challenge of considering fast fashion through an ESG lens stems from difficulty in identifying problems within a supply chain, “especially when you consider that fast-fashion companies’ supply chains may be sprawled all over the world and the people that make their products are often not directly employed by the companies”, he notes.

On the domestic front, a lack of regulatory oversight is a “concerning indication of what goes on deeper in the supply chain”, adds Diana Rose, ESG research director at Insig ESG.

“This is why it is so difficult to uncover ESG issues in the fast-fashion industry. Its most significant environmental and social impacts will be upstream, out of sight and still broadly optional to disclose,” she says.

But the consequences of these issues often start and end in the consuming areas of the world. Russ Mould, investment director at AJ Bell, points towards one such instance — the skyrocketing demand for organic cotton.

In 2020, the Global Organic Textile Standard obtained evidence of “systematic fraud” in the organic cotton certification system in India. In this one case, 20,000 tonnes of incorrectly labelled cotton were uncovered. The material would then be distributed through supply chains, resulting in consumers and businesses believing that the sustainable qualities of the fabric were legitimate.

The fraud, a result of western demand for sustainably produced fabrics, is a clear example of opaque supply chains acting as a “constraint” for asset managers, investors and consumers alike.

“There is only so much supply to go around,” Mould says, although that is prompting efforts to come up with alternatives.

“Never bet against human ingenuity,” he adds.

Investor action

For institutional investors, being able to see a complete picture of a fashion brand’s ESG credentials remains close to impossible, but there are still steps that can be taken to ensure that the implications are properly considered.

A good place to start is to “fundamentally consider whether fast fashion is compatible with the global transition to a net zero and socially equitable world”, Rose says.

She suggests that investors need to ask three robust questions of their ESG values, and whether they align with the consequences of fast-fashion holdings.

“In the short term, ask is it possible to make the margins that drive fast fashion with a legally compliant UK supply chain? In the medium term, might tighter regulation disrupt the business model? And in the longer term, what does the apparel industry’s own sustainability transition look like and who is leading the way?”

Indications of good practice in this area come down to governance and transparency, Rose notes. Factors such as strategic leadership, oversight and reporting of the supply chain, robust sourcing policies and adequate internal processes are all indicators of organisations that have prioritised ESG.

She adds that engagement with non-governmental organisations “such as Transparency Pledge, Labour Behind the Label, and the Better Cotton Initiative may demonstrate a brand is willing to be held to account to industry benchmarks”.

Similarly, Tveito-Duncan says simple assessments can be made of fast-fashion brands’ human rights policies and subsequent violations. For example, Boohoo was named and shamed in 2017 for labour standards violation — three years before the story garnered major attention.

But the emergence of artificial intelligence and machine learning is providing asset managers and institutional investors with a valuable tool to filter through vast amounts of information, and create frameworks to better consider the implications of fast fashion through an ESG lens.

Rose says: “Machine learning and AI are proving they can support the transparency and scrutiny required to drive a rise in ESG standards across the board, including hard-to-assess industries such as fast fashion.

“In the evolution of ESG best practice, the benefit of access to powerful technology directly by decision-makers is playing a part.”