How AI is Changing Impact Investing
Artificial Intelligence (AI) has been dominating the business world’s conversation recently, revolutionizing industries and business functions with sweeping force. But how is AI changing the way practitioners manage and evaluate impact investing? Where can AI help impact investing? The possibilities are continuously expanding. Simply put, AI specializes in matching traits and compiling information that allows technological systems to create inferences and data-driven decisions. Impact investors seek to make sustainable investments that generate both profit and purpose, with investments that specifically align with their goals and values. As a result, the options presented to investors using AI will be more tailored towards the tastes and preferences of the individual investor. On the other hand, sustainable companies and funds are reaping the benefits of enhanced visibility to value-aligned investors.
More importantly, the Environmental, Social and Governance (ESG) sector can be challenging to invest in. Companies and investors are increasingly allocating funds into this space as sustainability becomes an increasingly important investment criterion. However, because of the nascent nature of the industry, investment due diligence is incredibly important to screen out companies that are not financially viable and enterprises that do not create an adequate impact. This is why new investment funds are now focusing specifically on the ESG sector.
Searching for Sustainable Opportunities
Investment funds focusing on ESG opportunities will only become more advanced in their approach as sustainability continues to grow as a topic of concern. As a result, some funds are utilizing Artificial Intelligence (AI) to find ESG-focused companies. For example, Ecofi, a French firm, recently launched a fund that is managed by AI. In turn, it has led to more informed equity investments that are socially responsible. Christophe Geissler, chairman of the French asset manager Advertise, said:
“Our algorithm looks at all the variations in ESG scores since 2012. It understands variables such as sector biases and how a mining company will always have a higher E-score than a bank…We look at a company’s carbon footprint and its tax responsibilities compared to the index and find correlations between ESG scores and price returns.”
Using data to make effective and informed decisions about ESG-focused companies is becoming increasingly popular. Machine-learning methods are more advanced than traditional approaches, like examining company reports, as they can pull data from a wider range of sources. These methods can also solve various problems that arise when company reports are self-produced. For example, some companies may perform well on some ESG factors, while falling short on others. A firm could be achieving exceptional results in some areas of pollution, but be hopeless when it comes to human rights. AI can analyze all variables and effectively match investments to clients and their preferences. However, there are still legitimate concerns. If the human element is not present and AI is doing all the work, mistakes can be made. This is why investment funds using AI, such as Ecofi, are providing stock picks only after individuals have reviewed them. Another problem revolves around AI’s perception of sustainability. For example, Ball Corporation, an aluminum can maker, scores very poorly with ESG rating agencies. Despite this, the company has become the first to develop an aluminum cup. Therefore, AI investment bots could give companies such as Ball Corporation a poor score simply because they produce cans. These cans, however, will be in use for years to come due to their re-usability. This means the Ball Corporation should be given a better score for sustainability, but AI has actually punished the company. The question remains is whether or not AI will completely manage sustainable investment choices. For now, the human element still makes a significant difference.
In any industry, bias remains a challenge that must be dealt with when developing AI algorithms. In essence, an AI algorithm is only as unbiased as the objective it was built to optimize, and the data it was trained on. The limited amount of historical data for various impact investing cases can increase the chance of biased data. However, establishing better objectives and developing a stronger understanding of training data can help mitigate possible biases in AI models. In all circumstances, cleaning and labelling data is a crucial step to successfully using AI in sustainable investing and one that requires human knowledge.
Distilled Impact
Distilled Analytics is a tool that could revolutionize impact investing. Through “social physics,” the use of new computational social science from MIT, Distilled Impact can efficiently analyze vast amounts of public information to measure ESG data. After David Shrier left MIT, he applied the school’s mission to his AI startup, Distilled Identity. His goal is to disrupt impact measurements for companies and investors with the company’s new product, Distilled Impact. In Shrier’s words:
“Distilled IMPACT provides investors with objective, quantitative, 3rd-party-sourced (vs self-reported) AI-driven assessments of the non-financial impacts and risks of their investments.”
Shrier’s system is designed for investors that seek both profit and impact. Unlike other methods that pull data only on public companies, Distilled Analytics can provide data on 260 million public companies along with many private companies as well. The efficiency of this system saves time for both company executives and investors as it does not require companies to generate any new data. Shrier says the system’s strength is synthesis: the ability to take vast amounts of complicated information and condense them into a unique opportunity. All in all, impact investors now hold the ability to judge opportunities for themselves using Distilled Impact.
“With the advent of things like the Internet of Things and other ubiquitous data networks, we were able to come up with third-party, credible, quantitative data sources and new kinds of analytics that leverage artificial intelligence and machine learning to measure instead of guess,” Shrier says.
At the center of innovation and sustainability, AI can make a significant difference in impact investing — especially while considering all the environmental, social and governance opportunities and risks in investing. While AI can unveil new data and information for investors seeking sustainable opportunities, identifying misleading information will remain a key challenge. Thus, it seems that human ingenuity will remain a vital piece in impact investing in the near future.
By Ashraf Abouchacra