SESAMm Incorporates Generative AI to Enhance ESG Risk Mitigation and Process Efficiency in the Finance Sector

FOR IMMEDIATE RELEASE

PARIS, France - July 12, 2023 - SESAMm, a leading player in financial technology, announces a transformative initiative to incorporate Generative AI solutions into its operations and product offerings. This strategic move is geared towards assisting financial firms in enhancing risk mitigation focused on ESG controversies and streamlining their processes.

The implementation of Generative AI follows a three-pronged strategic approach. This comprises the integration of large language models into their tech stack, the development of a client-facing conversational agent, and fostering a culture of AI utilization across all teams.

"With Generative AI, we are not only enhancing our internal processes but also focusing on the development of new features that redefine industry standards," stated Sylvain Forté, CEO & Co-founder of SESAMm. "These include intuitive dashboards, automated ESG/SDG event analysis tools, and a client interaction chatbot - all created to streamline data interaction and boost efficiency in risk management."

The integration of Generative AI has significantly enhanced SESAMm's product functionality already. This includes quicker and more intuitive interaction with data and introducing new features, such as ESG/SDG event summarization and automatic competitor searches for public and private companies.

SESAMm is also employing Generative AI for advanced risk mitigation. "Our innovative approach provides our clients a virtual team of ESG analysts and experts for detecting risk and ESG controversies, enhancing their risk mitigation strategies in a robust and comprehensive manner," Forté added.

SESAMm is preparing to launch a suite of AI-powered features later this year. "These new features, powered by Generative AI, reinforce our commitment to developing solutions that enhance risk mitigation and streamline processes for financial firms," Forté emphasized.

To explore more about SESAMm's Generative AI solutions and how they can boost your firm's operations, watch the video below:

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Also, make sure you join our upcoming webinar, where Sylvain Forté will discuss live the future of fintech with Generative AI and how SESAMm is incorporating Generative AI into its processes and products. To register for the webinar, click here.

About SESAMm

SESAMm is a leading artificial intelligence and NLP (natural language processing) technology company serving global investment firms, corporations, and investors, such as asset managers, banks, private equity firms, hedge funds, and index providers. With over 100 employees and six offices worldwide, SESAMm celebrated its 9th anniversary in 2023.

Contact: contact@sesamm.com

The AI field is growing, and whether good or bad, people are doing more than talking about it; they’re using it more than ever. However, despite this increased use, I’ve noticed that, for some, their perception tends to alternate between false and too-high expectations of AI.

One case, in particular, was in 2021, Gartner placed natural language processing (NLP) at the top of its list of loaded expectations in terms of the Gartner hype cycle. As a result, many expected a potential “winter of AI,” so to speak. Yet, in 2022, we discovered the potential that we haven’t even touched on the true value AI could deliver.

Will there be a “winter of AI,” and are expectations bloated?

No, I don’t think so. As the past year has shown us, AI still has more to offer, a pocket of value that we have yet to see. I believe that while many people now accept that AI will be a transformative force—thanks to the fast democratization of large language models—our society hasn’t yet fully considered the actual changes it will make by lowering the barrier to access intelligence globally.

Progress in image generation, analysis, and computer vision—think autonomous driving—has leaped and bounded in the past year, and so has the progress in NLP, particularly in the natural language understanding (NLU) and natural language generation (NLG) aspects. We’re at a tipping point that will likely transform our world in the same way that the internet has.

Tipping point for AI

Today, we’re seeing the development of natural language processing through large language models, such as with the emergence of ChatGPT based on OpenAI’s large language model version GPT-3.

Astounding fact: ChatGPT’s growth in user adoption skyrocketed past one million users within a week of launching. In comparison, no other tech company has reached this feat in this short of a time frame. But the adoption rate is only part of it.

This advance has profoundly affected creative jobs because this might be the first time an AI generative system can create high-quality content. In public mode, users have tapped ChatGPT to do everything, from generating basic reports and ideas to writing lectures and producing code.

With a high adoption rate comes great opportunity. Any startup seeing this level of success could become the most funded project ever. And more, there’s revenue. OpenAI, as the example, could make one billion dollars by 2024, according to a report via Reuters.

On the other side of the same coin, however, there are greater risks due to AI generative system advancement. For example, with AI assistance, human hackers can develop more sophisticated phishing campaigns—hacking mechanisms based on social engineering.

Competition, specificity, and focus for AI advancement

Despite the risks, we still haven’t seen what’s yet to come with generative AI. GPT-4, for instance, is rumored to launch in 2023. I believe it will be a massive improvement over GPT-3, which is already mind-blowing.

And on the point of NLG and these large language models, there’s a lot that’s feasible in process automation. For context, creative content gets the most attention; it’s the area that makes more headlines. But I would also watch advancements in technical content and automated code generation, for example.

Process automation

Because of today’s AI advancements, it’s now possible for tools like ChatGPT to generate near-ready-to-use source code. That means instead of only being fun to play around with, these are becoming enterprise tools, making it possible for developers to automate technical tasks at scale.

NLP—specifically natural language understanding, which SESAMm works on—is not untouched by these applications. Many of these large language models can perform zero-short learning, which means NLU can be performed without pre-training, a huge advance in this industry. However, zero-short learning is insufficient for many advanced sentiment and ESG analysis tasks. We still need additional data sets to fine-tune the data for a specific purpose.

What does this mean for the natural language generation sector? Many startups—especially anything around chatbots—have folded, some just in Q4 of 2022. ChatGPT’s success means it’s solved and replaced the need for many of them, and basically, anything content creation on the B2C side has and will struggle.

Defensive edge

Otherwise, things are looking good in our sector. For example, at SESAMm, we’re focused on what I call “last-mile AI.” In our specific business application, you can’t bypass the need for a data set because we’re trying to attain a precise result for specific, often risk-related applications. Open-source large language models like GPT-3 and BERT can get you mostly there, and that’s fine for general purposes. But for “last-mile AI” applications, there’s a lot you can’t do without additional work.

And here lies what I think is one of SESAMm’s defensive edges: the “last-mile AI.”

Instead of finding ways to protect its algorithms, the AI business community would do better to defend its use cases because the algorithm’s value will decrease progressively. In contrast, the value of a use case’s purpose and the data set used to achieve the use case will grow.

Competitive edge

Computing power and the resources it takes to train large language models remain challenging to applications like OpenAI. It takes electricity, heat, and money to train these models, and AI has an environmental impact. So far, we’ve justified this cost in the name of optimization—meaning that we put in this extra cost upfront so that the likely efficiency will offset or reduce that cost later—but it’s still a cost to incur.

AI companies, especially those in the NLG space, will do well to find their competitive edges, areas optimized for a specific purpose like “last-mile AI.” Companies like OpenAI will likely continue to optimize their models for quicker responses but don’t necessarily have the problem of solving for a specific use case.

At SESAMm, for instance, a big challenge and expertise we developed in-house is inference time—or how quickly we can apply the model to an article or an individual sentence. Because we’re processing so much live content, the more time it takes to process—milliseconds multiplied by a billion—the more costly it is.

Our data lake currently holds over 20 billion articles, messages, etc., from over 14 years, and we add 10 million more daily. That’s a lot of content to analyze. But we make it so our clients can access the data within seconds.

The need to optimize models for fast inference and adapt to deep industry-specific use cases will remain one of the key reasons companies will have to continue re-training their own models. That doesn’t mean large language models don’t add value here. Their open-source versions simply become an impressive building block for any NLP application and accelerate the rate of innovation and productivity in the whole field.

My summary thoughts on AI for 2023

When Google launched BERT in November 2018, we quipped that Google had open-sourced this system as a joke because no one could put it into production because BERT was so big. Many companies didn’t have the computing capabilities to do anything with it at the time. Now we do.

This year, Google did it again; they released a model that’s even bigger than GPT-3. Of course, almost no one besides Google can put that model into production now. But my point is that there will always be computing, resources, and other challenges to making AI advancements. That’s why I think AI companies must focus on defensive and competitive edges.

Regardless of the challenges, I see good things happening in the NLU space being massively improved by large language models. I see improvements as we incorporate these models today compared to deep-learning models trained from scratch a few years ago. I also see a significant decrease in the amount of data we need to fine-tune results, reaching and focusing on the final client use case more quickly.

From a natural language generation perspective, I believe large language models will transform the world. And I’m really excited about this era because this transformation supports my deepest purpose, leveraging AI to accelerate innovative decision-making. We do this by giving decision-makers access to technology that analyzes research content, news, and discussions. And if we increase the rate of innovation or the quality of decision-making by 10% globally, the impact could be huge for all industries: healthcare, finance, fashion, you name it. Industry leaders can make better ESG and SDG choices that will affect our world on a grander scale.

2023 will be an exciting time for AI, specifically for NLG and NLU. Of course, we’ll continue to see AI innovations. But more importantly, leaders will have better insights to make better decisions, creators will create more—and more complex—content, and overall, the applications will become more specific to solving the needs of particular use cases.

Here’s to the new era of AI in 2023. Cheers!

About SESAMm

SESAMm is a leading NLP technology company serving global investment firms, corporations, and investors, such as private equity firms, hedge funds, and other asset management firms. SESAMm provides datasets and NLP capabilities through TextReveal^® to generate alternative data for use cases, such as ESG and SDG, sentiment, private equity due diligence, corporate studies, and more. With access to SESAMm’s massive data lake, comprised of 20 billion articles and messages and growing, its clients can make better investment decisions.

In a historic move, the agreement negotiated at COP 28, after tense negotiations, marks a significant turning point in the fight against climate change. For the first time, an international text explicitly calls for a reduction in the use of fossil fuels, symbolizing notable progress and a significant advance. However, the devil is in the details: the challenge was to reach a consensus among all participating states. The United Arab Emirates, in particular, played a key role, demonstrating its influence on the international stage by adhering to this notion of "transitioning away from fossil fuels."

NGOs and countries of the South, especially island states, would have preferred a firmer commitment towards a complete phase-out of fossil fuels. The current wording leaves room for interpretation and does not specify whether the reduction in fossil fuels should be relative or absolute.

At the heart of this climate battle lies a crucial distinction: it's not just about reducing the relative share of fossil fuels in favor of low-carbon energies but completely eliminating them. Indeed, a state can reduce the proportion of fossil fuels in its energy mix simply by increasing the use of renewables faster while continuing to increase its absolute consumption of fossil fuels, which would not solve the climate problem and could even worsen it.

It is also important to note that natural gas, despite its name, is a fossil fuel. This agreement considers it a "transition energy," part of a "just, orderly, and equitable" transition.

Beyond these semantic debates, the agreement addresses other crucial points, such as tripling renewable energy production capacities by 2030 and improving energy efficiency. It also highlights the development of nuclear energy, which, despite its drawbacks, has the significant advantage of being low-carbon.

Another notable aspect of this agreement is validating the fund for loss and damage, an idea mentioned at COP 27 in Sharm el-Sheikh. This fund, supported by the countries of the North, aims to cover the negative impacts suffered by the countries of the South. Although contributions are voluntary and potentially insufficient, they represent a step forward.

On the sidelines of the main agreement, several major powers, including the European Union, the United States, Indonesia, and Vietnam, committed to accelerating the phase-out of coal, a major source of climate pollution.

A striking fact of COP 28 is the increased presence of fossil fuel lobbyists, with 2,456 accredited representatives, four times more than the previous year. This presence is comparable to that of large national delegations and exceeds that of the countries most vulnerable to climate change.

It has been reported that the COP president, Mr. Sultan Al-Jaber, has made remarks questioning the scientific basis linking the transition away from fossil fuels to the goal of limiting global warming to 1.5°C. These comments appear to disregard the detailed findings of the IPCC reports, which provide an alternative and more alarming scientific perspective.

However, he clarified that his comments were about the challenges of transitioning away from fossil fuels while ensuring sustainable development. His stance, while acknowledging the complexities, does not directly oppose the IPCC reports' findings on the necessity of reducing fossil fuel use to mitigate climate change.

In conclusion, COP 28 stands as a landmark event in the global effort against climate change, balancing the urgency of action with the complexities of international consensus. While it pioneers in explicitly calling for fossil fuel reduction, the agreement also acknowledges the challenges of a full transition, especially from coal, particularly in the context of sustainable development. This nuanced approach, coupled with commitments to strengthen renewable energies and the loss and damage fund, reflects a pragmatic yet hopeful stride towards a more sustainable future. The presence of varied interests, including fossil fuel lobbyists, underscores the ongoing dialogue and debate that will shape our collective response to the climate crisis.