Poste du Conseil: Trois utilisations pour l’intelligence artificielle dans les services financiers En 2024

Le ML peut nous permettre de former les AI pour devenir les chiens de sang des flux financiers mondiaux.

Christian Frahm is the CEO of United Fintech.

AI is everywhere.

From boardrooms to classrooms to dining tables, 2023 made AI the hottest topic in virtually all corners of enterprise, education and private life. It’s not even an exaggeration to say that A and I were the two most important letters of the year; in fact, Collins Dictionary recently made it Word of the Year 2023.

It’s remarkable to think that the first version of ChatGPT was released by OpenAI in November 2022 and just a year later, its norm, not a novelty, with Meta, Microsoft and other members of the “Magnificent 7” having already promptly launched their own Generative AI counterparts (here’s a timeline from McKinsey & Company).

And on that note: Just imagine what 2024 will bring about—and dare one even think 10 years ahead?

The point is that we may be on the verge of a technological breakthrough comparable to the invention of the microchip. This breakthrough could help leapfrog humanity from Artificial Narrow Intelligence (ANI) hurtling towards Artificial General Intelligence (AGI). AGI is comparable to human-level independence and intelligence (here is a great deep dive on the different forms of AI from before AI was a thing), and such a breakthrough requires major adjustments and adaptations on all fronts, including in Financial Services (FS).

Thus, AI was not surprisingly the main theme at this year’s Singapore Fintech Festival where I had the pleasure of speaking this fall—yet it occurred to me during my stay in Asia’s financial capital that abstract talks about the prospective future of AI currently outweigh concrete use cases and guidelines on application in FS. Many have yet to understand the basics of AI, its subsets (Machine Learning (ML), Deep Learning (DL) and Generative AI, their interrelation and—namely—their applications, which brings me to my second point.

Figuring out how and where AI may fit into your FS organization will be one of the most important corporate challenges to tackle in 2024.

I say this not only as a qualified forecast but because it remains one of the most frequently asked questions that I receive on almost a daily basis from executives with global banks, asset managers and their likes:

What will be the best use cases of AI in FS, and how do you stay ahead of the curve?

To get concrete, I’ve divided the lay of the land into three main areas:

Internal Workflow Refinement

Generative AI has proven to be a remarkable tool for creating efficient language solutions for virtually all tasks relating to business analysis, compliance and legal matters, financial reporting, sales forecasting and content creation. This has been successful in all verticals of sales and marketing; including creating direct mails and generic email templates, graphical material, marketing content, product descriptions, sales pitches, social media assets, videos for web platforms and even entire websites, all with the possibility of being instantly translated into multiple languages. The possibilities with Generative AI are as endless as they are efficient in refining business processes whilst saving man hours.

External Product Enhancement

As financial services are heavily reliant on data, AI creates a broad array of use cases for processing data pools to enable swift new solutions that can significantly impact fintech product development.An example of this is risk management where AI can assist in running smooth simulations and scenario analysis to estimate risks and/or detect violations.

Furthermore, one can use AI to help identify fraud via unusual transactions and/or movements in a vast ocean of data by detecting and flagging unusual patterns. ML can enable us to train AIs to become the bloodhounds of the global financial streams.

Organizational Optimization

Lastly, the two-edged sword of organizational optimization becomes relevant to factor in as addressing the points above has the potential to contribute significantly to the growth of FS businesses whilst cutting down organizational needs—and, thus, costs. The challenge will be to perfect AI (i.e., to make people more valuable without creating overt redundancy or a threat to humans—the latter being a topic becoming increasingly more relevant). This may not just be the biggest challenge for AI in 2024 and for FS particularly, but for much of the foreseeable future in wide branches of industries. However, I leave that to more qualified people to figure out.

The common denominator to keep in mind regarding all of the above is predictability—as anything based on volumes of data can be managed by models of AI to predict the right or most plausible outcome (as long as you’re able to use different databases and not mix up proprietary data, that is).

Given that AWS cited Fortune Magazine during an ASEAN summit talk about Generative AI and ML saying that more data will be created in the next three years than in the preceding 30, the future is looking to be abundant with data. In other words: The use cases for AI in FS are likely to be plentiful and I look forward to 2024 revealing more of the scope of it.

The information provided here is not investment, tax or financial advice. You should consult with a licensed professional for advice concerning your specific situation.

Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?


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