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Artificial Intelligence, a friendly tool? BIG DATA, MACHINE LEARNING and ARTIFICIAL INTELLIGENCE

Aggiornamento: 1 set 2021

⌘ Overview:

Today's Overview resumes - after the summer break - in English.

We can extend the topic to Colleagues and Clients who are less familiar with the Italian language.

The aim is to involve each other in reflecting on the applications of artificial intelligence (AI) in the field of Business and Company life.

Artificial Intelligence (AI) techniques - as we can read in the OECD reports - are being increasingly deployed in finance, in areas such as asset management, algorithmic trading, credit underwriting or blockchain-based finance, enabled by the abundance of available data and by affordable computing capacity. Machine learning (ML) models use big data to learn and improve predictability and performance automatically through experience and data, without being programmed to do so by humans. We can see examples of AI application by OCED report below [ARTIFICIAL INTELLIGENCE, MACHINE LEARNING AND BIG DATA IN FINANCE © OECD 2021 ] :

The deployment of AI in finance is expected to increasingly drive competitive advantages for financial firms, by improving their efficiency through cost reduction and productivity enhancement, as well as by enhancing the quality of services and products offered to consumers. These competitive advantages can, in turn, benefit financial consumers by providing increased quality and personalised products, unlocking insights from data to inform investment strategies and potentially enhancing financial inclusion by allowing for the analysis of creditworthiness of clients with limited credit history (e.g. thin file SMEs).

At the same time, AI applications in finance may create or intensify financial and non-financial risks, and give rise to potential financial consumer and investor protection considerations (e.g. as risks of biased, unfair or discriminatory consumer results, or data management and usage concerns). The lack of explainability of AI model processes could give rise to potential pro-cyclicality and systemic risk in the markets, and could create possible incompatibilities with existing financial supervision and internal governance frameworks, possibly challenging the technology-neutral approach to policymaking. While many of the potential risks associated with AI in finance are not unique to this innovation, the use of such techniques could amplify these vulnerabilities given the extent of complexity of the techniques employed, their dynamic adaptability and their level of autonomy.

For this reason I found the aforementioned OECD report interesting [you can download the complete document here].

Scarica PDF • 4.04MB

The report can help us, besides the policy makers, to assess the implications of these new technologies and to identify the benefits and risks related to their use. It suggests policy responses that that are intended to support AI innovation in finance while ensuring that its use is consistent with promoting financial stability, market integrity and competition, while protecting financial consumers. Emerging risks from the deployment of AI techniques need to be identified and mitigated to support and promote the use of responsible AI. Existing regulatory and supervisory requirements may need to be clarified and sometimes adjusted, as appropriate, to address some of the perceived incompatibilities of existing arrangements with AI applications.

⌘ La Practice:

So, what to do? Utile considerare sempre il contesto in cui AI viene applicata e proposta. Un esempio è unire la forza e l’esperienza dei percorsi finanziari/di Corporate Governance allo strumento AI.

Un esempio di utilizzo pratico di questa nuova filosofia in un contesto di Marketplace del Business in .

Un esempio di metodo per capire se e dove la tua Azienda si può avvalere di specifici strumenti AI in

If you like to exchange ideas, needs and deepen you can activate 30 free minutes of Consulting at the following link

or write to me at

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