L'integrazione dell'Intelligenza Artificiale (IA) sta provocando una trasformazione significativa nel panorama finanziario. Questo abstract fornisce una panoramica completa dello stato attuale dell'adozione e dello sviluppo dell'IA nel settore finanziario, facendo luce sulle sue diverse applicazioni e sulla traiettoria della sua evoluzione. L'IA è stata ampiamente adottata in vari settori della finanza. Il trading algoritmico, ad esempio, ha rivoluzionato le dinamiche di mercato utilizzando algoritmi sofisticati per analizzare vaste serie di dati ed eseguire operazioni a velocità senza precedenti. Questo ha ridefinito il modo di fare trading. Anche la gestione del rischio ha tratto grandi benefici dall'IA, in quanto è in grado di elaborare i dati storici per prevedere e ridurre i rischi potenziali, migliorando così i processi decisionali. Inoltre, l'IA svolge un ruolo cruciale nel rilevamento delle frodi, utilizzando algoritmi di apprendimento automatico per identificare le anomalie e rafforzare le misure preventive in tempo reale. L'integrazione di chatbot e assistenti virtuali ha portato a un cambiamento di paradigma nel servizio clienti del settore finanziario. Questi strumenti basati sull'intelligenza artificiale offrono servizi personalizzati e risposte efficienti alle richieste, migliorando notevolmente l'esperienza del cliente. Inoltre, l'IA sta rivoluzionando il credit scoring, un aspetto fondamentale delle operazioni finanziarie. Incorporando fonti di dati non tradizionali, l'IA consente valutazioni del credito più accurate e complete. Lo stato di sviluppo dell'IA nella finanza è caratterizzato da continui progressi. I modelli di apprendimento automatico, in particolare quelli che utilizzano tecniche di deep learning come le reti neurali, continuano a evolversi, migliorando l'accuratezza predittiva e le capacità decisionali. Si sta inoltre ponendo sempre più l'accento sulla spiegabilità e sulla trasparenza dei modelli di IA, per rispondere alle preoccupazioni legate alla comprensione e all'interpretazione di decisioni finanziarie complesse. Le istituzioni finanziarie stanno lavorando attivamente per integrare perfettamente l'IA
The integration of Artificial Intelligence (AI) is causing a significant transformation in the financial landscape. This abstract provides a comprehensive overview of the current state of AI adoption and development in the finance sector, shedding light on its diverse applications and the trajectory of its evolution. AI has been widely adopted in various domains within finance. Algorithmic trading, for instance, has revolutionized market dynamics by utilizing sophisticated algorithms to analyze vast datasets and execute trades at unprecedented speeds. This has redefined the way trading is conducted. Risk management has also greatly benefited from AI, as it can process historical data to predict and mitigate potential risks, thereby enhancing decision-making processes. Additionally, AI plays a crucial role in fraud detection, using machine learning algorithms to identify anomalies and strengthen real-time preventive measures. The integration of chatbots and virtual assistants has brought about a paradigm shift in customer service within the finance sector. These AI-powered tools offer personalized services and efficient responses to inquiries, greatly enhancing the customer experience. Furthermore, AI is revolutionizing credit scoring, a fundamental aspect of financial operations. By incorporating non-traditional data sources, AI is enabling more accurate and inclusive credit assessments. The development status of AI in finance is characterized by ongoing advancements. Machine learning models, particularly those employing deep learning techniques such as neural networks, continue to evolve, improving predictive accuracy and decision-making capabilities. There is also a growing emphasis on explainability and transparency in AI models, addressing concerns about understanding and interpreting complex financial decisions. Financial institutions are actively working on seamlessly integrating AI with traditional systems, overcoming challenges related to interoperability and transitioning from legacy processes. However, challenges still exist in the widespread adoption of AI in finance. Data privacy and security concerns, especially considering the sensitive nature of financial information, require careful attention. Financial institutions must ensure that robust measures are in place to protect data and maintain the trust of their customers.
AI in Finance - An overview of the current adoption and development status
PERRI, LEONARDO
2022/2023
Abstract
The integration of Artificial Intelligence (AI) is causing a significant transformation in the financial landscape. This abstract provides a comprehensive overview of the current state of AI adoption and development in the finance sector, shedding light on its diverse applications and the trajectory of its evolution. AI has been widely adopted in various domains within finance. Algorithmic trading, for instance, has revolutionized market dynamics by utilizing sophisticated algorithms to analyze vast datasets and execute trades at unprecedented speeds. This has redefined the way trading is conducted. Risk management has also greatly benefited from AI, as it can process historical data to predict and mitigate potential risks, thereby enhancing decision-making processes. Additionally, AI plays a crucial role in fraud detection, using machine learning algorithms to identify anomalies and strengthen real-time preventive measures. The integration of chatbots and virtual assistants has brought about a paradigm shift in customer service within the finance sector. These AI-powered tools offer personalized services and efficient responses to inquiries, greatly enhancing the customer experience. Furthermore, AI is revolutionizing credit scoring, a fundamental aspect of financial operations. By incorporating non-traditional data sources, AI is enabling more accurate and inclusive credit assessments. The development status of AI in finance is characterized by ongoing advancements. Machine learning models, particularly those employing deep learning techniques such as neural networks, continue to evolve, improving predictive accuracy and decision-making capabilities. There is also a growing emphasis on explainability and transparency in AI models, addressing concerns about understanding and interpreting complex financial decisions. Financial institutions are actively working on seamlessly integrating AI with traditional systems, overcoming challenges related to interoperability and transitioning from legacy processes. However, challenges still exist in the widespread adoption of AI in finance. Data privacy and security concerns, especially considering the sensitive nature of financial information, require careful attention. Financial institutions must ensure that robust measures are in place to protect data and maintain the trust of their customers.| File | Dimensione | Formato | |
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https://hdl.handle.net/20.500.14240/106931