Nowadays, the rapid adoption of Artificial Intelligence (AI) in the Financial Advisory field is generating novel opportunities and challenges, fundamentally reshaping the logic, practices, and structures of the sector. This Doctoral research is positioned within the broader field of AI applications in Asset Management, an area marked by growing technological sophistication but still limited theoretical development. Despite the growing volume of FinTech research, the existing theoretical contributions remain fragmented and embryonic: indeed, most studies focus on limited practical and conceptual dimensions, providing insufficient insight into how AI can fundamentally reshape advisory logics and mechanisms. Critically, there is no consolidated theoretical framework capable of capturing the implications of AI-driven innovation in Financial Advisory. This study addresses the gap by situating AI adoption within the FinTech paradigm with a novel conceptual framework, the AI-FinTech Innovation Grid (AFIG), proposed with the aim of interpreting AI-driven transformation in Financial Advisory, by investigating the relationship between AI maturity and financial sector’s contextual readiness. To correctly position Financial Advisory within the model, the study examines five key dimensions: the theoretical foundations of advisory practice, the historical and institutional evolution of services, the role and function of advisors (human and robo), the computational structure of investment processes, and the AI impact on advisory processes and functions. Building upon this comprehensive examination, the Financial Advisory Agency Evolution (FAAE) model is developed: by contextualizing the articulation of AI-driven advisory within the broader landscape of financial intermediation actors, it effectively positions Financial Advisory within the AFIG model. From these theoretical investigations, design guidelines for next-generation autonomous advisory systems are derived, resulting in the AI-Advisor Architecture (AAA) which ensures transparency, adaptability, portfolio personalization, and regulatory compliance. Finally, to substantiate the architecture conceptual integrity and its potential to inform practical innovation in AI-driven financial advisory, a prototype system is developed. Hence, the Ph.D. study overarching goal is to examine how Financial Advisory processes can be enhanced and transformed through the strategic identification, assessment, and application of AI capabilities. This research contributes substantively to theory, practice, and policy: it fills a critical conceptual gap for academics, provides insights for practitioners on the AI’s evolving impact in investment practices, and offers policy makers novel guidance to inform proactive regulation of AI-driven financial services. By integrating theoretical innovation with practical application, the study advances the understanding of AI’s transformative role in the Financial Advisory field and lays a foundation for future research and systems development.
Attualmente, la rapida adozione dell'Intelligenza Artificiale (IA) nel campo della consulenza finanziaria sta generando nuove opportunità e sfide, rimodellando profondamente la logica, le pratiche e le strutture del settore. Questo studio si inserisce nel più ampio ambito delle applicazioni dell'IA nella gestione patrimoniale, un'area caratterizzata da una crescente sofisticazione tecnologica e da uno sviluppo teorico ancora limitato. Nonostante il crescente volume di ricerche nel campo FinTech, i contributi teorici esistenti rimangono frammentati ed embrionali: infatti, la maggior parte degli studi si concentra su dimensioni pratiche e concettuali limitate, fornendo scarse indicazioni su come l'IA possa rimodellare in modo fondamentale le logiche e i meccanismi della consulenza finanziaria. Ad oggi non esiste un quadro teorico consolidato in grado di cogliere le implicazioni dell'innovazione guidata dall'IA nella consulenza finanziaria. Questo studio colma tale lacuna posizionando l'adozione dell'IA nel paradigma FinTech con un nuovo framework concettuale, l'AI-FinTech Innovation Grid (AFIG), proposto con l'obiettivo di interpretare la trasformazione guidata dall'IA nella consulenza finanziaria, esaminando la relazione tra lo stato evolutivo dell'IA e la maturità contestuale del settore finanziario. Per posizionare correttamente la consulenza finanziaria all'interno del modello, lo studio analizza cinque dimensioni chiave: le fondamenta teoriche della pratica consulenziale, l'evoluzione storica e istituzionale dei servizi finanziari, il ruolo e la funzione dei consulenti (umani e robo), la struttura computazionale dei processi di investimento e l'impatto dell'IA sui processi e le funzioni di consulenza. Su questa base, viene sviluppato il modello Financial Advisory Agency Evolution (FAAE): contestualizzando l'articolazione della consulenza basata sull'IA all'interno del più ampio panorama degli attori dell'intermediazione finanziaria, tale modello posiziona in modo efficace la consulenza finanziaria all'interno del modello AFIG. Da queste elaborazione teoriche, vengono derivate le linee guida progettuali per i sistemi di consulenza autonomi di nuova generazione, dando vita all'AI-Advisor Architecture (AAA), che garantisce trasparenza, adattabilità, personalizzazione del portfolio e conformità normativa. Infine, per sostenere l'integrità concettuale dell'architettura e il suo potenziale nel favorire l'innovazione pratica nella consulenza finanziaria basata sull'IA, viene sviluppato un modello prototipale. L'obiettivo complessivo di questo studio consiste nell'esaminare come i processi di consulenza finanziaria possano essere migliorati e trasformati attraverso l'identificazione strategica, la valutazione e l'applicazione delle capacità dell'IA. Questa ricerca contribuisce in modo significativo sia sul piano teorico, sia su quello pratico e normativo: aiuta a colmare una mancanza concettuale importante nel panorama accademico, fornisce spunti per le istituzioni finanziarie sull'impatto evolutivo dell'IA nelle pratiche di investimento e offre ai legislatori nuove indicazioni per una regolamentazione proattiva dei servizi finanziari basati sull'IA. Integrando innovazione teorica con applicazione pratica, lo studio approfondisce la comprensione del ruolo trasformativo dell'IA nel campo della consulenza finanziaria e pone le basi per future ricerche e sviluppo di soluzioni tecniche.
The future of investing: financial advisory agents based on Artificial Intelligence
FELICE, DOMENICO
2025/2026
Abstract
Nowadays, the rapid adoption of Artificial Intelligence (AI) in the Financial Advisory field is generating novel opportunities and challenges, fundamentally reshaping the logic, practices, and structures of the sector. This Doctoral research is positioned within the broader field of AI applications in Asset Management, an area marked by growing technological sophistication but still limited theoretical development. Despite the growing volume of FinTech research, the existing theoretical contributions remain fragmented and embryonic: indeed, most studies focus on limited practical and conceptual dimensions, providing insufficient insight into how AI can fundamentally reshape advisory logics and mechanisms. Critically, there is no consolidated theoretical framework capable of capturing the implications of AI-driven innovation in Financial Advisory. This study addresses the gap by situating AI adoption within the FinTech paradigm with a novel conceptual framework, the AI-FinTech Innovation Grid (AFIG), proposed with the aim of interpreting AI-driven transformation in Financial Advisory, by investigating the relationship between AI maturity and financial sector’s contextual readiness. To correctly position Financial Advisory within the model, the study examines five key dimensions: the theoretical foundations of advisory practice, the historical and institutional evolution of services, the role and function of advisors (human and robo), the computational structure of investment processes, and the AI impact on advisory processes and functions. Building upon this comprehensive examination, the Financial Advisory Agency Evolution (FAAE) model is developed: by contextualizing the articulation of AI-driven advisory within the broader landscape of financial intermediation actors, it effectively positions Financial Advisory within the AFIG model. From these theoretical investigations, design guidelines for next-generation autonomous advisory systems are derived, resulting in the AI-Advisor Architecture (AAA) which ensures transparency, adaptability, portfolio personalization, and regulatory compliance. Finally, to substantiate the architecture conceptual integrity and its potential to inform practical innovation in AI-driven financial advisory, a prototype system is developed. Hence, the Ph.D. study overarching goal is to examine how Financial Advisory processes can be enhanced and transformed through the strategic identification, assessment, and application of AI capabilities. This research contributes substantively to theory, practice, and policy: it fills a critical conceptual gap for academics, provides insights for practitioners on the AI’s evolving impact in investment practices, and offers policy makers novel guidance to inform proactive regulation of AI-driven financial services. By integrating theoretical innovation with practical application, the study advances the understanding of AI’s transformative role in the Financial Advisory field and lays a foundation for future research and systems development.| File | Dimensione | Formato | |
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https://hdl.handle.net/10589/248217