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Análise da implementação da inteligência artificial : caso prático na banca
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A crescente digitalização dos serviços financeiros tem impulsionado o setor bancário a adotar tecnologias inovadoras para melhorar a eficiência operacional e a experiência do cliente. Entre essas tecnologias, a Inteligência Artificial (IA) destaca-se como uma ferramenta essencial para otimizar processos, aprimorar a tomada de decisão e fortalecer o relacionamento com os clientes. Esta dissertação analisa a implementação de uma solução baseada em IA numa instituição de banca privada, avaliando os seus benefícios, desafios e aplicações práticas. O estudo segue uma abordagem qualitativa, utilizando a metodologia de estudo de caso para investigar como a IA pode ser integrada nas operações da instituição. Através de entrevistas semiestruturadas com profissionais dos departamentos comercial e de investimentos, bem como com a equipa de TI, foram recolhidos insights fundamentais sobre a adoção de soluções baseadas em IA. Os resultados indicam que a IA tem o potencial de agilizar significativamente os processos internos, automatizar tarefas administrativas e melhorar as recomendações de investimento. No entanto, desafios como a fiabilidade dos modelos, a segurança dos dados e a resistência à mudança continuam a ser preocupações relevantes. Foi realizada uma análise financeira da implementação da IA, demonstrando que, apesar do investimento inicial necessário, as soluções de IA podem levar a uma redução significativa de custos e ao aumento das receitas, através da otimização da gestão de ativos e da redução do tempo gasto em tarefas repetitivas. Os resultados também destacam a importância de garantir transparência e explicabilidade na tomada de decisão baseada em IA, de modo a manter a confiança nos serviços de consultoria financeira. Esta dissertação contribui para o debate académico e profissional sobre a IA no setor bancário, oferecendo uma perspetiva prática sobre a sua implementação. Além disso, apresenta recomendações para instituições financeiras que pretendem integrar a IA, salientando a necessidade de uma abordagem gradual e estratégica. Pesquisas futuras deverão explorar o papel crescente da IA generativa na banca privada e analisar as perceções dos clientes em relação aos serviços baseados em IA.
The increasing digitalization of financial services has driven the banking sector to adopt innovative technologies to improve operational efficiency and customer experience. Among these technologies, Artificial Intelligence (AI) is emerging as a key tool for optimizing processes, enhancing decision-making, and strengthening customer relationships. This dissertation analyzes the implementation of AI in a private banking institution, assessing its benefits, challenges, and practical applications. The study follows a qualitative approach, using a case study methodology to investigate how AI is being integrated into the institution’s operations. Through semi-structured interviews with professionals from the commercial and investment departments, as well as the IT team, key insights were gathered regarding AI adoption. The results indicate that AI has the potential to significantly streamline internal processes, automate administrative tasks, and improve investment recommendations. However, challenges such as model reliability, data security, and resistance to change remain major concerns. A financial analysis of AI implementation was conducted, demonstrating that, despite the initial investment required, AI solutions can lead to significant cost savings and increased revenues by optimizing asset management and reducing the time spent on repetitive tasks. The findings also highlight the importance of ensuring transparency and explainability in AI-driven decision-making to maintain trust in financial advisory services. This dissertation contributes to the academic and professional debate on AI in the banking sector by offering a practical perspective on its implementation. Additionally, it provides recommendations for financial institutions looking to integrate AI, emphasizing the need for a gradual and strategic approach. Future research should explore the evolving role of generative AI in private banking and analyze customer perceptions regarding AI-driven services.
The increasing digitalization of financial services has driven the banking sector to adopt innovative technologies to improve operational efficiency and customer experience. Among these technologies, Artificial Intelligence (AI) is emerging as a key tool for optimizing processes, enhancing decision-making, and strengthening customer relationships. This dissertation analyzes the implementation of AI in a private banking institution, assessing its benefits, challenges, and practical applications. The study follows a qualitative approach, using a case study methodology to investigate how AI is being integrated into the institution’s operations. Through semi-structured interviews with professionals from the commercial and investment departments, as well as the IT team, key insights were gathered regarding AI adoption. The results indicate that AI has the potential to significantly streamline internal processes, automate administrative tasks, and improve investment recommendations. However, challenges such as model reliability, data security, and resistance to change remain major concerns. A financial analysis of AI implementation was conducted, demonstrating that, despite the initial investment required, AI solutions can lead to significant cost savings and increased revenues by optimizing asset management and reducing the time spent on repetitive tasks. The findings also highlight the importance of ensuring transparency and explainability in AI-driven decision-making to maintain trust in financial advisory services. This dissertation contributes to the academic and professional debate on AI in the banking sector by offering a practical perspective on its implementation. Additionally, it provides recommendations for financial institutions looking to integrate AI, emphasizing the need for a gradual and strategic approach. Future research should explore the evolving role of generative AI in private banking and analyze customer perceptions regarding AI-driven services.
Descrição
Palavras-chave
Inteligência artificial Banca privada Tecnologia financeira Automação Implementação de IA Gestão de investimentos Artificial intelligence Private banking Financial technology Automation AI implementation Investment management
Contexto Educativo
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Licença CC
Sem licença CC