Cited by Building Trust in AI: Leadership Insights from Malaysian Fintech Boards

Building Trust in AI: Leadership Insights from Malaysian Fintech Boards DOI

Опубликована: Янв. 1, 2025

Abstract Fintech companies face the challenge of trying to lead in AI adoption while navigating potential pitfalls. The board directors plays a critical role demonstrating leadership and building trust with key stakeholders during implementation AI. This research involves interviews 21 members from Malaysian identify most effective strategies for fostering among shareholders, staff, customers. findings reveal that methods differ these three groups stakeholders. Leaders should build two ways: First, through trustworthy AI, second, by transparently communicating how is used manner addresses stakeholders’ concerns. show certain applications Generative can facilitate trust-building even if they are more limited scope, entail some sacrifice performance. There inherent trade-offs between unleashing an unrestricted way constrained, transparent, predictable application builds balancing act, fast cautious, controlled approach, central challenges faced board.

Язык: Английский

Engineering Sustainable Data Architectures for Modern Financial Institutions DOI

Sergiu-Alexandru Ionescu,

Vlad Dıaconıța,

Andreea-Oana Radu

и другие.

Electronics, Год журнала: 2025, Номер 14(8), С. 1650 - 1650

Опубликована: Апрель 19, 2025

Modern financial institutions now manage increasingly advanced data-related activities and place a growing emphasis on environmental energy impacts. In modeling, relational databases, big data systems, the cloud are integrated, taking into consideration resource optimization sustainable computing. We suggest four-layer architecture to address processing issues. The layers of our design for sources, integration, processing, storage. Data ingestion processes market feeds, transaction records, customer data. Real-time captured by Kafka transformed Extract-Transform-Load (ETL) pipelines. layer is composed Apache Spark real-time analysis, Hadoop batch an Machine Learning (ML) infrastructure that supports predictive modeling. order optimize access patterns, storage includes various components. test results indicate in real-time, compliance reporting, risk evaluations, analyses can be conducted fulfillment sustainability goals. metrics from deployment support implementation strategies technical specifications architectural also looked at integration models flow improvements, with applications finance. This study aims enhance enterprise context guidance modernizing infrastructure.

Язык: Английский

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