Towards a unified ontology for IoT fabric with SDDC

Koorapati, Koundinya; Pandu, Rubini; Ramesh, P.; Veeraswamy, Sairam; Narasappa, Usha

doi:10.1016/j.jksuci.2021.04.015

Cited by 6 publications

(2 citation statements)

References 13 publications

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“…This trend of integration is not just a response to the availability of big data but also a strategic move towards a more holistic approach to banking operations. Koorapati et al (2022) highlight this integration, noting the pivotal role of big data technologies in enabling banks to process and analyze large volumes of data efficiently. Similarly, Gai, Qiu, and Sun (2018) emphasize the transformative impact of these technologies in enhancing the analytical capabilities of banks.…”

Section: Trends In Predictive Analytics Adoption In Bankingmentioning

confidence: 99%

“…Another limitation is the cost associated with the deployment of advanced predictive analytics tools, particularly for smaller banks. As observed by Koorapati et al (2022), the integration of big data technologies and AI-driven analytics requires substantial investments in infrastructure and technology. Smaller banks may face constraints in terms of financial resources, potentially limiting their ability to leverage these advanced tools effectively.…”

Section: Challenges and Limitationsmentioning

confidence: 99%

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Predictive analytics in credit risk management for banks: A comprehensive review

Wilhelmina Afua Addy,

Chinonye Esther Ugochukwu,

Adedoyin Tolulope Oyewole

et al. 2024

GSC Adv. Res. Rev.

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This comprehensive review explores the dynamic landscape of predictive analytics in credit risk management within the banking sector. Anchored in a qualitative research design, the study synthesizes existing literature and real-world case studies to provide a multifaceted understanding of predictive analytics' role in modern banking. The review identifies key trends, highlighting the integration of predictive analytics across diverse banking operations, the transition to advanced machine learning algorithms, the democratization of predictive analytics tools, and the growing emphasis on ethical and regulatory compliance. It underscores the effectiveness of predictive analytics, showcasing its ability to enhance risk assessment precision, decision-making agility, and overall banking performance. Comparative analyses reveal the varying performance of predictive models across contexts, emphasizing the importance of tailored model selection. However, challenges such as data quality, model interpretability, talent scarcity, ethical considerations, and implementation costs pose significant hurdles. Looking forward, predictive analytics promises to be an indispensable tool for mitigating credit risk in the banking sector, offering refined risk assessments, smarter decisions, and enhanced resilience. The insights from this review provide valuable guidance for banking professionals, regulators, and researchers navigating the evolving landscape of predictive analytics in banking.

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Section: Trends In Predictive Analytics Adoption In Bankingmentioning

confidence: 99%

Section: Challenges and Limitationsmentioning

confidence: 99%