MicroServices-driven enterprise architecture model for infrastructure optimization

Abd-Elwahab, A. M.; Mohamed, A. G.; Shaaban, E. M.

doi:10.1186/s43093-023-00268-3

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2024

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Cited by 2 publications

References 22 publications

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Enterprise Management System using Deep Learning Algorithm

2024

2024 Second International Conference on Data Science and Information System (ICDSIS)

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Enterprise Management System using Deep Learning Algorithm

2024

2024 Second International Conference on Data Science and Information System (ICDSIS)

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Optimal data-driven strategy for in-house and outsourced technological innovations by open banking APIs

Dezem,

Sachan,

Macedo

et al. 2024

Futur Bus J

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Open banking is a customer consent-driven data-sharing framework to maintain interoperability among financial and non-financial institutions through secure application programming interfaces. Traditional retail banks are losing their competitive edge against digital banks, FinTech, and BigTech firms due to a higher outflow of customer account data than inflow. These firms capitalize on open banking data to launch innovative products and introduce “ready-to-deploy” Banking-as-a-Service platforms for end-to-end banking operations without the need to build full-scale infrastructures. Therefore, banks must reshape data-driven strategies to stay competitive. This research proposes a decision-support tool to select optimal digital strategies using the Strategic Value Index (SVI), a metric derived from analyzing multiple strategic objectives of bank stakeholders. The SVI minimizes implementation time and costs while maximizing operational action importance to a balanced digital strategy that combines both in-house and outsourced technological developments. The importance of operational action is quantified using evidential reasoning with fuzzy logic to address the challenge of aggregating incomplete and ambiguous banking data and assessments from multiple stakeholders. The theoretical approach is validated through a real application in a Latin American bank, and its findings are globally transferable. Cost and time data were sourced from public repositories, as made available accessible through government-mandated disclosures. The sensitivity analysis revealed that hybrid in-house and outsourced development is more flexible in meeting tight timelines and budget constraints. The combined approach is more cost-effective, time-efficient, and aligned with the internal needs of banks compared to either entirely in-house or fully outsourced models.

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MicroServices-driven enterprise architecture model for infrastructure optimization

Cited by 2 publications

References 22 publications

Enterprise Management System using Deep Learning Algorithm

Enterprise Management System using Deep Learning Algorithm

Optimal data-driven strategy for in-house and outsourced technological innovations by open banking APIs

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