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Leveraging Automation and Data Analytics for Enhanced Financial Performance

In: Strategic Innovative Marketing and Tourism

Author

Listed:
  • Chara Pappa

    (University of West Attica)

  • Odysseas Pavlatos

    (University of Macedonia)

  • Nicos Sykianakis

    (University of West Attica)

  • Androniki Kavoura

    (University of West Attica)

Abstract

Automation, entwined with advanced data analytics, is recasting the finance function from ledger-centric record-keeping into a reflexive, prognostic command centre. Synthesising 125 empirical and conceptual studies, this chapter interrogates the technological-organisational dialectic that determines whether algorithmic routines translate into tangible financial gains. Evidence indicates that robotic process automation compresses cycle times and purges clerical error, whilst predictive analytics renders cash-flow aberrations visible at incipient stages, collectively fortifying accuracy, transparency and strategic agility. Yet these dividends crystallise only when superintended by visionary leadership, meticulous data governance and a culture that valorises interpretative acumen over deterministic fetishism. Legacy architectures, skill asymmetries and siloed mindsets otherwise transmute digital initiatives into brittle, self-defeating artefacts. By mapping the interplay between capability, culture and control, the study offers a hermeneutic lens through which executives, transformation architects and policymakers may orchestrate sustainable, analytics-infused finance ecosystems. Scholars may likewise exploit its agenda to illuminate future interdisciplinary inquiries.

Suggested Citation

  • Chara Pappa & Odysseas Pavlatos & Nicos Sykianakis & Androniki Kavoura, 2026. "Leveraging Automation and Data Analytics for Enhanced Financial Performance," Springer Proceedings in Business and Economics, in: Androniki Kavoura & Ulrike Gretzel & Vasiliki Vrana (ed.), Strategic Innovative Marketing and Tourism, pages 39-45, Springer.
    • Handle: RePEc:spr:prbchp:978-3-032-12968-0_5
    DOI: 10.1007/978-3-032-12968-0_5
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