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Immutable Ledger-Based Modeling for Payroll and Compensation Backends in Cloud-Native Applications
Subject area: Science,Engineering and Technology · Area of research: Business Management
DOI: https://doi.org/10.64388/IREV8I9-1714978
Abstract
Payroll and compensation backends represent some of the most legally sensitive and financially consequential components of enterprise software systems. Traditional implementations often rely on mutable database records that overwrite prior state, complicating auditability, replay safety, and regulatory compliance. In cloud-native, distributed environments, mutable state models further amplify risks related to concurrency, partial failures, and inconsistent recovery. This paper proposes an immutable ledger-based modeling approach for payroll and compensation backends deployed in cloud-native architectures. By treating every compensation-relevant change as an append-only, versioned ledger entry, the system achieves deterministic state reconstruction, strong audit traceability, and resilience under distributed execution. The study examines canonical ledger design, event-sourced architectures, retroactive correction handling, concurrency isolation, and cross-entity coordination within compensation workflows. It also analyzes partitioning strategies, operational resilience, and anti-patterns associated with mutable payroll systems. The resulting framework demonstrates how immutable modeling principles—when combined with identity-scoped partitioning and cloud-native scalability patterns—enable high-integrity financial backend systems that remain deterministic, replay-safe, and regulatorily compliant under high concurrency and infrastructure variability.
Keywords
Immutable Ledger; Payroll Automation; Compensation Systems; Event Sourcing; Cloud-Native Architecture; Financial Backend Engineering; Deterministic State Reconstruction; Auditability; Distributed Systems; Identity-Based Partitioning
How to cite this paper
@article{1714978,
author = {Sefa Teyek},
title = {Immutable Ledger-Based Modeling for Payroll and Compensation Backends in Cloud-Native Applications},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {8},
number = {9},
pages = {1911-1925},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1714978.pdf},
abstract = {Payroll and compensation backends represent some of the most legally sensitive and financially consequential components of enterprise software systems. Traditional implementations often rely on mutable database records that overwrite prior state, complicating auditability, replay safety, and regulatory compliance. In cloud-native, distributed environments, mutable state models further amplify risks related to concurrency, partial failures, and inconsistent recovery. This paper proposes an immutable ledger-based modeling approach for payroll and compensation backends deployed in cloud-native architectures. By treating every compensation-relevant change as an append-only, versioned ledger entry, the system achieves deterministic state reconstruction, strong audit traceability, and resilience under distributed execution. The study examines canonical ledger design, event-sourced architectures, retroactive correction handling, concurrency isolation, and cross-entity coordination within compensation workflows. It also analyzes partitioning strategies, operational resilience, and anti-patterns associated with mutable payroll systems. The resulting framework demonstrates how immutable modeling principles—when combined with identity-scoped partitioning and cloud-native scalability patterns—enable high-integrity financial backend systems that remain deterministic, replay-safe, and regulatorily compliant under high concurrency and infrastructure variability.},
keywords = {Immutable Ledger; Payroll Automation; Compensation Systems; Event Sourcing; Cloud-Native Architecture; Financial Backend Engineering; Deterministic State Reconstruction; Auditability; Distributed Systems; Identity-Based Partitioning},
month = {March},
doi = {https://doi.org/10.64388/IREV8I9-1714978}
}