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.