Client Background
The client is a leading bank in the United Arab Emirates with a substantial international footprint, whose group Finance function spans eight distinct teams covering financial, management, and regulatory reporting. Over years of growth and successive system migrations, critical reporting inputs had come to depend heavily on offline processes, with spreadsheets and email operating as the de facto integration layer between core systems. Many data elements were either not captured in source systems or were maintained manually, compromising reporting agility, limiting audit traceability, and constraining the bank's readiness for automation and generative-AI initiatives. Finance teams were increasingly acting as the primary data-enrichment layer rather than the underlying systems, concentrating institutional knowledge in individuals and limiting scalability. Cedar was engaged to conduct a structured assessment of these data gaps and to define a practical way-forward roadmap.
Cedar’s Approach
Cedar structured the engagement as a sixteen-week program organised into sequential phases, anchored on a diagnostic that traced data lineage from source systems through staging, enrichment, and reporting layers.
Kick-Off and Data Collection – Cedar began by establishing the program governance cadence, issuing a data checklist, and reviewing the bank's existing finance data architecture and prior internally documented issues to build a preliminary view before engaging stakeholders.
Stakeholder Conversations – Cedar conducted over 140 structured conversations across the eight Level 1 Finance reporting teams and a wide set of Level 2 support functions, including Risk, Operations, HR, IT, and Treasury, to validate how data is captured, enriched, and reconciled in practice.
Architecture and Data-Flow Assessment – Working alongside the technology function, Cedar mapped the multi-layer data architecture spanning core source systems, ETL and staging layers, and downstream output tools, and identified the manual intervention touchpoints embedded at each layer as well as the active migration programs affecting resolution sequencing.
Business Understanding Document – Cedar consolidated the findings into a signed-off Business Understanding Document capturing several hundred discrete data, process, and control gaps across all reporting teams, refined through multiple review cycles with department heads.
Data Mapping Document – Cedar produced a detailed report-to-source mapping, documenting each gap across more than forty-five attributes so that every issue could be traced to its originating system and root cause.
Way Forward Recommendations – Cedar clustered the gaps into enterprise themes and applied a three-pillar solution framework, covering source-system fixes, workflow and process fixes, and controls and monitoring fixes, translated into six solution areas.
Strategic Outcome and Way Forward
The engagement gave the bank a single, validated view of its finance-reporting data landscape, replacing a fragmented, individually held understanding with a documented and traceable baseline agreed across all reporting teams. Cedar's recommendations were sequenced into a phased implementation roadmap across three horizons: quick wins focused on mapping corrections, workflow formalisation, and SLA enforcement; medium-term source-system enhancements and straight-through data flows; and a longer-term horizon establishing enterprise-wide data governance, reconciliation controls, and cross-entity standardisation.
By positioning data ownership, certification, and change-control frameworks as foundational enablers, the roadmap set a direction in which automation is built on governed, auditable data rather than added on top of manual workarounds, materially strengthening reporting agility, audit traceability, and the bank's readiness for AI and automation as it moves into execution.