Risk, Regulatory &
Compliance
Data Services

Regulatory reports depend on consistent definitions, complete source records and traceable calculations. A correctly formatted submission can still contain inaccurate information if exposures are missing, classifications differ between systems or reporting dates are misaligned. These weaknesses also affect internal risk assessment, management oversight and the ability to explain reported figures.

Smarbl’s risk and regulatory data management services connect reporting obligations to source attributes, business rules, ownership and controls. By resolving gaps before data reaches SmartReg, financial institutions can reduce recurring manual corrections, improve reconciliation and establish a more reliable basis for regulatory reporting automation.

Common challenges include inconsistent customer and counterparty identifiers, missing residency or sector classifications, incomplete collateral relationships, differing exposure measures and insufficient historical data. Finance, Risk and Treasury may also use different cut-offs or aggregation rules, creating discrepancies between returns and internal reports. A change in reporting granularity can expose gaps that previously remained hidden in aggregated spreadsheets.

Smarbl assesses these issues against the institution’s applicable reporting requirements. Deliverables include a report-to-data requirements matrix, documented source mappings, an accountable gap register and a prioritised remediation plan, connected to the relevant SmartReg reporting scope.

Data-quality controls test whether information is complete, valid, consistent and available at the required time. Reconciliation establishes whether related measures agree after allowing for legitimate differences in scope, accounting treatment, currency or timing. Both are necessary: a portfolio can pass field-level checks while still excluding accounts, and a reconciled total can contain incorrect underlying classifications.

Smarbl designs controls around the reporting use case, including source-to-mart checks, portfolio-to-ledger reconciliation and comparisons across returns. Exceptions are assigned to owners, investigated and resolved through source corrections or approved adjustments. SmartReg can then apply the agreed reporting validations and retain review evidence.

A regulatory data dictionary defines what an attribute means, where it comes from, how it is derived and which reports use it. Data governance assigns responsibility for those definitions and for resolving defects. Data lineage connects a submitted figure to its source records, transformations and adjustments, helping teams explain the result and assess the impact of change.

Together, these capabilities support consistent interpretation and more efficient responses to audit or supervisory questions. Smarbl documents business definitions, ownership, mappings, controls and source-to-report lineage, then connects the approved specifications to data marts and SmartReg configuration.

Granular regulatory reporting increases the importance of customer, account, transaction and exposure-level information. Readiness therefore requires an assessment of source coverage, required history, common identifiers, reporting dimensions and data-quality controls. Converting an existing report into a machine-readable format does not resolve missing attributes or inconsistent business definitions.

Smarbl works backwards from the applicable reporting specifications to define the required data foundation. The engagement can cover gap assessment, regulatory data models, source mappings, reconciliation and controlled interim inputs. Approved datasets are connected to SmartReg calculations, validations and submission outputs, with delivery priorities aligned to the institution’s implementation scope.

FATCA and CRS reporting preparation brings together customer identity, account ownership, relevant tax information, classification decisions and reportable financial information. Data issues can include incomplete self-certifications, missing tax identification numbers, inconsistent tax-residency records, unclear entity or controlling-person relationships and differences between customer and account systems. FATCA and CRS require distinct rule interpretations; one common data foundation must preserve those differences.

Smarbl helps document the required attributes and approved classification logic, identify gaps, assign remediation owners and prepare traceable reporting datasets. The institution’s tax and compliance teams approve reportability decisions and applicable treatments, while the agreed SmartReg scope supports controlled reporting preparation.

Credit-bureau reporting depends on reliable links between borrowers, facilities and their changing repayment status. Incorrect identifiers, duplicated facilities, outdated closure information or inaccurate arrears and balances can misrepresent a customer’s credit history and lead to disputes or corrective submissions. In the UAE, Etihad Credit Bureau’s correction process relies on the relevant information provider to review and update supplied information.

Smarbl assesses borrower and facility data across the credit lifecycle, defines reporting mappings and quality rules, and traces rejection or correction issues to source records. Curated datasets and the agreed SmartReg workflow support validation, controlled corrections and submission evidence.

BCBS 239 addresses effective risk data aggregation and risk reporting, including governance, data architecture, aggregation capabilities and reporting practices. Its emphasis on reliable risk information makes data ownership, consistent definitions, completeness, timeliness and usable reporting central considerations. The same data foundations can also strengthen prudential reporting, although applicability and supervisory expectations must be assessed for the institution concerned.

Smarbl supports structured gap assessment, control design, remediation planning and evidence collection within an agreed BCBS 239 alignment scope. The work can connect improvements in data governance, quality and lineage to the data marts and reporting processes used by SmartReg.

A governed data foundation can reuse customer identities, account relationships, balances and reference data across reporting obligations. However, each framework may require different definitions, reporting populations, cut-offs, classifications and history. Reuse works when shared attributes are clearly defined and framework-specific rules remain explicit and version controlled.

Smarbl designs common domain data with purpose-specific reporting views where needed. This helps reduce repeated sourcing and inconsistent mappings while preserving each authority’s requirements. SmartReg consumes the agreed datasets for configured reporting processes. Jurisdictions, report templates and integrations are confirmed in scope, and existing warehouses or data marts can be retained where suitable.

Smarbl’s services establish the functional data foundation: reporting requirements, source mappings, business definitions, remediation actions, governance, lineage and curated data marts. SmartReg uses that foundation to operate the configured reporting process, including calculations, validations, adjustments, approvals and submission outputs. Connecting the two helps ensure that data acceptance is tested against actual reporting needs.

An engagement can begin with a priority reporting domain or a recurring reconciliation problem and expand through agreed milestones. Smarbl coordinates the functional work with the institution’s reporting, data and technology teams, while the institution retains accountability for interpretation, data ownership and final reporting sign-off.