Introduction
Manual regulatory reporting puts banks at risk, costs money, and takes too long. As banks become flooded with more regulations to report on within shorter windows, automating regulatory reporting across its lifecycle helps to deliver accuracy, traceability, speed, and lower costs. This step-by-step guide focuses on best practices to design, build, operate, and govern regulatory reporting automation for banks. Additionally, we share recommendations on banking IT platforms you can leverage today to start your automation journey.
Why You Should Care About Regulatory Reporting Automation
Regulators are demanding more granular, frequent, and standardized reporting. Automating regulatory reporting can help manage risks while lowering costs and time-to-report.
Transaction-level reporting, stress testing data feeds, new tax reporting, and environmental, social, and governance (ESG) disclosures all have banks racing to automate faster.
With reporting obligations often sprawled across siloed systems, modern data platforms can reduce reconciliation efforts with automated, governed pipelines.
Why Automate Regulatory Reporting?
- Human error and heavy reconciliation are costly. Automation improves accuracy and drastically cuts costs.
- Automated reporting pipelines can scale and adapt faster than legacy processes.
- Modern automation technology reduces time-to-compliance for new reports/regulations.
Hexaware’s experience modernizing transaction reporting for banks illustrates just how automation and data platforms can make regulatory reporting faster and easier.
Core Components of Regulatory Reporting Automation Architecture
Automated regulatory reporting solutions typically include several of these components:
- Ingestion layer: Brings in data from core banking systems, payments systems, market feeds, and external vendors.
- Data lake/data fabric: Governed storage with support for lineage, versioning, and historical lookback. A data fabric architecture helps break silos.
- Transformation and validation: Rules-based mapping, enrichment, reconciliation, and validation engines. Formats and consolidates raw data into reporting-ready datasets.
- Controls and audit logging: Automated testing of controls, evidence capture, and immutable audit logs provide assurance of compliance.
- Report generation/submission: Report assembly engines or pre-built connectors assemble regulator-specific formats, then securely transmit to regulators/approved reporting mechanisms (ARMs).
- Dashboard/monitoring: Orchestrates exceptions, approvals, and human-in-the-loop workflows for complete visibility and automation.
Security and privacy are table stakes for any regulatory reporting automation. Sensitive data should be encrypted, access should be role-based access, and data masking and privacy rules should be applied where regulators require them.
Regulatory Reporting Automation Checklist
Detailed best practices broken out by key phases: prepare, build, test, operate, and improve.
- Prepare phase — set a strategy that aligns with both compliance needs and business priorities.
- Involve compliance early: Gain buy-in from compliance leads to ensure business rules are understood and regulatory expectations are coded correctly.
- Know thy inventory: Document all regulatory reports, due dates, data elements, frequency, pain points, and identify opportunities to consolidate efforts. Tag reports by criticality.
- Plan for change: Automating regulatory reports does not mean they are future-proof. Select technology and design modular pipelines so new rules and validations can be adjusted without re-writing complex code.
- Build phase — focus on data and lean engineering practices.
- Establish governed master data and lineage: Implement a centralized data fabric or governed data lake that defines each reported data element’s source of truth with full end-to-end lineage. This will minimize future reconciliation work.
- Use standard data models when possible: Reduce mapping efforts by using industry standard data models when possible. ISO 20022 for payments and using common financial data models are examples.
- Leverage rule-driven transformations: Business rules should be maintained in configurable rule engines rather than hard coding rules within transformation scripts. Rule metadata should also be auditable and version-controlled.
- Implement comprehensive validation/reconciliation: Build validations to confirm record counts, totals, and critical reconciliations before allowing submission. Automate tolerance thresholds and auto-assignments for exceptions.
- Ensure modular report generation: Build reusable report ‘blocks’ and standardized connectors so you can pivot quickly for regulator-specific needs.
- Test phase — testing should use real world scenarios and control validations.
- Challenge your testing data: Make sure your synthetic test data realistically matches production complexity including edge cases, and high-volume traffic days. Where possible, leverage anonymized slices of production data.
- Automate controls: Validate your controls and validations with automated testing to continuously assure control coverage. Read about Hexaware’s cutting-edge controls automation and testing technology.
- Regression test any regulatory updates: Every time a regulation is updated, automated regression suites should run to ensure any changes don’t impact other reports.
- Operate phase — monitor pipelines, govern changes, and manage exceptions.
- Dashboards should give you real-time (or near-real-time) visibility into pipeline health, whether SLAs are being met, queues of exceptions, and pending transmissions/submission confirmations.
- Define exception remediation SLAs: Since automated pipelines will never be perfect, define your SLAs for catching and handing off exceptions to downstream analysts. Ensure continuous improvement by documenting root cause and remediation steps.
- Create a governance board: Just like other business-critical IT projects, assign a cross-functional team of stakeholders (compliance, data, IT, and operations) to approve reporting changes and prioritize automation projects.
- Capture audit-ready logs: Ensure your automation tools retain immutable logs of audit trails, report versions, transformations applied, and submission evidence for regulator audits.
- Improve phase — measure, iterate, and scale automation efforts.
- Automation should always improve: Track accuracy and timeliness metrics like submission errors, time to remediate exceptions, meeting SLAs, and post submission adjustments.
- Continue to expand automation: Start with a few high-value reports and scale from there. Apply lessons learned to data models and automate more pipelines.
Recommended Technology and Platform Considerations
When evaluating technology vendors or planning an in-house solution, consider the following architecture and technology options:
- Cloud native data platforms: Strongly governed cloud storage with built-in security and audit logging makes scaling and compliance easier. Multi-cloud or hybrid solutions allow for regulator required redundancies.
- Data fabric/metadata-driven platforms: A data fabric will help with discovery, cataloging, and governing your various data sets while maintaining lineage.
- Low-code/no-code transformation tools: These ETL/ELT (extract, transform, load/extract, load, transform) tools allow for visual mapping of rules/replacements. It speeds up your change management compared to heavy custom developments.
- Regulatory technology connectors/adapters: Pre-built connectors for common regulatory or reporting channels can save time and accelerate ROI. Hexaware’s regulatory technology radar outlines common RegTech categories and our offerings include accelerators for accelerating transaction reporting and validations.
- Analytics and observability: Dashboards that show your lineage, SLAs, and anomalies help quickly identify root causes. Hexaware’s PaymatiX™ analytics platform includes banking analytics that can surface insights from your reporting data.
Data Governance and Controls: Non-negotiables
Regardless of what technology you use, your automation solutions must include solid data governance.
- Single source of truth: Maintain canonical reference data definitions of instruments, customers, legal entities, GL accounts, currencies, country codes, etc.
- Data stewards: Appoint data stewards that are responsible and accountable for data quality, access, and documentation.
- Version control: All data transformations, business rules, and mappings should be versioned with documented approval processes.
- Automation isn’t permission to skip security: Enforce role-based access controls and separation of duties. Development and testing should never have production access.
- Data retention policies: Define how long submitted data is kept to satisfy regulator requirements.
Implementation Roadmap (Phased Approach Recommended)
Discovery and current state assessment (4–8 weeks)
Identify reports, underlying systems, and manual efforts to create a baseline. Uncover who the data owners are and where reconciliation/repeated efforts exist.
Proof of concept / pilot (8–12 weeks)
Automate one high-value report with medium complexity. Build your pipeline for ingestion, transformation, validation, and submission with full audit logging.
Scaling and optimization (3–9 months)
Automate similar reports. Start to reuse components and standardize data models.
Operationalization (continuous)
Hand over operations, create runbooks, implement dashboards, and define governance. Run cycles of continuous improvement.
Hexaware’s banking transformation approach frequently uses adaptable accelerators and domain expertise to shorten pilot and scale phases for banks.
Ensuring Testing, Validation, and Audit Readiness
- Automated reconciliations: Just as you reconcile your bank account, automate reconciliation from source systems to the final reported amount. Create alerts if reconciliations break.
- Automated controls: Implement technology to automatically run controls, including sample and deterministic tests. You can leverage Hexaware’s expertise in controls automation.
- Packaging for audit: Have templated packages ready for internal audit or regulator inspections that include lineage reports, validation checks, control test results, and submission/receipt confirmations.
- Validate with a 3rd party: Before going live, have an independent 3rd party validate your reporting logic. This could be internal audit or external consultants.
Security, Privacy, and Other Regulator-specific Requirements
- Encryption: Data at rest and in-transit should be encrypted using strong standards. Have encryption key management practices that meet regulator standards.
- Masking/tokenization: If you are using production data in test environments, mask sensitive customer information.
- Regulatory boundary/data sovereignty: Determine if data can cross borders or needs to adhere to localization rules. Assess if you need a hybrid cloud deployment.
Preparing Your Teams and Organization For Automation
- Skills: Ensure your analysts, data engineers, and operations team are trained on new automation tools and updated processes. Expect job roles to change with automation. Prepare your teams for their new responsibilities like handling exceptions and managing rule governance.
- Operating model: Think about how you will operate reporting once manual spreadsheets are out of the picture. Define your new SLAs, owners, and responsibilities.
- Communicate: Regularly communicate with regulators and internal stakeholders. Maintain a feedback loop to remain transparent and stay ahead of any expected exceptions.
Key Performance Indicators to Measure Success
After you’ve automated your regulatory reporting, track these metrics to understand its impact.
- Hours spent on manual reporting: How many hours did automation save you in each reporting cycle?
- Error rates: Did automation help decrease your error rates? Track adjustments you make after submissions.
- Exception remediation: How long did it take to remediate exceptions? Are you meeting SLAs?
- Percentage of automation: What percentage of reports are fully automated across the board? Aim for 100%.
- Regulatory audit findings: Track findings and feedback over time to measure improvements.
Automation should always result in cost savings when compared to your legacy process. Refer to Hexaware’s trading profit and loss (P&L) case study to see how automating your reporting and operational workflows improves efficiency.
Conclusion
Regulatory reporting automation is no longer optional for modern banks. With careful planning, a data-first architecture, robust controls, and a governance backbone, banks can transform regulatory reporting from a cost center into a resilient, auditable capability that supports business agility. Start with prioritized reports, validate thoroughly, and expand automation iteratively. Whether you choose a partner-led approach or an in-house build, emphasizing data lineage, controls automation, and audit readiness are keys to long-term success.
