Hexaware designed and implemented a PaaS enabled audit analytics that streamlined audit data processing and established an audit solution for long-term scalability, AI & Cloud platform integration and global consistency.
Client
A Big 4 Accounting Firm
The client is a global provider of audit, tax, and consulting services, requiring robust analytics to manage complex audit engagements. Its legacy platform struggled with large volumes of audit data, manual effort, and lack of scalability.
Challenge
Legacy Audit Analytics Built for Samples, Not Scale
As audit engagements became more data intensive, the client’s existing audit analytics platform revealed structural limitations:
- Audits relied heavily on sample-based testing, limiting visibility into full populations of audit data and increasing the risk of undetected anomalies
- Manual data preparation dominated workflows, with teams spending excessive time extracting, cleansing, and reconciling audit data outside the audit analytics platform
- Audit analytics were not embedded across the audit methodology but peripherally, reducing their impact on risk assessment, planning, and execution
- The platform struggled to scale across very large transaction volumes, slowing analysis and straining infrastructure
- Disconnected tools for data ingestion, analytics, workpapers, and reporting resulted in fragmented workflows and limited audit engagement insight
These challenges constrained audit quality, delayed insights, and made it difficult to support consistent, scalable audit engagements globally.
Solution
PaaS Enabled Audit Analytics Platform
Hexaware enabled a modern audit analytics solution powered by PaaS, enabling scalability, automation, and actionable insights across the audit lifecycle.
Solution Highlights
- Audit analytics were embedded across all phases of the audit lifecycle, from risk assessment through execution and reporting, making analytics integral to every audit engagement.
- Enterprise-scale processing of audit data was enabled to support full-population analysis across virtually any data volume, including millions or billions of records.
- An expanded analyzer library was implemented to support end-to-end audit cycles, including general ledger, revenue and trade receivables, inventory, and industry- and sector-specific audit analytics.
- A cloud-based PaaS architecture was integrated with modern AI and data platforms, supporting elastic scaling, advanced visualization, and long-term sustainability of the audit analytics platform.
- AI-augmented audit analytics were embedded directly within core analyzers to enable anomaly detection, pattern recognition, and predictive insights.
- High-performance analytics execution was designed to deliver near real-time responses, even across highly complex and large-scale audit data environments.
- Self-service flexibility was provided to allow audit teams to tailor analytics for local and engagement-specific needs while maintaining governance and data sovereignty across the audit solution.
- Interactive and visual analytics were enabled to support collaborative exploration of audit data during client discussions and throughout the audit engagement.
- Integration with global audit methodologies and platforms was implemented to ensure consistent audit analytics delivery across regions and engagement teams.
Benefits
Delivering Scalable and Actionable Audit Insights
The modernized audit solution delivered clear improvements across audit quality and efficiency:
- Expanded coverage through full-population audit analytics
- Reduced manual effort in audit data preparation and processing
- Faster insight generation across large-scale datasets
- Consistent analytics-driven audits across regions
- Improved visibility across every audit engagement
- Future-ready, cloud-based audit analytics foundation
Summary
Hexaware enabled the client to reimagine audit analytics with a PaaS enabled audit solution that streamlined audit data processing, delivered actionable insights across audit engagements, and established a cloud-based roadmap for future scalability.
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