Case Studies
Energy / Public Sector · India
Indian Oil Corporation
AI-driven reconciliation, revenue-leakage detection and SAP/logistics analytics across a national footprint.
Full-population testing on SAP and logistics data across a national energy operator — surfacing control exceptions and revenue leakage that sample-based audit had missed.
5M+
daily transactions covered by audit analytics
100%
population testing vs. sample-based coverage
National
SAP and logistics footprint in scope
Challenge
The starting point.
Indian Oil Corporation runs one of India's largest and most complex SAP and logistics footprints, spanning refineries, depots, pipelines and retail across the country. Traditional sample-based audit and reconciliation could not reliably catch revenue leakage, control exceptions or process breaks at that scale. The practice was engaged to deploy AI-driven analytics against the full transaction population.
Approach
How the engagement was structured.
- Deployed AI-driven reconciliation over SAP and logistics data at national scale.
- Built revenue-leakage detection routines running against the full transaction population rather than statistical samples.
- Integrated the analytics with existing SAP and logistics workflows so exceptions surfaced inside the operational rhythm.
- Applied audit-analytics practice capable of full-population testing across 5M+ daily transactions.
Outcome
What changed.
- Full-population testing surfaced control exceptions and revenue-leakage patterns that traditional sampling had missed.
- Reconciliation shifted from periodic, sample-based reviews to continuous coverage across SAP and logistics.
- Established a repeatable analytics pattern usable across the national footprint.
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