TRACKSAN migrates legacy on-premise systems to Microsoft Azure and Fabric, governs the result under DAMA-DMBOK, deploys risk and fraud detection models, and adds a forensic-standard traceability layer — built for banks, government agencies, large taxpayers, and other regulated organizations whose data has to hold up to an examiner, an auditor, or a court.
No line stands alone. Migration without governance doesn't solve auditability, and AI without forensic traceability won't survive regulatory examination.
The same sequence in every engagement — each step depends on the one before it, and none is offered on its own.
On-premise Oracle, SQL Server, and Sybase systems migrated to Microsoft Azure and Fabric on a lakehouse architecture, preserving full historical integrity and lineage.
Pipelines built with data-quality, completeness, and consistency controls, and full lineage documentation from source to destination.
Unsupervised risk-classification and multivariate fraud-alert models applied to the now-governed data.
A forensic-standard traceability layer that reconstructs the origin, transformation, and use of any data behind an AI model or regulatory report.
Institutions between USD 500M and USD 5B in assets — regulated enough to carry real compliance weight, small enough to lack an internal data-architecture team — facing the retirement of legacy core-banking platforms by Fiserv, FIS, and Jack Henry.
Agencies still running decades-old mainframe systems, unable to cross-check data in real time — a gap tied to USD 100–135B in undetected pandemic-era unemployment fraud, and to state tax gaps most states have never even measured.
Transfer-pricing cases, extractive industries such as mining, and any organization whose tax position or data practices face sustained regulatory or judicial scrutiny — a structural gap that isn't unique to banks and government.
Every model and pipeline is built to reconstruct its own lineage — the same rigor a bank examiner, a state auditor, or a court would demand, documented once.
At Ecuador's national tax authority, a risk-classification model projected roughly USD 1.8B in recovered tax revenue, and a fraud-alert pipeline cut review time by 50% — the same discipline now applied to U.S. state agencies and mid-market banks.
A court-certified forensic IT expert and a tax-litigation lead who has defended expert reports before Ecuador's tax courts for two decades — including transfer-pricing and extractive-industry cases for the country's largest taxpayers — work as one team. Data forensics and financial/tax forensics, applied together.
Built on Microsoft Azure and Fabric, under the same data-governance framework applied as a consultant to the Inter-American Development Bank — not a generic tools stack.
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