Error-Free by Design: AI‑Enabled TDS Compliance at EY India

The Global Compliance and Reporting Direct Tax team at EY India worked with a mid-sized multinational company that processes more than 2,500 vendor payments each quarter, about 84% of which are subject to withholding. A manual pre-check found that six percent of the time there was an error. PANs that don't match challan errors, wrong classification of sections, and duplicates that are dangerous Form 26Q trust in the vendor and being on time The choice is how to plan a 90-day redesign that uses AI to automate validation and reconciliation through RPU OLTAS integration, shows anomalies on dashboards, and adds exception governance without slowing down throughput. The exhibit pack has a section on baseline error rates, a section on section-wise distributions, a section on detection time and escalation aging, and a section on section-wise distributions. You can choose from a minimal validation stack, a TDS Exception Probability Score control as a checklist, or targeted client training. The goal of learning is to find mistakes that can be avoided. Figure out how much money automation design governance will save on judgment calls and make a plan for a staged rollout. The unresolved tension affects both model and change

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-11-12
DOI
https://doi.org/10.5281/zenodo.17586932
Primary Topic
Data Analysis with R
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article
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article

Error-Free by Design: AI‑Enabled TDS Compliance at EY India

Zenodo (CERN European Organization for Nuclear Research)
Data Analysis with R
article

Error-Free by Design: AI‑Enabled TDS Compliance at EY India

article en

Abstract

The Global Compliance and Reporting Direct Tax team at EY India worked with a mid-sized multinational company that processes more than 2,500 vendor payments each quarter, about 84% of which are subject to withholding. A manual pre-check found that six percent of the time there was an error. PANs that don't match challan errors, wrong classification of sections, and duplicates that are dangerous Form 26Q trust in the vendor and being on time The choice is how to plan a 90-day redesign that uses AI to automate validation and reconciliation through RPU OLTAS integration, shows anomalies on dashboards, and adds exception governance without slowing down throughput. The exhibit pack has a section on baseline error rates, a section on section-wise distributions, a section on detection time and escalation aging, and a section on section-wise distributions. You can choose from a minimal validation stack, a TDS Exception Probability Score control as a checklist, or targeted client training. The goal of learning is to find mistakes that can be avoided. Figure out how much money automation design governance will save on judgment calls and make a plan for a staged rollout. The unresolved tension affects both model and change

Zenodo (CERN European Organization for Nuclear Research)
Jain University (IN)
Peace, Justice and strong institutions
Openalex Percentile: Top 22%
Data Analysis with R
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Error-Free by Design: AI‑Enabled TDS Compliance at EY India · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS