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Tax Ombud finds that SARS's risk engine curbs refund losses

South Africa's Tax Ombud found that audits of refund claims that SARS identified in its 2017 financial year cut the credits claimed by 17.38%. It judged that the risk engine was relatively effective.

  • 2 an organization

    Audits of the claims that SARS identified cut refund credits by 34,776,054,832 rand, or 17.38%, in the 2017 financial year.

Depth runs from 1, a convenience or time saved, to 5, a life or a livelihood preserved.

Status
Reported benefit.
Evidence

found, the grade of this recordstudiedreportedclaimed

An independent audit report supports the benefit.

Compared with

The credits claimed on the original returns before audit or verification. Measured.

Without the system
-200,128,001,510
With it
-165,351,946,677
The system’s part
Not isolated. The Ombud counted the outcomes of audits on claims that SARS identified and compared no selection made without the engine. The report does not describe how the engine scores claims or show that it learns from data, and the reviews themselves are manual.
Limits
Not yet checked.
Costs and harms
Not yet checked.
Later
Not yet checked.
Benefit type
Improved Task Accuracy
System
SARS refund risk engine
Made by
South African Revenue Service (the report names no other developer)
Used by
South African Revenue Service (SARS)
Where
South Africa (national)
Stage
In routine use
Date
, when the source was published
Kind
Aggregate result
Broad benefit
Analyzing Data or Information, Evaluating Information to Determine Compliance with Standards
Collections
Scrutiny and findings
Last revised
31 October 2026

Sources

  1. Audit report, by authors with no tie to the system.

    Office of the Tax Ombud (South Africa), Judge B M Ngoepe. Tax Ombud's Report on the investigation in terms of Section 16(1)(b) of the Tax Administration Act 28 of 2011 into alleged delayed payment of refunds as a systemic and emerging issue. Dated 28 August 2017, Section V, paras 2.4.1 and 2.4.2.

    This shows that a significant number of refunds claimed were correctly identified for audit/verification. It would therefore seem that SARS's risk engine is relatively effective in preventing losses through refund claims.

Details

AI system

Workflow

SARS identified refund claims for audit or verification systematically, for example by comparing third-party data. SARS staff then resolved the identified risks by manual review.

Money

Kind of value

Cut in the refund credits claimed on returns that SARS identified for audit or verification in the 2017 financial year, which the Ombud calls the amount that would have been paid out without those checks

Amount

34,776,054,832 rand

Recipients and payers

SARS, for the South African fiscus

Follow-up

Conditions for replication

SARS must have enough audit capacity to finish reviews without holding back valid refunds. Refunds must be released promptly once a verification is done, and the Ombud found that SARS often failed to do so.

Entry details

Sector

Tax administration.

Purpose

The South African Revenue Service built its risk engine to identify refund claims for audit or verification before refunds are paid.

Not yet checked: model, release, provider, role, configuration, inputs and outputs, autonomy, human oversight, AI contribution, beneficiaries, reach, distribution and access, duration, every measurement question, currency and prices, period and unit, realization, gross or net, included costs, comparison, method, return and payback, ownership and control, dependencies, cross-border connections, geopolitical implications, replication attempts, availability, institutional decision, grounds, action and result, success criterion, every evidence question, observed or intended, alternative assessments, related entries, other identifiers, category mappings.

Cite this record

Peter H. Vartanian. (2026). Tax Ombud finds that SARS's risk engine curbs refund losses (AIBD-0855). Artificial Intelligence Benefits Database, edition of 31 October 2026. https://petervartanian.xyz/aibd/records/AIBD-0855.html (archived at https://doi.org/10.7910/DVN/AADSII, version 4.0)

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