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[ Here's a different take, suggesting Services Australia is now trying
to paste a happy-face on its AI applications.
> ... a key fraud-related use case relates to identity theft, and
specifically to Services Australia wanting to alert customers when
suspicious behavior associated with their account is detected
[ Wouldn't it be nice if the agency's prior performance justified the
public regarding the agency's execs as being trustworthy.
[ Added to that, a nonsensical pretext was used to try to explain away
the second example application, 'debt prioritisation'. It suggests a
great deal of wool is being blown across Estimates Ctees' eyes. ]
Services Australia describes fraud, debt-related machine learning use
cases - A "long, long way" from being production-ready.
Ry Crozier
itNews
Feb 28 2025 6:55AM
https://www.itnews.com.au/news/services-australia-describes-fraud-debt-related-machine-learning-use-cases-615323
Services Australia is trialling machine learning to detect potential
instances of identity theft affecting Centrelink customers, with the
goal of stopping payments from being rerouted.
The agency was forced to defend its trials of machine learning
technology late on Thursday night, after offering only a vague
explanation in response to a news report by Information Age.
The report identified use cases in “debt prioritisation” and “fraud
detection”.
Appearing before senate estimates late on Thursday night, officials
sought to provide a more open view of the technology trials, while also
saying they were “a long, long way from being able to deploy” anything
to production.
The agency offered a twofold explanation for how machine learning is
being applied in fraud-related use cases.
General manager of the fraud control and investigations Peter Timson
said a key fraud-related use case relates to identity theft, and
specifically to Services Australia wanting to alert customers when
suspicious behavior associated with their account is detected.
“It’s being used in the investigative space where we can actually
identify - without going into too much detail because scammers would be
watching - what we identify as traits if they’ve taken your identity,”
Timson said.
Deputy CEO for payments and integrity Chris Birrer said the “archetypal
example here would be indicators that the person whose name the claim is
submitted in, has not actually submitted it - that somebody else has -
either by tricking that person or other forms of identity theft - taken
their identity.”
Timson cited a SIM farm operation that harvested Centrelink customers
details via malicious links sent in millions of text messages.
“People have clicked on that link and [the attackers have] harvested
your name, and then someone’s starting to change your bank accounts,” he
said.
“How do we [Services Australia] actually ‘forward lean’ to protect you
because someone else is trying to get into your account and redirect
payments.”
Timson said the technology is aimed at “someone who we suspect is not
who they say they are”, rather than conducting broad-brush identity checks.
A second fraud-related check using machine learning is to aid
“prepayment checks” around Australian government disaster relief payments.
“We aim to process those claims as fast as possible because people have
been impacted by a disaster, and we want to get money into their bank
accounts - as long as it’s not a fraudster’s bank account,” Birrer said.
“We have a system where we identify, through a number of potential
checks, where a claim might potentially be fraudulent, and then staff
look at it, and either release the claim because they don’t think it’s
fraudulent, or they do something like follow up with the customer to
check to see their identity or whether or not the bank account is indeed
their bank account.
“What we’re looking at here is how to further refine that process … to
help to predict certain anomalies which mean it’s more likely to be a
fraudulent claim.”
Debt backlog reduction
The report in Information Age also revealed a “debt prioritisation”
trial also involving machine learning technology.
This was characterised in the report as a triage exercise to aid efficiency.
The agency clarified at senate estimates that the trial is not about
raising debts, but about detecting a subset of cases likely to be
“finalised, no debt”, removing them from a “backlog” of debt decisions
needing to be made.
General manager of payment assurance, program and appeals Robert Higgins
said this was “not an insubstantial number”. Previous audits have put
this at about seven percent of debt determinations.
Services Australia’s CEO David Hazlehurst characterised the trial as “a
mechanism for us with a backlog of potential debts to say which of these
are most likely to not result in a debt - and let’s get rid of those
quickly.”
“The machine is identifying which ones are most likely to be that. A
person still makes the decision about finalising that matter,”
Hazlehurst said.
“It’s not taking the human out of the loop. It’s simply a process of
helping us try to be more efficient in getting through the potential
debt backlog.”
Birrer said the model could also help in internal allocation of
potential debt cases, noting that not all staff were equipped to handle
more complex cases, which could delay determinations from being made.
“One inefficiency we have is being able to allocate the right type of
work of the right complexity to the skill of the staff member,” Birrer said.
“‘Finalised, no debt’ is amongst the easiest of the debt work, and so if
there’s new staff within the payments and integrity group it’s a good
thing for them to do as they’re on their skills ladder.
[ But if a software aid - whether implemented using a procedural
language, a rule-based expert system, Prolog, or any of a flotilla of
'AI' techniques - hardly seems necessary to do the easiest task. (Apart
from which, why are *any* ‘Finalised, no debt’ cases lying around in a
database of 'live cases'??). ]
“If you’ve got a very large and complex potential debt that relates to
something that happened historically where there might be different
policy settings and they need to do a retrospective calculation there,
that’s a much more complex task, it also helps.
“We do know that sometimes staff get allocated work they’re not skilled
to do, they’ll do a bit of it, their time isn’t used productively,
because they throw it back into the pool for somebody else to pick up.”
[ Ah, do it's a flexible workflow with a cyclical 'too-hard basket'.
If that's the level APS operational services have declined to, no wonder
so many agencies are providing such dismally bad service-levels. ]
Entitlements decisions
Services Australia’s CEO David Hazlehurst said there are “no current
plans to use AI” to make decisions about entitlements.
“We’ve got a long list of things that we would consider before we could
do anything in relation to that,” he said.
Minister for government services Katy Gallagher said that “there would
be some level of government decision-making involved in that as well.”
“A decision to move into that space would have to be elevated, I would
think,” she said.
Agency officials repeatedly said the machine learning uses were only at
a trial phase, and that “any further movement” towards production usage
would require multiple gates and assessments.
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Roger Clarke mailto:[email protected]
T: +61 2 6288 6916 http://www.xamax.com.au http://www.rogerclarke.com
Xamax Consultancy Pty Ltd 78 Sidaway St, Chapman ACT 2611 AUSTRALIA
Visiting Professorial Fellow UNSW Law & Justice
Visiting Professor in Computer Science Australian National University
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