Artificial intelligence Part 7: specific industry impacts - corporate governance and risk management

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The impact of artificial intelligence on governance and risk management is nuanced. There are some tasks that AI can do, but other functions must be undertaken by human beings. As a general description of work functions, governance teams usually produce documentation such as board briefing papers, governance reports, policy updates, compliance documentation and meeting summariess. These are all processes open to AI. This posting will provide an indication of changes coming to roles in corporate governance and risk management due to AI. 

Corporate Governance

• Board reporting and briefing papers preparation: AI can synthesise management informaiton, financial data and identify risks into baord-ready material reducing the administrative teams that currently do this work.
• Corporate secretariat functions: the various documents produced such as records of minutes, compliance tracking, regulatory filings are all highly structured functions that are automatable and suited to AI.
• ESG reporting: Environmental, Social and Governance (ESG) reporting requirements have increased in recent years creating an additional compliance burden. AI can offset the increase through tools that aggregate and report sustainability data.

Risk Management

• Credit risk modelling: Credit risk modelling is a heavily quantitative task suited to AI thus reducing the analyst layers that do the manual model running and reporting. 
• Operational risk assessment: risk assessment identifies and quantifies risk across various buinsess processes. The assessment often produces a risk assessment table which rates and grades the risk including mitigation method. These processes are suited to be augmented by AI at the data gathering and first-analysis stage.
• Market risk monitoring: real-time AI surveillance is replacing/reducing some human monitor headcounts for this task.

Compliance and regulation

• KYC (Know Your Customer) and Anti-Money Laundering (AML): AI is transforming these tasks from labour-intensive manual processes to AI-monitored exception-handling workflows. The impact is the reduction in the size of large compliance teams. 
• Regulatory change management: the tracking and interpreting of new regulations can be augmented by AI formonitoring ans summarising only. The interpretation of ambiguous regulatory language remains an essential human function not AI.
• Audit: the largest 4 accounting firms are deploying AI to analyse entire transation populations rather than the traditional scope of only sampling. This change provides a better quality audit but it requires fewer junior auditors.

Prudential Regulation (Central Banks and Regulators)

• Supervisory data anlysis: regulators such as APRA (in Australia), FCA (US) and Central Banks are using AI to monitor systemic risk across institutional data.
• Examination and inspection of institutions: the inspection teams face efficiency improvements and possibly headcount pressure.
• Policy and rule-making: roles in these functions are more protected however the judgement and accountability requires is high for human decision making.

In terms of employment, senior risk professionals are still needed for a range of actions such as - determining acceptable risk levels, advising executives on risk management, assessing complex of emerging risks, balancing regulatory, financial and reputational considerations. 

Physical inspection teams are still needed and remain human-based for purposes such as: construction safety inspections, infrastructure maintenance checks, environmental site visits, equipment integrity inspections. 

In the future, the workforce changes are more likely to be a reduction in the entry-level and junior operational roles with a structure as shown -

Before
Chief risk/governance officer

Senior specialists

Large analyst and reporting teams

After

Chief risk/governance officer

Senior specialists (fewer)

Small team supervising AI monitoring systems

The AI systems in use include: Microsoft Copilot, IBM Watson, Palantir Technologies and SAS Institute.

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Health - Hantavirus explanation

 

What is hantavirus, the disease that has killed 3 cruise ship passengers?

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Thomas Jeffries, Western Sydney University

Three people have died after a suspected outbreak of hantavirus on a cruise ship in the middle of the Atlantic ocean. At least one other passenger is in intensive care in South Africa.

The World Health Organization announced the deaths in a social media statement on Monday, along with one confirmed case of the rare disease. Authorities are investigating another five suspected cases among passengers travelling on the MV Hondius.

So, what is hantavirus? And why can it be so deadly?

As the investigation unfolds, here’s what we know.

What is hantavirus?

Hantavirus is a rare but severe respiratory illness that can cause severe bleeding, fever and even death.

The virus is spread by rodents, such as mice and rats, mainly through the urine and droppings of infected animals.

Hantavirus does not typically spread from person to person. However, in rare cases it may spread between people.

Globally, there are an estimated 150,000 to 200,000 cases of hantavirus each year.

It is less contagious than airborne viruses such as COVID and influenza, as it typically does not spread from person to person.

What makes it so deadly?

There are two main types of hantavirus, each with different symptoms.

Hantavirus pulmonary syndrome, which affects the lungs, is mainly found in the United States. If a person becomes infected with this type of hantavirus, within days they will likely experience coughing and shortness of breath.

As the illness progresses, they can develop symptoms such as fatigue, fever and muscle aches. They may also get headaches, dizziness, nausea, vomiting and abdominal pain. This is the most deadly kind of hantavirus. Tragically, about 38% of people who develop these symptoms die from the disease.

Hemorrhagic fever with renal syndrome is mainly found in Europe and Asia, but the strain known as the Seoul virus has spread around the world. This form of hantavirus mainly affects the kidneys.

People usually develop symptoms within two weeks of being exposed to this virus. Early symptoms include severe headaches, abdominal pain, nausea and blurred vision. More advanced symptoms include low blood pressure, internal bleeding and even acute kidney failure. This disease can be caused by different viruses and some are more deadly than others, meaning between 1% and 15% of cases can be fatal.

Unfortunately, there is no specific treatment or cure for either type of hantavirus. However, early medical treatment may increase a person’s chance of survival. This can include using respirators, oxygen therapy and dialysis.

Authorities are still investigating which type of hantavirus the passengers were exposed to.

How did it get on a cruise ship?

In a closed environment such as a cruise ship, there are two possible ways passengers could have contracted hantavirus.

One is being exposed to the virus while on a shore excursion.

The other possibility is that rodents may have entered the ship on cargo, and then spread the disease to passengers through their infected urine or droppings. Other factors such as hygiene standards and food storage practices may have caused the infection to spread more quickly.

To contain this suspected outbreak, authorities must first ensure any rodents are safely contained and removed from the ship. They should then monitor all passengers for hantavirus symptoms. The virus is diagnosed with a PCR test, similar to those used to diagnose viruses such as COVID.

Given there is no specific treatment for the disease, authorities must help any infected passengers manage their symptoms. This involves checking that they are breathing normally and their kidneys are functioning properly.

So, how worried should we be?

Although alarming, cases of hantavirus remain are extremely rare. But it can look similar to other respiratory illness, so you should always get symptoms checked. If you’ve been in regions where the virus is found and experience shortness of breath, fever or any other flu-like symptoms, see your GP.

Thomas Jeffries, Senior Lecturer in Microbiology, Western Sydney University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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Artificial intelligence Part 6: specific industry impacts - healthcare

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The impact of AI on healthcare is more nuanced and varied than most other white collar sectors. Healthcare is more complex due to need to retain a strong physical human presence in the medical and care functions that cannot be automated or replaced by digital technology. Healthcare has a complex set of regulations, legal liability and an irreducable human dimension with doctors, nurses, allied health professionals required for direct patient face-to-face contact inclusive of the use of tele-health services.

In contrast, administrative and diagnostic supportive functions are highly exposed. A summary is provided below and is not exhaustive -

Clinical diagnosis and decision support

• Radiology already has AI systems that match or exceed radiologists in detecting certain cancers (breast, lung, skin). The aspect of concern is potential over diagnosis due to the sensitivity of the digital systems used. The radiologist role is shifting towards oversight, complex cases handling and AI exception management. The potential risk in this field being the volume of radiologists needed to undertake radiology functions may reduce.
• Pathology has a similar pattern to radiology as AI can analyse tissue samples at scale. The role of pathologists however is not removed at this time but is being augmented.
• Diagnostic support to General Practitioners can be provided through AI tools that synthesise patient medical history, symptoms and test results. These AI tools are being deployed already however the intention is to support the medical service provided by doctors to patients not substitute it. A secondary intention is to reduce the need for specialist referrals however this has yet to be achieved.
• Dermatology and ophthalmology are two specialties that are heavily dependent on pattern recognition and will face some AI encroachment, however as with other diagnositic tools it may be a supportive function not a medical role replacement one. 

Clinical administrative functions and documentation

• Medical transcription is already largely automated with voice-to-text using clinical AI being widely used.
• Clinical note writing is being addressed by ambient AI scribes such as Nuance DAX. Documentation can consume 30-40% of physician time and assists medical practitioners to achieve quality of life improvement however it reduces medical transcription services significantly, if not in some cases, entirely.
• Prior authorisation, coding and billing  are very large cost centres and are being progressively automated and threatening large administrative workforces in hospitals and insurance companies.

Nursing and Allied Health

• Triage and patient monitoring: AI can monitor patient vital signs, identity and alert to deterioriation and prioritise nursing care. AI provides service augmentation but not replacement of front line nursing care which must be physically provided.
• Care coordination roles do face pressure from AI that can track patient journeys, identify gaps and schedule follow-ups. At this time however this remains an augmentation tool rather than a job replacement one. 
• Bedside care, emotional support and physical nursing are strongly human services and cannot be replaced by AI. It remains one of the most protected areas across all industries. 

Pharmaceuticals and medical science/research

• Drug discovery timelines are being compressed by AI which reduces some research roles but creates new roles in AI-guided drug design. An example of AI impact is Alphaford's protein structural predictions which transformed structural biology.
• Clinical trial design and patient matching is being assisted by AI but not replaced by it. 

In healthcare, it is the administrative organisational pyramid that is being compressed with headcount reduction. Clinical roles continue with signifcantly increased volumes of patients possibly over time.

ANZAC Day 2026

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ANZAC Day continues to have strong public support in recognition of the service of men and women during time of war. This special commemorative day has been held for 110 years on the 25th April and was originally intented to honour the members of the Australian and New Zealand Army Corps who served in the Gallipoli campaign in 1915 (World War I). Since that time, it has expanded to include other conflicts and peace keeping operations until the present time. On this day, those who lost their lives as a result of their service are particularly remembered.

Lest we forget

Science: the mind and imagination

 

How does imagination really work in the brain? New theory upends what we knew

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Thomas Pace, University of the Sunshine Coast and Roger Koenig-Robert, University of Technology Sydney; UNSW Sydney

Your brain is currently expending about a fifth of your body’s energy, and almost none of that is being used for what you’re doing right now. Reading these words, feeling the weight of your body in a chair – all of this together barely changes the rate at which your brain consumes energy, perhaps by as little as 1%.

The other 99% is used on the activity the brain generates on its own: neurons (nerve cells) firing and signalling to each other regardless of whether you’re thinking hard, watching television, dreaming, or simply closing your eyes.

Even in the brain areas dedicated to vision, the visuals coming in through your eyes shape the activity of your neurons less than this internal ongoing action.

In a paper just published in Psychological Review, we argue that our imagination sculpts the images we see in our mind’s eye by carving into this background brain activity. In fact, imagination may have more to do with the brain activity it silences than with the activity it creates.

Imagining as seeing in reverse

Consider how “seeing” is understood to work. Light enters the eyes and sparks neural signals. These travel through a sequence of brain regions dedicated to vision, each building on the work of the last.

The earliest regions pick out simple features such as edges and lines. The next combine those into shapes. The ones after that recognise objects, and those at the top of the sequence assemble whole faces and scenes.

Neuroscientists call this “feedforward activity” – the gradual transformation of raw light into something you can name, whether it’s a dog, a friend, or both.

In brain science, the standard view is that visual imagination is this original seeing process run in reverse, from within your mind rather than from light entering your eyes.

So, when you hold the face of a friend in mind, you start with an abstract idea of them – a memory or a name, pulled from the filing cabinet of regions that sit beyond the visual system itself.

That idea travels back down through the visual sequence into the early visual areas, which serve as your brain’s workshop where a face would normally be reconstructed from its parts – the curve of a jawline, the specific shade of an eye. These downward signals are called “feedback activity”.

A signal through the static

However, prior research shows this feedback activity doesn’t drive visual neurons to fire in the same way as when you actually see something.

At least in the brain regions early in the vision process, feedback instead modulates brain activity. This means it increases or decreases the activity of the brain cells, reshaping what those neurons are already doing.

Even behind closed eyes, early visual brain areas keep producing shifting patterns of neural activity resembling those the brain uses to process real vision.

Imagination doesn’t need to build a face from scratch. The raw material is already there. In the internal rumblings of your visual areas, fragments of every face you know are drifting through at low volume. Your friend’s face, even now, is passing through in pieces, scattered and unrecognised. What imagining does is hold still the currents that would otherwise carry those pieces away.

All that’s needed is a small, targeted suppression of neurons that are pulled by brain activity in a different direction, and your friend’s face settles out of the noise, like a signal carving its way through static.

Steering the brain

In mice, artificially switching on as few as 14 neurons in a sensory brain region is enough for the animal to notice it and lick a sugar-water spout in response. This shows how small an intervention in the brain can be while still steering behaviour.

While we don’t know how many neurons are needed to steer internal activity into a conscious experience of imagination in humans, growing evidence shows the importance of dampening neural activity.

In our earlier experiments, when people imagined something, the fingerprint it left on their behaviour matched suppression of neuronal activity – not firing. Other researchers have since found the same pattern.

Other lines of evidence strengthen our theory, too. About one in 100 people have aphantasia, which means they can’t form mental images at all. One in 30 form these images so vividly they approach the intensity of images we actually see, known as hyperphantasia.

Research has found that people with weaker mental imagery have more excitable early visual areas, where neurons fire more readily on their own. This is consistent with a visual system whose spontaneous patterns are harder to hold in shape.

Taking all this together, the spontaneous activity reshaping hypothesis – our new theory that imagination carves images out of the steady stream of ongoing brain activity – explains why imagination usually feels weaker than sight. It also explains why we rarely lose track of which is which.

Visual perception arrives with a strength and regularity the brain’s own internal patterns don’t match. Imagination works with those patterns rather than against them, reshaping what is already there into something we can almost see.

Thomas Pace, Researcher and Lecturer at the Thompson Institute, University of the Sunshine Coast and Roger Koenig-Robert, Senior Research Fellow, Graduate School of Health, University of Technology Sydney; UNSW Sydney

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Earth Day 2026