The Common
Survival Report.

By 23, I was operating inside the Fraud & Intelligence unit of one of Australia’s largest insurers. I’d moved fast: from frontline call centres to the inner sanctum where loss ratios, risk appetites, and “emerging threats” were discussed behind glass. My job was simple in theory and brutal in practice: protect the loss ratio.

On paper, the loss ratio is just a number — the percentage of premiums returned as claims. In reality, it is a measure of how much human misery can be absorbed, delayed, or denied before it becomes a problem for shareholders.

I was trained to see every claimant not as a person in crisis, but as a potential cost centre. The tools I was given:

• Systems that flagged “suspicious” claims based on opaque rules.
• Algorithms that triaged who got paid fast, who got investigated, and who got quietly starved of updates.
• Internal playbooks that leaned on “wear and tear”, “pre-existing damage”, and “maintenance” exclusions to push storm and flood losses back onto households.

I watched hydrology reports commissioned not to find the truth, but to find a “riverine flood” angle that would trigger an exclusion. I saw “poor maintenance” used as a weapon against pensioners who couldn’t afford a new roof. I watched people be told, in polite legal language, that a rusted nail — not the cyclone, not the storm surge — was the “real” cause of their ruin.

The more I learned, the less I could pretend this was neutral. What I saw didn’t turn me into a company man. It radicalised me.

I saw how the industry weaponises information asymmetry and administrative friction. I saw systems built not to process claims, but to filter them — through delay, doubt, and documentation traps — until enough people gave up.

I walked out of that glass tower. I didn’t leave empty-handed. I left with the playbook. This report is what happens when that playbook is turned inside-out and handed back to the people it was used against.

1.1 The Mission: Democratizing Tradecraft

I realized that silence was complicity. The system relies on the consumer being passive, confused, and intimidated by technical jargon. My work is now dedicated to shattering that reliance. I exist to educate survivors—renters, precarious workers, and climate refugees—on the specific, "guerrilla" tactics necessary to survive in a market-driven economy that is fundamentally antithetical to human welfare.

This report is not just a guide; it is a weapon. It is the curriculum of a counter-education designed to level the playing field. I teach survivors how to think like an adjuster to defeat the adjuster. We replace "hope" with forensic documentation. We replace "politeness" with regulatory leverage. We turn the insurer’s own bureaucracy—the General Insurance Code of Practice, the Privacy Act, and the AFCA Rules—into a cage that traps them in their own obligations.

In a system where profit is extracted from the denial of safety, survival is an act of rebellion. This guide is your manual for that rebellion.

“We are being sorted by risk, by cost, and by inconvenience.”

The sorting mechanism is most visible — and most violent — where people anchor their lives: housing, credit, and climate-exposed property.

2.1 From Maps to Models: The Evolution of Redlining

Old-school redlining used physical maps. Whole suburbs and postcodes were coded as “hazardous” or “high risk”.

Today, that explicit discrimination is illegal. But the outcomes are eerily similar.

Now the red lines run through models, not maps.

• Automated decision systems use “neutral” variables:
  • postcode
  • income band
  • credit file data
  • tenancy history
  • unpaid bills
• These variables are statistically entangled with race, class, and history.

This creates Algorithmic Redlining: discrimination that has been mathematically laundered.

A landlord, bank, or insurer can say, truthfully:

“We didn’t reject you. The system did.”

The harm is displaced onto software. Responsibility evaporates.

2.2 The Tenant Screen: PropTech as Gatekeeper

Automated tenant screening has become the default filter in many rental markets.

These systems:

• scrape credit reports, court lists, tenancy databases
• treat almost any “record” as a black mark
• heavily weight non-tenancy debts (medical bills, fines, old phone plans)

One of their most toxic outputs:

“Records Found.”

This can mean:

• a dismissed case
• a charge without conviction
• another person with a similar name
• a ten-year-old minor matter

To a landlord faced with 40 applications, “Records Found” is often an auto-no.

The effect:

• people are banished from decent rentals
• pushed into informal arrangements or unsafe housing
• instability produces more debt, more stress, more bad data

The system then points to that instability as proof you were “high risk” all along.

2.3 Bluelining: Climate Risk as a Sorting Engine

As the climate destabilises, another kind of red line appears — one drawn in blue.

Insurers and reinsurers are redrawing their maps of what is “insurable”:

• floodplains in Western Sydney
• fire-exposed peri-urban belts
• coastal and riverine communities

Where risk can’t be priced with confidence, the industry retreats:

• eye-watering premiums
• giant excesses
• exclusions that hollow out cover
• outright refusal to quote

This is Bluelining: sorting populations based on climate risk.

The overlap is not accidental:

• communities with poor drainage, no trees, and old infrastructure
• are often the same places historically starved of investment

The result is a kind of managed retreat without announcement:

• people cannot insure → cannot rebuild → are quietly displaced
• the protection gap between what was lost and what was paid out grows each year

“The poor are transparent. Capital is opaque.”
“Surveillance turns everyday survival into a permanent trial.”

4.1 Bossware: The Architecture of Distrust

As work has shifted into call centres, home offices, warehouses, and platforms, surveillance has followed.

Bossware — employee monitoring software — tracks:

• keystrokes and clicks
• mouse movements
• screen activity
• time “idle” vs “active”

Productivity scores are generated from these signals.
Thinking, reading, planning — anything that looks like “not typing” — is punished.

The result:

• performative busyness
• fake activity to keep the meters happy
• constant low-level anxiety

The workplace becomes:

• not just a site of labour
• but a site of continuous evidence collection against you

4.2 The Amazon Model & Biometric Oversight

In high-tempo logistics, the body itself is surveilled.

• barcode scans tracked to the second
• “Time Off Task” calculated and enforced
• toilet breaks converted to metrics

Algorithms act as foremen that never sleep and never need to justify themselves.

In gig and delivery work:

• AI-equipped cameras monitor eye movement and driving behaviour
• apps track location, route choices, and “dwell time”
• ratings decide who gets work and who is silently dropped

For many workers, every gesture is a data point.

Your behaviour becomes evidence:

• to discipline you
• to terminate you
• or to justify squeezing more “efficiency” from you next quarter

Privacy becomes a class luxury.

“Power concedes nothing without a demand. It never did and it never will.”

The systems described above — the sorting, the sludge, the surveillance — are not accidental glitches. They are optimisations for capital.

They will not be fixed by:

• asking nicely
• better design
• ethical AI guidelines

They will be fixed only when they become too expensive to maintain.

6.1 The Withdrawal of Cooperation

The only leverage we have is our participation.

• The Rent Strike: When tenants collectively withhold rent, the landlord's cash flow — and their leverage with the bank — evaporates.
• The Data Strike: When users collectively poison the data well or refuse to generate the "exhaust" that trains the models.
• The Labour Strike: When workers refuse to turn the gears.

In the algorithmic age, the strike must evolve.

It is not just about stopping the factory line. It is about jamming the signal.

6.2 Solidarity as Survival

The individual is easily crushed. The algorithm can route around one driver, one tenant, one claimant.

But it cannot route around a class.

We build unions not just for wages, but for dignity. To assert that we are not data points to be optimised, but human beings to be respected.

See our Workplace Defense Guide for tactical steps on building power where you work.