Personal Fraud methodology

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Reference period
2020-21 financial year


This publication contains results from the Personal Fraud Survey (PFS), a topic on the Multipurpose Household Survey (MPHS) conducted throughout Australia from July 2020 to June 2021. The MPHS, undertaken each financial year by the Australian Bureau of Statistics (ABS), is a supplement to the monthly Labour Force Survey (LFS) and is designed to collect statistics for a number of small, self-contained topics.

The survey collected information from individuals about their experience of selected types of personal fraud in the 12 months prior to interview, including card fraud, identity theft, and selected types of scams. Identity theft questions also collected some information about incidents that occurred in the five years prior to interview. The survey also collected information about the socio-demographic characteristics of persons who experienced fraud, and information about the most recent/serious incident experienced for each type of fraud. Non-person victims of fraud (e.g. organisations and businesses) were not included in the scope of the survey.

Data collection


The scope of the survey was restricted to people aged 15 years and over who were usual residents of private dwellings and excludes:

  • members of the Australian permanent defence forces
  • certain diplomatic personnel of overseas governments, customarily excluded from Census and estimated resident population counts
  • overseas residents in Australia
  • members of non-Australian defence forces (and their dependants)
  • persons living in non-private dwellings such as hotels, university residences, boarding schools, hospitals, nursing homes, homes for people with disabilities, and prisons
  • persons resident in the Indigenous Community Strata (ICS).

The scope for MPHS included households residing in urban, rural, remote and very remote parts of Australia, except the ICS.


In the LFS, rules are applied which aim to ensure that each person in scope is associated with only one dwelling, and hence has only one chance of selection in the survey. See Labour Force, Australia for more detail.

Sample size

Information was collected from 28,386 fully responding persons. This includes 486 proxy interviews for people aged 15 to 17 years, where permission was not given by a parent or guardian for a personal interview.

Collection method

The survey is one of a number of small, self-contained topics on the MPHS.

Each month, one eighth of the dwellings in the LFS sample were rotated out of the survey and selected for the MPHS. After the LFS had been fully completed for each person in scope and coverage, a usual resident aged 15 years or over was selected at random (based on a computer algorithm) and asked the additional MPHS questions in a personal interview. 

In the MPHS, if the randomly selected person was aged 15 to 17 years, permission was sought from a parent or guardian before conducting the interview. If permission was not given, the parent or guardian was asked the questions on behalf of the 15 to 17 year old (proxy interview). 

Data were collected using Computer Assisted Interviewing (CAI), whereby responses were recorded directly onto an electronic questionnaire in a notebook computer, with interviews conducted over the telephone. 

Processing the data

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Estimation methods

Survey estimates of counts of persons are obtained by summing the weights of persons with the characteristic of interest.


Weighting is the process of adjusting results from a sample survey to infer results for the total 'in-scope' population. To do this, a 'weight' is allocated to each enumerated person. The weight is a value which indicates the number of persons in the population represented by the sample person.

The first step in calculating weights for each unit is to assign an initial weight, which is the inverse of the probability of being selected in the survey. For example, if the probability of a person being selected in the survey was 1 in 600, then the person would have an initial weight of 600 (that is, they represent 600 people).


The initial weights were calibrated to align with independent estimates of the population of interest, referred to as 'benchmarks'. Weights calibrated against population benchmarks ensure that the survey estimates conform to the independently estimated distribution of the population rather than the distribution within the sample itself. Calibration to population benchmarks helps to compensate for over or under-enumeration of particular categories of persons/households which may occur due to either the random nature of sampling or non-response.

The MPHS estimates do not (and are not intended to) match estimates for the total Australian person/household populations obtained from other sources.

The survey was benchmarked to the Estimated Resident Population (ERP) living in private dwellings in each state and territory at December 2020. People living in Indigenous communities were excluded. These benchmarks are based on the 2016 Census.

While LFS benchmarks are revised every 5 years, to take into account the outcome of the 5-yearly rebasing of the ERP following the latest Census, the supplementary surveys and MPHS (from which the statistics in this publication are taken) are not. Small differences will therefore exist between the civilian population aged 15 years and over reflected in the LFS and other labour household surveys estimates, as well as over time. If comparisons are being made over time then proportions should be used rather than estimates of persons.

Reliability of the estimates

The estimates in this publication are based on information obtained from a sample survey. Any data collection may encounter factors, known as non-sampling error, which can impact on the reliability of the resulting statistics. In addition, the reliability of estimates based on sample surveys are also subject to sampling variability. That is, the estimates may differ from those that would have been produced had all persons in the population been included in the survey. This is known as sampling error.

Non-sampling error

Non-sampling error is caused by factors other than those related to sample selection. It is any factor that results in the data values not accurately reflecting the true value of the population.

It can occur at any stage throughout the survey process. Examples include:

  • selected people that do not respond (e.g. refusals, non-contact) 
  • questions being misunderstood
  • responses being incorrectly recorded
  • errors in coding or processing the survey data.

Sampling error

Sampling error is the expected difference that can occur between the published estimates and the value that would have been produced if the whole population had been surveyed. Sampling error is the result of random variation and can be estimated using measures of variance in the data.

Standard error

One measure of sampling error is the standard error (SE). There are about two chances in three that an estimate will differ by less than one SE from the figure that would have been obtained if the whole population had been included. There are about 19 chances in 20 that an estimate will differ by less than two SEs.

Relative standard error

The relative standard error (RSE) is obtained by expressing the standard error as a percentage of the estimate.

\(R S E \%=\left(\frac{S E}{estimate}\right) \times 100\)

Only estimates with RSEs less than 25% are considered reliable for most purposes. Estimates with larger RSEs, between 25% and less than 50% have been included in the publication, but are flagged to indicate that they should be used with caution. Estimates with RSEs of 50% or more have also been flagged and are considered unreliable for most purposes. RSEs for these estimates are not published.

Calculating measures of error

Proportions and percentages formed from the ratio of two estimates are also subject to sampling errors. The size of the error depends on the accuracy of both the numerator and the denominator. A formula to approximate the RSE of a proportion is given below. This formula is only valid when the numerator (x) is a subset of the denominator (y):

\(R S E\left(\frac{x}{y}\right) \approx \sqrt{[R S E(x)]^{2}-[R S E(y)]^{2}}\)

When calculating measures of error, it may be useful to convert RSE to SE. This allows the use of standard formulas involving the SE. The SE can be obtained from RSE using the following formula:

\(SE(y) = {RSE(y) \times\ y\over 100}\)

Calculating differences

The difference between two survey estimates (counts or percentages) can also be calculated from published estimates. Such an estimate is also subject to sampling error. The sampling error of the difference between two estimates depends on their SEs and the relationship (correlation) between them. An approximate SE of the difference between two estimates (x-y) may be calculated by the following formula:

\(S E(x-y) \approx \sqrt{[S E(x)]^{2}+[S E(y)]^{2}}\)

While this formula will only be exact for differences between separate and uncorrelated characteristics or sub populations, it provides a good approximation for the differences likely to be of interest in this publication.

Significance testing

When comparing estimates between surveys or between populations within a survey, it is useful to determine whether apparent differences are 'real' differences or simply the product of differences between the survey samples. 

One way to examine this is to determine whether the difference between the estimates is statistically significant. This is done by calculating the standard error of the difference between two estimates (x and y) and using that to calculate the test statistic using the formula below. 



\(SE(y) = {RSE(y) \times\ y\over 100}\)

If the value of this test statistic is greater than 1.96, there is good evidence of a statistically significant difference at 95% confidence levels between the two populations with respect to that characteristic. Otherwise, it cannot be stated with confidence that there is a real difference between the populations.

Significant differences identified by the ABS have been annotated with a footnote in select published tables. In all other tables which do not show the results of significance testing, users should take RSEs into account when comparing estimates for different populations, or undertake significance testing using the formula provided to determine whether there is a statistically significant difference between any two estimates.

Data quality

Victimisation surveys are best suited to measuring crimes against specific individuals. However, respondents need to be aware of and recall what happened to them and how it happened, as well as be willing to relate what they know to interviewers.

Crimes which rely on deception, such as fraud, can be more challenging to recognise, and therefore may not be fully represented in the data collected.

Information collected in this survey is essentially 'as reported' by respondents, and hence may differ from that which might be obtained from other surveys or administrative data sources. This factor should be considered when interpreting the estimates and when making comparisons with other data sources.

Statistical measures of personal fraud

The level of victimisation can be measured and expressed in more than one way. The most common measure derived from Personal Fraud Surveys is prevalence, that is, the number of the relevant population that have experienced a given crime at least once in the reference period. Victimisation rates used in this publication represent the prevalence of selected crimes in Australia, and are expressed as a percentage of the total relevant population. Reporting rates used in this publication are expressed as the percentage of persons whose most recent/serious incident of each type of crime had been reported to an authority.


Country of birth

Country of birth data are classified according to the Standard Australian Classification of Countries (SACC), 2016


Education data are coded to the Australian Standard Classification of Education (ASCED), 2001. The ASCED is a national standard classification which can be applied to all sectors of the Australian education system including schools, vocational education and training and higher education. The ASCED comprises two classifications: Level of Education and Field of Education.

Equivalised weekly household income

Equivalised weekly household income is household income adjusted by the application of an equivalence scale to facilitate comparison of income levels between households of differing size and composition, reflecting that a larger household would normally need more income than a smaller household to achieve the same standard of living. Using an equivalising factor for household income enables the direct comparison of the relative economic well-being of households of different size and composition (for example, lone person households, families and group households of unrelated individuals).

For more information about equivalised weekly household income see Household Income and Wealth, Australia and Survey of Income and Housing, User Guide, Australia.

Socio-Economic Indexes for Areas (SEIFA)

This survey uses the 2016 Socio-Economic Indexes for Areas (SEIFA).  

SEIFA is a suite of four summary measures that have been created from 2016 Census information. Each index summarises a different aspect of the socio-economic conditions of people living in an area. The indexes provide more general measures of socio-economic status than is given by measures such as income or unemployment alone.

For each index, every geographic area in Australia is given a SEIFA number which shows how disadvantaged that area is compared with other areas in Australia.

Two indexes are used in this publication – the Index of Relative Socio-Economic Advantage and Disadvantage; and the Index of Relative Socio-Economic Disadvantage. These measures are derived from Census variables related to income, educational attainment, unemployment, occupational skill level and whether a dwelling has a motor vehicle.

SEIFA uses a broad definition of relative socio-economic disadvantage in terms of people's access to material and social resources, and their ability to participate in society. While SEIFA represents an average of all people living in an area, it does not represent the individual situation of each person. Larger areas are more likely to have greater diversity of people and households.

For more detail, and for the SEIFA 2016 Technical paper (under Downloads), go to Census of Population and Housing: Socio-Economic Indexes for Areas (SEIFA), Australia, 2016.

Comparing the data

Comparability of Time Series

As a similar methodology has been adopted across successive Personal Fraud Survey cycles, data on the prevalence of personal fraud is comparable across the survey periods. This has enabled some time series comparisons of victimisation rates to be made in this publication. Reporting rate figures in 2020-21 are not comparable with results from previous surveys due to changes in the question wording.

Comparability to monthly LFS Statistics

Since the survey is conducted as a supplement to the LFS, data items collected in the LFS are also available in this publication. However, there are some important differences between the two surveys. The scope of the Personal Fraud Survey and the LFS differ (refer to the Scope section above). Due to the differences between the samples, data from this survey and the LFS are weighted separately. Differences may therefore be found in the estimates for those data items collected in the LFS and published as part of the Personal Fraud Survey.

Data release


Datacubes containing all tables for this publication in Excel spreadsheet format are available from the Data downloads section of the main publication. The spreadsheets present tables of estimates and proportions, and their corresponding relative standard errors (RSEs).

As well as the statistics included in this and related publications, the ABS may be able to provide other relevant data on request. Subject to confidentiality and sampling variability constraints, tables can be tailored to individual requirements for a fee. A list of data items from this survey is available from the Data downloads section. For inquiries about these and related statistics, contact the Customer Assistance Service via the ABS website Contact Us page.


To minimise the risk of identifying individuals in aggregate statistics, a technique is used to randomly adjust cell values. This technique is called perturbation. Perturbation involves a small random adjustment of the statistics and is considered the most satisfactory technique for avoiding the release of identifiable statistics while maximising the range of information that can be released. These adjustments have a negligible impact on the underlying pattern of the statistics. After perturbation, a given published cell value will be consistent across all tables. However, adding up cell values to derive a total will not necessarily give the same result as published totals.

Perturbation has been applied to Personal Fraud Survey datasets from the 2014-15 survey onwards. Data from cycles prior to 2014-15 (i.e. 2007 and 2010-11) have not been perturbed, but underwent a different confidentialisation method to protect the confidentiality of respondents.


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Balance of state/territory

Comprises statistical areas outside the Greater Capital City Statistical Areas of states and territories as defined in the Australian Statistical Geography Standard (ASGS): Volume 1 - Main Structure and Greater Capital City Statistical Areas, July 2016.

Betting or sports investment scams

A fake offer to invest in ‘foolproof’ betting systems or software that offer returns on sporting events.

Buying or selling scam

Scams involving requests for payment of fake invoices or products, or the purchase of non-existent, stolen or counterfeit goods. Includes false billing, classifieds, overpayments, and online shopping scams.

Capital city

Refers to the Greater Capital City Statistical Areas of states and territories as defined in the Australian Statistical Geography Standard (ASGS): Volume 1 - Main Structure and Greater Capital City Statistical Areas, July 2016.

Card fraud

Card fraud involves the use of credit, debit, or EFTPOS card details to make purchases or withdraw cash without the account owner's permission.

Charity scam

A fake request to donate to a charity or person in need. Includes fraudulent impersonation of real charities.

Computer support scam

A computer support scam is a fake offer over the phone, or through other means, to fix a problem with a computer/laptop (e.g. malware removal).

Degree/diploma or higher education

Degree/diploma or higher education includes education qualifications of postgraduate degree, graduate diploma or graduate certificate, bachelor degree and advanced diploma or diploma.


All people aged 15 years and over who met one of the following criteria during the reference week:

  • Worked for one hour or more for pay, profit, commission or payment in kind, in a job or business or on a farm (employees and owner managers of incorporated or unincorporated enterprises).
  • Worked for one hour or more without pay in a family business or on a farm (contributing family workers).
  • Were employees who had a job but were not at work and were:
    • away from work for less than four weeks up to the end of the reference week; or
    • away from work for more than four weeks up to the end of the reference week and received pay for some or all of the four week period to the end of the reference week; or
    • away from work as a standard work or shift arrangement; or
    • on strike or locked out; or
    • on workers' compensation and expected to return to their job.
  • Were owner managers who had a job, business or farm, but were not at work.

Employment scam

A fake offer of a job, franchise, or other business opportunity, often involving working from home.

Equivalised weekly household income

Equivalised household weekly income is gross weekly household income adjusted using an equivalence scale. For a lone person household it is equal to gross weekly household income. For a household comprising more than one person, it is an indicator of the gross weekly household income that would need to be received by a lone person household to enjoy the same level of economic well-being as the household in question.

Exposed to a scam

A person was considered to have been exposed to a scam if they had received an unsolicited invitation, request, notification or offer, and read, viewed, or listened to the material. This is a measure of how many people are encountering scams in the community. In this survey, mere exposure to a scam does not constitute being a victim of a scam.

Financial advice scam

A fake offer of an investment or any other financial advice with a guaranteed high return.

Full-time (employed)

Employed people who usually worked 35 hours or more a week (in all jobs) and those who, although usually working less than 35 hours a week, worked 35 hours or more during the reference week (i.e. the week before the interview).


Fraud is the act of intentionally deceiving another for the purpose of gaining an advantage or benefit, whether financial or otherwise.

Greater Capital City Statistical Areas (GCCSA)

Greater Capital City Statistical Areas (GCCSA) are geographical areas built from Statistical Areas Level 4 (SA4), as defined in the Australian Statistical Geography Standard (ASGS): Volume 1 - Main Structure and Greater Capital City Statistical Areas, July 2016. They are designed to represent the functional extent of each of the eight State and Territory capital cities. This includes the population within the urban area of the city, as well as people who regularly socialise, shop or work within the city, and live in small towns and rural areas surrounding the city. Within each State and Territory, the area not defined as being part of the Greater Capital City is represented by a Balance of State/Territory region.

Identity theft

Identity theft is the use of someone's personal details in stolen, fraudulent or forged documents without permission, or otherwise illegally appropriating another's identity.


A single occurrence of a crime event, which may involve one or more crime types.

Information request (or phishing) scam

An information request scam is where a person receives a fake notification or request from a bank, business, or other organisation to confirm personal details.

Labour force status

A classification of the civilian population aged 15 years and over, including employed, unemployed or not in the labour force, as defined in Labour Force, Australia. These definitions conform closely to the international standard definitions adopted by the International Conference of Labour Statisticians.

Level of highest non-school qualification

Non-school qualifications are awarded for education attainments other than those of pre-primary, primary or secondary education. They include qualification at the following levels: Postgraduate degree, Master degree, Graduate Diploma and Graduate Certificate, Bachelor degree, Advanced Diploma and Diploma and Certificates I, II, III and IV. Non-school qualifications may be attained concurrently with school qualifications.

Lottery scam

A lottery scam is where a person receives a fraudulent notification of having won a lottery or prize for a draw that they did not enter, and are asked to provide personal details or pay a fee in order to collect their prize or winnings. 

Non-school qualification

Non-school qualifications are awarded for educational attainments other than those of pre-primary, primary or secondary education. They include qualifications at the postgraduate degree level, master degree level, graduate diploma and graduate certificate level, bachelor degree level, advanced diploma and diploma level, and certificates I, II, III and IV levels. Non-school qualifications may be attained concurrently with school qualifications.

Not in the labour force

Persons who were neither employed nor unemployed as defined by Labour Force Status.

Other non-school qualification

Other non-school qualification includes education qualifications of certificate I, II, III or IV, or other certificates that are not further defined.

Other type of scam

Any other scam not separately identified in the survey.

Part-time (employed)

Employed people who usually worked less than 35 hours a week (in all jobs) and either did so during the reference week (i.e. the week before the interview), or were not at work in the reference week.

Pyramid schemes

Pyramid schemes are a multi-level selling technique where the main feature is that earning money and gaining promotion depends on recruiting other people into the operations rather than selling a product or providing a service.


Refers to a formal certification, issued by a relevant approved body, in recognition that a person has achieved an appropriate level of learning outcomes or competencies relevant to identified individual, professional, industry or community needs. Excludes statements of attainment awarded for partial completion of a course of study at a particular level.

Romance or relationship scam

A relationship scam is a request from someone the respondent has built an online relationship with (for example, through social media or an online dating website) for money or the respondent's bank details, where the person is pretending to be romantically interested for the purposes of financial gain.

Reporting rate

Reporting rate refers to the number of persons who reported the most recent/serious incident to an authority, expressed as a proportion of all persons who experienced the fraud type.

Relative standard error

A measure of the extent to which an estimate might have varied by chance because only a sample of dwellings was surveyed, and not the entire in-scope population. Relative standard error (RSE) is obtained by expressing the standard error as a percentage of the estimate.

Responded to a scam

A person is considered to have responded to a scam if, after being exposed to the scam, they sought further information, provided money or personal information, or accessed links associated with the scam. In this survey, only those who responded to a scam were considered to have been a victim of (experienced) a scam.


A scam is a fraudulent invitation, request, notification or offer, designed to obtain personal information or money or otherwise obtain a financial benefit by deceptive means.

Scam exposure rate

Scam exposure rate refers to the number of persons who were exposed to a scam, expressed as a percentage of total persons.

Scam responding rate

Scam responding rate refers to the number of persons who responded to a scam, expressed as a percentage of total persons who were exposed to a scam.

Scam victimisation rate

Scam victimisation rate refers to the number of persons who responded to a scam, expressed as a percentage of total persons.

Threats or extortion scams

Scams involving threats of legal action, arrest, death or other harm if demands are not met.


People aged 15 years and over who were not employed during the reference week (i.e. the week before the interview), and:

  • had actively looked for full-time or part-time work at any time in the four weeks up to the end of the reference week and were available for work in the reference week
  • were waiting to start a new job within four weeks from the end of the reference week and could have started in the reference week if the job had been available then.

Up-front payment scam

A request to send money or banking details in return for the false promise of a monetary payment or entitlement. Includes offers of unexpected money from inheritance, rebates, and international money transfer scams.


A victim is a person who has experienced card fraud, identity theft, or responded to one or more selected scams.

Victimisation rate

Victimisation rate is the number of persons in a population who experienced a personal fraud type, expressed as a proportion of that population. This is a measure of how prevalent a crime type is in a given population, and is used to measure changes in crime rates over time.


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ABSAustralian Bureau of Statistics
ASCEDAustralian Standard Classification of Education
ASGSAustralian Statistical Geography Standard
CAIcomputer assisted interview
ERPEstimated Resident Population
GCCSAGreater Capital City Statistical Areas
ICSIndigenous Community Strata
LFSLabour Force Survey
MPHSMultipurpose Household Survey
PFSPersonal Fraud Survey
RSErelative standard error
SACCStandard Australian Classification of Countries
SEstandard error
SEIFASocio-Economic Indexes for Areas
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