Latest release

# Personal Fraud methodology

Reference period
2014-15 financial year
Released
20/04/2016
Next release Unknown
First release

## Explanatory notes

### Introduction

1 The statistics presented in this release were compiled from data collected in the Australian Bureau of Statistics' (ABS) 2014-15 Multipurpose Household Survey (MPHS). The MPHS is conducted each financial year throughout Australia from July to June as a supplement to the ABS' monthly Labour Force Survey (LFS) and is designed to provide annual statistics for a number of small, self-contained topics.

2 In 2014-15, the topics were:

• Barriers and Incentives to Labour Force Participation
• Retirement and Retirement Intentions (including Method of Meeting Current Living Costs)
• Household Use of Information Technology
• Patient Experience
• Crime Victimisation (including Personal Fraud)
• Income (Personal, Partner's, Household).

3 For all topics, general demographic information such as age, sex, labour force characteristics, education and income are also available.

4 The Personal Fraud Survey collected information from individuals about their experience of selected personal fraud in the 12 months prior to interview (except for identity theft where persons were asked if they had ever been a victim of identity theft and then data were collected about experiences in the five years and 12 months prior to interview), and whether they incurred any financial loss. Detailed characteristics of persons who experienced fraud and incidents of fraud were also collected.

### Scope

5 The scope of the LFS is restricted to people aged 15 years and over who were usual residents of private dwellings, except:

• members of the permanent defence forces
• certain diplomatic personnel of overseas governments, customarily excluded from census and estimated populations
• overseas residents in Australia
• members of non-Australian defence forces (and their dependants).

6 In addition, the 2014-15 MPHS also excluded the following from its scope:

• households in Indigenous Communities
• persons living in non-private dwellings (e.g. hotels, university residences, students at boarding schools, patients in hospitals, inmates of prisons and residents of other institutions (e.g. retirement homes, homes for persons with disabilities).

7 As indicated above, the scope of the MPHS excluded persons living in very remote parts of Australia. The exclusion of these people is unlikely to impact on state and territory estimates, except in the Northern Territory where they account for approximately 23% of the total population.

### Coverage

8 The coverage of the 2014-15 MPHS was the same as the scope, except that persons living in Indigenous Communities in non-very remote areas were not covered for operational reasons.

9 In the LFS, rules are applied which aim to ensure that each person in coverage is associated with only one dwelling and hence has only one chance of selection in the survey. See Labour Force, Australia (cat. no. 6202.0) for more details.

### Data collection

10 The MPHS was conducted as a supplement to the monthly LFS. Each month one eighth of the dwellings in the LFS sample were rotated out of the survey. In 2014-15, all of these dwellings were selected to respond to the MPHS each month. In these dwellings, after the LFS had been fully completed for each person in scope and coverage, a person aged 15 years and over was selected at random (based on a computer algorithm) and asked the various MPHS topic questions in a personal interview. If the randomly selected person was aged 15–17 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 personal fraud questions on behalf of the 15–17 year old. Data were collected using Computer Assisted Interviewing (CAI), whereby responses were recorded directly onto an electronic questionnaire in a notebook computer, usually during a telephone interview.

11 For the 2014-15 MPHS, the sample was accumulated over a 12 month period from July 2014 to June 2015.

12 The publication Labour Force, Australia (cat. no. 6202.0) contains definitions of demographic and labour force characteristics, and information about telephone interviewing that is relevant to both the monthly LFS and MPHS.

### Sample size

13 The initial sample for the personal fraud topic was 44,786 private dwellings, from which one person was randomly selected. Of the 37,701 private dwellings that remained in the survey after sample loss (for example, dwellings selected in the survey which had no residents in scope for the LFS, vacant or derelict dwellings and dwellings under construction), 27,341 or 73.7% fully responded to the questions on personal fraud victimisation.

### What is new in the 2014-2015 personal fraud survey?

14 There have been a number of changes from the 2010-11 Personal Fraud Survey that have affected the availability or comparability of some data items in the 2014-15 Survey. Information about characteristics of incidents of personal fraud are not comparable across the two reference periods. The 2010-11 survey collected detailed characteristics about all incidents in the last 12 months for each fraud type. The 2014-15 survey collected details on the most recent incident only.

15 Some of the characteristics of the incidents that were collected also changed between 2014-15 and 2010-11. The 2014-15 survey collected data about time lost and behaviour change due to the fraud incident for each fraud type, for the first time. The 2010-11 survey collected additional data items about incidents of personal fraud types, such as information about joint account holder status, and how many cards were subjected to card fraud.

16 Information about total financial loss for all incidents of each type of fraud in the last 12 months was collected in both 2014-15 and 2010-11 and is therefore comparable.

### Card fraud

17 Additional questions were asked in the 2014-15 survey on the impact of fraud incidents. These included whether the respondent had discovered the most recent incident of fraud or someone else had informed them, the amount of time lost in the most recent incident, and whether their behaviour has changed as a result of the fraud incident. To accommodate these new questions, some information collected in the 2010-11 survey was not included in 2014-15: the number of cards fraudulently used, whether these were part of a joint account with another person, and the number of joint account holders.

### Identity theft

18 Due to changes in the question regarding experience of identity theft, data from 2014-15 are not comparable with those from 2010-11.

19 The number of response categories for 'how details were obtained' was increased in 2014-15 to reflect current common methods, such as social media. Additional questions were asked in the 2014-15 survey on the impact of identity theft incidents. These included the amount of time lost in the most recent incident, and whether behaviour has changed as a result of the fraud incident.

### Scam fraud

20 In 2014-15, additional information was collected on how the respondent became aware the incident was a scam. Additional questions were also asked on the impact of scam incidents, including the amount of time lost in the most recent incident, and whether behaviour has changed as a result of the fraud incident.

21 Some of the selected scam types included in 2014-15 differ from those in 2010-11, and it is not recommended to directly compare data by scam type. The two categories 'lottery' and 'pyramid scheme' are consistent between the two surveys. Information on additional scam types was included in 2014-15. These were: 'information request', 'relationship', 'up-front payment', 'financial advice', 'computer support', 'working from home', and 'online trading or auction site'.

### What is personal fraud?

22 In this survey, personal fraud comprises:

• card fraud
• identity theft
• selected scams, which include:
• lottery
• information request
• pyramid scheme
• relationship
• up-front payment
• computer support
• working from home
• online trading or auction site
• other scams.

### Identity fraud

23 A person was defined as having experienced identity fraud if they had their credit, debit or EFTPOS card, or other personal details or documents, such as driver’s licence, tax file number or passport, used by another person for unauthorised gain. This included instances where business transactions were conducted or accounts opened in the individual’s name without permission, or any other uses of their identity without permission. Persons who became aware of an occurrence of identity fraud against them were considered to have experienced identity fraud.

24 The survey sought to establish the number of incidents of card fraud or identity theft that were experienced, that is, the number of times the respondent had their personal or financial details stolen. The survey did not collect the number of individual transactions or cash withdrawals that occurred in each incident before the breach was detected. For example, if a respondent's card was stolen and was used to make five transactions before the card was cancelled, only the one incident of the card being stolen and used fraudulently was counted.

### Scams

25 A person was defined as having experienced a scam if they were not only exposed to a scam or fraudulent offer, but also responded to a scam invitation, request, notification or offer by way of supplying personal information, money or both, or if they sought more information from the sender of the scam.

### Counts of persons experiencing personal fraud

26 A person could have experienced one or more selected personal fraud types; where this was the case they were counted in each personal fraud type. For example, a person may have experienced both a relationship scam and a lottery scam. This person would be counted in both scam categories. A total count of persons experiencing all types of personal fraud is also available, but persons are only counted once in the totals. Using the previous example, the total would only count this person once even though two incident types occurred. Components therefore will not always add to the total counts in the publication.

### Socio-demographic characteristics

27 Socio-demographic characteristics, such as age, sex, labour force status and personal weekly income were collected about all respondents. The survey provides a profile of these characteristics for each type of personal fraud.

### Incident characteristics

28 Detailed characteristics (such as method of fraud, reporting of incidents, and financial loss) of each type of fraud were collected for the most recent (card fraud and identity theft) or most serious (scams) incident of each fraud type only in 2014-15.

### Total financial loss

29 For each different type of personal fraud, individuals were asked to report the amount of money they lost as a result of all incidents. For card fraud this refers to the total financial loss before any reimbursement from the card issuer. Information is reported separately for the amount of money lost after reimbursement for card fraud.

30 Where mean, median and total financial losses are reported in this publication, the total financial loss before any reimbursement from the card issuer is used.

### Equivalised weekly household income

31 Equivalence scales are used to adjust the actual incomes of households in a way that enables the analysis of the relative well-being of people living in households of different size and composition. For example, it would be expected that a household comprising two people would normally need more income than a lone person household if all the people in the two households are to enjoy the same material standards of living. Adopting a per capita analysis would address one aspect of household size difference, but would address neither compositional difference (i.e. the number of adults compared with the number of children) nor the economies derived from living together.

32 When household income is adjusted according to an equivalence scale, the equivalised income can be viewed as an indicator of the economic resources available to a standardised household. For a lone person household, it is equal to income received. For a household comprising more than one person, equivalised income is an indicator of the household income that would be required by a lone person household in order to enjoy the same level of economic well-being as the household in question.

33 The equivalence scale used in this publication was developed for the Organisation for Economic Co-operation and Development and is referred to as the 'modified OECD' equivalence scale. It is widely accepted among Australian analysts of income distribution.

34 The scale allocates 1.0 point for the first adult (aged 15 years and over) in a household; 0.5 for each additional adult; and 0.3 for each child. Equivalised household income is derived by dividing total household income by the sum of the equivalence points allocated to Australian household members. For example, if a household received combined gross income of $2,100 per week and comprised two adults and two children (combined household equivalence points of 2.1), the equivalised gross household income would be calculated as$1,000 per week.

35 For more information on the use of equivalence scales, see Household Income and Wealth, Australia (cat. no. 6523.0).

### Weighting

36 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 sample unit, which, for the MPHS, can be either a person or a household. The weight is a value which indicates how many population units are represented by the sample unit. For the MPHS, the first step in calculating weights for each unit was 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 (i.e. they represent 600 people).

### Benchmarking

37 The initial weights were then calibrated to align with independent estimates of the population of interest, referred to as 'benchmarks', in designated categories of age by sex by area of usual residence. 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.

38 For person estimates, the MPHS was benchmarked to the projected population in each state and territory, as at 31 March 2014. For household estimates, the MPHS was benchmarked to independently calculated estimates of the total number of households in Australia. The MPHS estimates do not (and are not intended to) match estimates for the total Australian person/household populations obtained from other sources.

### Estimation

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

### Confidentiality

40 To minimise the risk of identifying individuals in aggregate statistics, a technique called perturbation is used to randomly adjust cell values. 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 only been applied to data from 2014-15.

41 For data from previous cycles (2007 and 2010-11), only cells containing small values were randomly adjusted to avoid releasing confidential information, a technique known as randomisation. One effect of randomisation is that totals may vary slightly across tables. These adjustments do not impair the value of the tables as a whole.

### Reliability of estimates

42 All sample surveys are subject to error which can be broadly categorised as:

•  sampling error
•  non-sampling error.

### Sampling error

43 Sampling error is the difference between the published estimates, derived from a sample of persons, and the value that would have been produced if the total population (as defined for the scope of the survey) had been included in the survey. For more information refer to the Technical Note.

### Non-sampling error

44 Non-sampling error may occur in any collection, whether it is based on a sample or a full count such as a census. Sources of non-sampling error include non-response, errors in reporting by respondents or recording of answers by interviewers and errors in coding and processing data. Every effort is made to reduce non-sampling error by careful design and testing of questionnaires, training and supervision of interviewers, and extensive editing and quality control procedures at all stages of data processing.

### Comparability with monthly LFS statistics

45 Due to differences in the scope and sample size of the MPHS and that of the LFS, the estimation procedure may lead to some small variations between labour force estimates from this survey and those from the LFS.

### Other methodological issues

46 When interpreting data from the 2014-15 MPHS, consideration should be given to the representativeness of the survey sample in relation to the entire in-scope population. This is affected by the response rate and scope and coverage rules. For example, people living in boarding houses, refuges or on the streets are excluded from this survey and may experience different levels of victimisation than those surveyed who live in private dwellings.

### Classifications

47 Country of birth data are classified according to the Standard Australian Classification of Countries (Second Edition) (SACC), 2011 (cat. no. 1269.0).

48 Educational attainment data are classified according to the Australian Standard Classification of Education (ASCED), 2001 (cat. no. 1272.0).

### Products and services

49 All data tables are available in Excel spreadsheet format and can be accessed from the Data downloads section. The data tables contain number and proportion estimates, and their corresponding relative standard errors.

### Data available on request

50 A further option for accessing data from the Personal Fraud Survey is to contact the National Information and Referral Service. A range of additional data not provided in the standard spreadsheets may be provided on a fee-for-service basis through ABS Information Consultancy. A spreadsheet containing a complete list of the data items available from the Personal Fraud Survey can be accessed from the Data downloads section.

### Acknowledgments

51 ABS surveys draw extensively on information provided freely by individuals, businesses, governments and other organisations. Their continued cooperation is very much appreciated. Without it the wide range of statistics published by the ABS would not be available. Information received by the ABS is treated in strict confidence as required by the Census and Statistics Act 1905.

## Technical note

### Reliability of the estimates

1 The estimates in this publication are based on information obtained from a sample survey. Errors in data collection or processing, known as non-sampling error, can impact on the reliability of the resulting statistics. In addition, estimates based on sample surveys are subject to sampling error. That is, the estimates may differ from the true value of the characteristics being measured that would have been obtained had all persons in the population been included in the survey.

### Non-sampling error

2 Non-sampling error may occur in any statistical collection, whether it is based on a sample or a full count such as a census. Sources of non-sampling error include non-response, errors in reporting by respondents or recording of answers by interviewers, and errors in coding and processing data. Every effort is made to reduce non-sampling error by careful design and testing of questionnaires, training and supervision of interviewers, and extensive editing and quality control procedures at all stages of data processing.

### Sampling error

3 Sampling error refers to the difference between an estimate obtained from surveying a sample of persons, and the true value of the characteristic being measured that would have been obtained if the entire in-scope population was surveyed. Sampling error can be measured in a standardised way using standard error (SE) calculations, which indicate the extent to which an estimate might have varied by chance because only a sample of persons was surveyed. There are about two chances in three (67%) that a sample estimate will differ by less than one SE from the number that would have been obtained if all persons had been surveyed, and about 19 chances in 20 (95%) that the difference will be less than two SEs.

4 In this publication, the standard error of the estimate is given as a percentage of the estimate it relates to, known as the relative standard error (RSE).

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

5 RSEs for all estimates have been calculated using the Jackknife method of variance estimation. This involves the calculation of 30 'replicate' estimates based on 30 different sub-samples of the obtained sample. The variability of estimates obtained from these sub-samples is used to estimate the sample variability surrounding the estimate.

6 The Excel files available from the Data downloads section contain all the tables produced for this release, including all estimates and their corresponding RSEs.

7 Only estimates (numbers or percentages) with RSEs less than 25% are considered sufficiently reliable for most analytical purposes. However, estimates with RSEs over 25% have also been included. Estimates with an RSE in the range 25% to 50% are less reliable and should be used with caution, while estimates with RSEs greater than 50% are considered too unreliable for general use. All cells in the publication tables containing an estimate with an RSE of 25% or over have a cell comment attached, indicating whether the RSE of the estimate is in the range 25-49% or is over 50%. These cells can be identified by a red indicator in the corner of the cell. The comment appears when the mouse pointer hovers over the cell.

### Calculation of standard error

8 Standard error (SE) can be calculated using the estimate (count or percentage) and the corresponding RSE. For example, Table 1 shows that the estimated number of persons who experienced personal fraud in the last 12 months was 1,592,400 with a corresponding RSE of 2.1%. The SE (rounded to the nearest 100) is calculated by:

$$\begin{array}{l}{ SE\ of \ estimate} \\ {=\left(\frac{R S E \%}{100}\right) \times estimate} \\ {=0.021 \times 1,592,400} \\ {=33,400}\end{array}$$

9 Therefore, there is about a two in three chance that the true value, which would have been obtained had all persons been included in the survey, falls within the range of one standard error below to one standard error above the estimate (1,559,000 to 1,625,800), and about a 19 in 20 chance that the true value falls within the range of two standard errors below to two standard errors above the estimate (1,525,600 to 1,659,200). This example is illustrated in the diagram below:

There are about two chances in three that the value that would have been produced if all dwellings had been included in the survey will fall within the range 1559.0 to 1625.8 and about 19 chances in 20 that the value will fall within the range 1525.6 to 1659.2.

### Proportions and percentages

10 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 x is a subset of y:

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

11 As an example, using data from Table 3, 765,300 persons experienced a single incident of card fraud, representing 70% of all persons who experienced card fraud (1.1 million). The RSE for the number of persons experiencing one incident of card fraud is 3.7% and the RSE for the total number of persons experiencing card fraud is 3.0%. Applying the above formula, the RSE of the proportion (70%) is:

$$R S E=\sqrt{[(3.7)]^{2}-[(3.0)]^{2}}=2.2 \%$$

12 Using the formula given in technical note 8 above, the standard error (SE) for the proportion of persons experiencing card fraud who experienced a single incident is 1.5% (0.022 x 70.0). Hence, there are about two chances in three that the true proportion of persons experiencing card fraud who experienced a single incident is between 68.5% and 71.5%, and 19 chances in 20 that the true proportion is between 67.0% and 73.0%.

### Differences

13 Standard error can also be calculated on the difference between two survey estimates (counts or percentages). The standard error of the difference between two estimates is determined by the individual standard errors of the two estimates and the relationship (correlation) between them. An approximate standard error of the difference between two estimates (x,y) can be calculated using the following formula:

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

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

### Significance testing

15 The difference between two survey estimates can be tested for statistical significance, in order to determine the likelihood of there being a real difference between the populations with respect to the characteristic being measured. The standard error of the difference between two survey estimates (x and y) can be calculated using the formula shown above in technical note 13. This standard error is then used in the following formula to calculate the test statistic:

$$\LARGE{\left(\frac{x-y}{S E(x-y)}\right)}$$

16 If the value of the test statistic is greater than 1.96 then this supports, with a 95% level of confidence, a real difference (i.e. statistically significant) between the two populations with respect to the characteristic being measured. If the test statistic is not greater than 1.96, it cannot be stated with confidence that there is a real difference between the populations with respect to that characteristic.

17 Changes in personal fraud victimisation rates between 2014-15 and 2007 and 2010-11 respectively have been tested to determine whether the change is statistically significant. Significant differences have been annotated with a cell comment. In all other tables which do not show the results of significance testing, users should take account of RSEs when comparing estimates for different populations, or undertake significance testing using the formula provided in technical note 15 to determine whether there is a statistical difference between any two estimates.

## Glossary

### Show all

#### 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.

#### 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.

#### 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.

#### Exposure to scams

Scams operate by presenting a person with a deceptive story, request or other lure, which results in the person responding to the scam in some manner, such as by providing or verifying personal details or money to the scammer. In this survey, a person was considered to have been exposed to a scam if they had:

• read or viewed the material.

It was not sufficient for the person to have received a piece of correspondence which was simply unwanted. Notifications or invitations which were received via email, but bypassed the person's inbox and were removed by a spam filter were excluded if the respondent did not open the message.

A financial advice scam is a fake offer to supply financial advice about topics such as investment and shares.

#### Financial loss

Financial loss relates to all incidents experienced in the last 12 months (as well as 5 years for identity theft). Respondents were asked to provide an approximate total amount of money lost as a result of all their incidents of each fraud type. For card frauds, this amounted to money lost before any form of reimbursement from authorities. For scams, this amounted to money lost through responding to the fraudulent invitation, request, notification or offer.

#### Fraud

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

#### Identity fraud

Includes identity theft and card fraud.

#### 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.

#### Information request

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

#### 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. This excludes registered lotteries such as Readers Digest.

#### Mean financial loss

The mean financial loss is calculated by dividing total financial loss by the number of persons who had lost money.

#### Median financial loss

Median financial loss is calculated by arranging, from smallest to largest, the total financial losses for each person who lost money to personal fraud. The median is the number that divides these data into two equal parts, one half having total financial losses below the median and the other half having total financial losses above the median.

#### 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.

#### Online trading or auction site scam

An online trading or auction site scam is a fake offer to purchase goods advertised for sale, or the purchase of non-existent, stolen, or counterfeit goods.

#### 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

Other scams are scams not separately identified in the survey. These include fraudulent door-to-door sales, fraudulent repair work, advance fee scams, unsolicited fraudulent financial advice, and others.

#### Personal weekly income

Personal weekly income is gross weekly personal income from all sources.

#### 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.

#### 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.

#### Reporting rate

Reporting rate refers to the total number of persons who experienced personal fraud and reported the incident to an authority, expressed as a percentage of all persons.

#### Responding rate

Responding rate refers to the total number of persons who responded to a scam (by supplying personal information, money or both, or by seeking more information in relation to the request), expressed as a percentage of the total number of persons exposed to the scam.

#### 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.

#### Total financial loss after reimbursement

For persons who experienced card fraud, this refers to the total financial loss after any reimbursement from the card issuer and reflects the out of pocket loss to the person.

#### Up-front payment scam

An up-front payment scam is a request from another person to send a large amount of money overseas, in return for a commission or fee paid for the assistance provided (for example, a Nigerian 419 scam).

#### Victimisation rate

Victimisation rate is the total number of persons who experienced a personal fraud type in a given population, expressed as a percentage of that population.

#### Victim

A victim is a person who has experienced card fraud, identity theft or a person who has not only received a fraudulent invitation, request or notification, but has also responded to that offer or request by supplying personal information, money or both, or seeking more information in relation to these requests.

#### Working from home scam

A working from home scam is a fake offer to work from home as a front for illegal activities (for example, money laundering).

## Quality declaration

### Institutional environment

For information on the institutional environment of the Australian Bureau of Statistics (ABS), including the legislative obligations of the ABS, financing and governance arrangements, and mechanisms for scrutiny of ABS operations, please see ABS Institutional Environment.

### Relevance

Data relating to people's experiences of selected types of personal fraud were collected as part of the 2014-15 Multipurpose Household Survey (MPHS). The MPHS is a supplement to the monthly Labour Force Survey (LFS) and is designed to collect annual statistics on a small number of self-contained topics. The scope of the LFS is restricted to people aged 15 years and over and excludes: members of the permanent defence forces; certain diplomatic personnel of overseas governments usually excluded from census and estimated resident populations; overseas residents in Australia; and members of non-Australian defence forces (and their dependants). Refer to Labour Force, Australia (cat. no. 6202.0) for further information regarding the LFS. In addition, the 2014-15 MPHS scope excluded people living in non-private dwellings such as hotels, university residences, students at boarding schools, patients in hospitals, inmates of prisons and residents of homes (e.g. retirement homes, homes for persons with disabilities). People living in Indigenous Community Frame (ICF) Collection Districts (CDs) were also excluded for operational reasons.

In the Personal Fraud Survey, respondents aged 15 years and over were asked questions about their experiences of selected types of personal fraud, including card fraud, identity theft and selected scam types (lottery, information request, pyramid scheme, relationship, up-front payment, financial advice, computer support, working from home, online trading or auction site, and other type of scam). Information was collected from one person selected at random in each selected household.

The survey has been designed to meet the growing need for data regarding the occurrence of personal fraud, and the changing prevalence and nature of fraud offences over time.

### Timeliness

As the survey reference period was the 12 months prior to the survey interview during 2014-15, the data relate to experiences occurring at some time between July 2013 and June 2015. Information about experiences of identity theft were also collected for a five year reference period, encompassing any incidents occurring between July 2009 and June 2015. Generally, results from the Personal Fraud Survey are released approximately 7–8 months after final enumeration.

### Accuracy

The 2014-15 Personal Fraud Survey comprised a sample of 27,341 fully responding households, which represented a response rate of 73%.

Two types of error are possible in an estimate based on a sample survey: non-sampling error and sampling error. Non-sampling error arises from inaccuracies in collecting, recording and processing the data. Every effort is made to minimise reporting error through carefully designed questionnaires, intensive training and supervision of interviewers, and efficient data processing procedures. Non-sampling error also arises because information cannot be obtained from all persons selected in the survey.

Sampling error occurs because a sample, rather than the entire population, is surveyed. One measure of the likely difference resulting from not including all dwellings in the survey is given by the standard error (SE). There are about two chances in three that a sample estimate will differ by less than one SE from the figure that would have been obtained if all dwellings had been included in the survey, and about 19 chances in 20 the difference will be less than two SEs. Measures of the relative standard errors (RSE) of the estimates for this survey are included with this release.

### Coherence

This is the third time that national data about personal fraud have been collected as part of the MPHS, with the previous two surveys being conducted in 2007 and 2010-11.

There have been a number of changes to survey content since it was last conducted in 2010-11, which affect the comparability of some data items across time periods. These changes were made following consultation with key stakeholders, in order to enable the survey to capture developments in the field of personal fraud.

The specified types of scams included in the 2014-15 survey have changed since the 2010-11 survey. Scam types that were included in the 2010-11 survey but not in the 2014-15 survey include chain letter, fake notification or offer from a bank or financial institution, fake notification or offer from an established business, and request to send bank or financial details to another person. New scam types that were added in the 2014-15 survey include relationship, up-front payment, financial advice, computer support, working from home, and online trading. Information on lottery and pyramid scheme scams was collected in both the 2010-11 and 2014-15 surveys.

Another change made since the 2010-11 survey relates to the way in which incident characteristics questions were asked. In 2010-11, incident characteristics questions were asked in relation to all incidents experienced. In 2014-15, the majority of incident characteristics questions were asked only in relation to the most recent incident experienced for card fraud and identity theft, and the most serious incident experienced (as judged by the respondent) for scams. The only two incident characteristics questions that were asked in relation to all incidents experienced in 2014-15 were whether behaviour has changed and how behaviour has changed as a result of all incidents experienced. Due to these changes, care should be taken when comparing incident characteristics information between the 2014-15 and 2010-11 surveys.

Due to changes in the question regarding experience of identity theft, data from 2014-15 are not comparable with those from 2010-11.

The terms used to describe the various types of offences in this publication may not necessarily correspond with legal or police definitions.

### Interpretability

To aid in the interpretation of the personal fraud data, detailed information on concepts, definitions, terminology and other technical aspects of the survey can be found in the relevant web pages included with this release. This includes the Explanatory Notes, Glossary, Abbreviations, and Technical Note.

### Accessibility

All tables containing estimates and associated RSEs are available in Excel spreadsheets and can be accessed from the Data downloads section. For the 2014-15 release, any RSEs greater than 50% have been suppressed, while the estimate has been released.

Additional data may also be available on request through an ABS customised data consultancy. The Data downloads section includes an Excel spreadsheet containing a complete list of the data items available.

For further information about these or related statistics, contact ABS National Information and Referral Service or on 1300 135 070. https://www4.abs.gov.au/web/survey.nsf/contactform/

## Abbreviations

### Show all

 ABS Australian Bureau of Statistics ACORN Australian Cybercrime Online Reporting Network APCA Australian Payment Clearing House ASCED Australian Standard Classification of Education CAI Computer Assisted Interviewing CDs Collection Districts GPS Global Positioning System ICF Indigenous Community Frame LFS Labour Force Survey MPHS Multipurpose Household Survey RSE relative standard error SACC Standard Australian Classification of Countries SE standard error