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  • Large super fund industry report

    The large super fund industry report summarises the collective outcomes from the 2015–16 risk differentiation framework (RDF) and tailored diagnostic reports. It is designed to be used by super fund trustees, executives, administrators, and reporting and risk managers.

    Each year, large super funds receive a tailored diagnostic report assessing their performance in meeting their reporting obligations to the ATO. These results help the funds understand their performance relative to their industry peers, and indicate reporting performance trends from year to year.

    Funds need to invest in and deliver SuperStream implementation, super legislation changes and Single Touch Payroll integration. Despite these challenges, they continue to demonstrate a willingness to invest in their reporting processes and governance, and improve their overall reporting performance.

    The large super fund industry report and each tailored diagnostic report help fund executives decide how and where to invest their resources to continue this improvement.

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    2016 RDF results

    The 2016 financial year large fund RDF reporting performance results are based on data extracted as at 31 December 2016. This is the third year we’ve provided diagnostic reports to large super funds.

    As well as giving us confidence in the quality of your member reporting, we’ve been pleased by the positive response from industry about the value of tailored diagnostic reports.

    We want to work with large funds to ensure the integrity of member reporting, and in turn provide a better experience for your members. Our intent is to improve the experience for both funds and members.

    The RDF conceptual and analytical model underpins our engagement strategy with large super funds in relation to super reporting obligations. It assesses fund performance across 13 quantitative indicators.

    Our analysis reveals a trend of continuous improvement in fund reporting over the past three years. In 2016, 99% of funds met their reporting obligations to a good or high standard.

    We want you to have confidence in these results, and be assured that recent ATO system incidents have not influenced the outcomes. We did extensive internal and external quality assurance testing of the data.

    Anyone dealing with the taxation of large business entities will be familiar with the concept of justified trust. Just as in tax, in the super context we seek sufficient evidence to indicate we can justifiably trust that an entity is meeting its super obligations.

    This approach allows us to focus more on support and partnership activities, and less on compliance. The idea is to encourage continuous improvement in super industry governance and risk management, while implementing significant structural changes across the sector. This in turn should yield benefits for super fund members and underpin the retirement incomes of all Australians.

    Given this year’s RDF results, we’re confident that super funds’ member reporting obligations are being met to a high standard, even in an environment of change. We recognise the industry’s commitment to continuous improvement, and we thank you for your efforts.

    Funds have been actively identifying member reporting errors and disclosing them voluntarily. We encourage you to continue to do this to improve the integrity of member reporting and ultimately provide a better experience for members.

    What we’re doing to support funds

    We held a webinar on 5 April 2017 to discuss the 2016 large fund diagnostic report. You can watch a video recording of the webinar, download the transcript and read answers to frequently asked questions.

    We met with key clients during April and May 2017 to discuss their results, and their readiness to implement SuperStream, Single Touch Payroll and super legislation changes.

    This year we will continue to provide support to those who need it most.

    Given the overall high quality of this year's results, we do not see a need for audits or a large program of compliance casework.

    However, we are planning information system risk assessments and specific issue reviews with funds that have not met benchmarks over several years, or that may have systemic issues across multiple reporting obligations. We’d like to help them improve their systems and processes.

    A high priority this year is to support funds going through a merger or successor fund transfer. We're aware of several mergers planned for this year, and we are setting up a special team to provide guidance and support for any fund requesting it.

    We will continue to:

    • help you resolve voluntary disclosures in relation to past reporting errors
    • provide lists of mismatched tax file numbers (TFNs) to help identify lost members
    • provide lists of members for you to investigate issues such as high value lost accounts, lost accounts under $6,000, and potentially unclaimed superannuation money (USM).

    We also have a focus on the integrity of the super system through checking:

    • tax agent compliance with the departing Australia superannuation payment (DASP) online system
    • the compliance of money finding organisations with their agreements (with the ATO) to search for lost super accounts on behalf of clients.

    Super in 2017 and beyond

    We recognise funds face many challenges. Given the large reform program, we will focus on providing funds with guidance and tailored communications.

    In consultation with industry, we have revised our SuperStream release timeframes, and we continue to co-design the forward program of work and new measures implementation.

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    SuperStream

    SuperStream has supported government rollovers since November 2016 following successful upgrades of ATO and super fund systems. Government contributions followed in May 2017, and SuperStream will support rollovers from funds to the ATO in early 2018.

    Mandatory reporting of opened and closed accounts using SuperTICK3 began on 31 March 2017. Funds also cut over to version 2 of the electronic portability form on 1 May 2017.

    Enhancements to the performance of our super services on the Standard Business Reporting 2 (or SBR2) platform (including SuperTICK3 and SuperMatch2) between April and July 2017 are also important.

    We are committed to maintaining a strong, coordinated consultation framework across the super industry to provide confidence, clarity and certainty on key dates, approaches and requirements. This applies to SuperStream, Single Touch Payroll, the super legislation changes and the potential for more event-based contributions reporting.

    New reporting solution

    We are working with funds to co-design a new reporting solution to replace the member contribution statement (MCS) and lost member statement (LMS). This will support ongoing SuperStream implementation, improved super guarantee (SG) compliance, and the 2016 Budget super changes.

    Formerly known as Member information eXchange (or MiX), the new standardised reporting framework will have more regular reporting arrangements. This will bring significant benefits for government and funds.

    It will also give members more accurate and up-to-date information about their super account balances – a 2016 Budget requirement. This requirement presented an opportunity to co-design the new event-based reporting solution, and in turn required us to redesign and improve our reporting framework.

    We worked closely with funds to tailor the existing RDF and its metrics so they can actively reform and improve reporting processes and performance. This partnership approach has seen several changes to the framework over the past three years.

    We will continue to tailor the reporting metrics, even as we partner with the industry on design and implementation.

    ATO system incidents

    In mid-December 2016 and again in early February 2017, significant ATO system incidents rendered online services unavailable – including super services. This affected funds’ ability to meet their processing obligations.

    The Australian Prudential Regulation Authority (APRA) provided specific guidance in relation to breach reporting where the breach could be shown to be directly related to these incidents. Most funds indicated they could quickly clear backlogs once services resumed, and we deferred USM and other production runs during January to help with this processing peak load.

    We recognise these incidents have affected the broader change agenda for 2017, and some of its elements have been deferred as a result.

    Our internal and external quality assurance processes in relation to data used to populate the RDF model assures us the recent system incidents have not influenced the results.

    We are now working to improve and rebuild confidence in our technology platforms and enabling services, and to continue our industry partnership approach.

    We recognise that ATO system stability and performance is critical to your business. The replacement of our storage area network (the cause of our recent system incidents) over Easter 2017 was a key activity.

    Super changes

    We have published content to support funds and the community since the government introduced new super legislation. This includes detailed web content, live stream and webinar events, a series of law companion guidelines and practical compliance guidelines. We have also developed guidance notes and other support material to help customer-facing staff in funds and other organisations provide advice that is consistent with advice being given by the ATO.

    We are consulting with funds, other stakeholders and audiences to develop appropriate and effective information and communication material. We update this material frequently and make it available to all stakeholders as early and often as possible. This ensures you can support and advise your members, and encourage them to prepare for the upcoming changes to maximise their super benefits.

    Understanding the RDF

    The 2016 RDF-tailored diagnostic reports examine each fund’s:

    • consequence rating based on relative rank using key indicators such as member numbers, contributions value, and lost accounts number/value
    • likelihood rating based on performance against super-related obligations benchmarks.

    Figure 1: The risk differentiation framework

    Figure 1 The risk differentiation framework. The graph shows the 4 risk categories and the likeliness of compliance. The Y axis shows the consequence of non-compliance. The X axis shows the likelihood of non-compliance. The 4 risk categories form a square along these axis'. The bottom left square is the 'Lower Risk' category which reads 92%. The top left square, further up the consequence of non-compliance side, reads the 'Key Client' category at 7%. The bottom right square, along the likelihood of non-compliance, has the 'Medium Risk' category which reads 1%. The top right section has the 'Higher Risk' category which reads 0%.

    The four broad risk categories are:

    1. higher risk
    2. key client
    3. medium risk
    4. lower risk.

    The large fund population was rated using several timeliness and completeness indicators. Funds categorised as higher risk or key client have significant membership and contribution levels, while lower- and medium-risk categories capture smaller funds. Key-client and lower-risk funds are those ranked low in likelihood, as they have higher compliance with their reporting obligations.

    The combination of the likelihood and consequence indicators also enables each fund to be ranked, determining their overall position in the industry relative to other large funds. Categorisations and rankings are calculated using data reported to us relating to the relevant financial year.

    Tailored diagnostic reports

    To determine each fund’s consequence and likelihood ratings, data for the period ended 30 June 2016 was extracted as at 31 December 2016.

    In March 2017 we issued each fund its tailored diagnostic report. This year’s reports differ from those provided last year. Changes based on industry feedback include:

    • easier-to-read text and improved calculations and visual design
    • results and scores from 2014 and 2015 included for easy comparison with the 2016 results
    • more tailored information and, where possible, the formulas used for determining scores
    • an updated formula for calculating open and lost MCS accounts to include the number of distinct individuals reported as ‘open and lost’
    • an updated formula for calculating the 'completeness of MCS' indicator to include closed pension accounts
    • tailored reports provided to some of the largest external administrators.

    Administrator reports

    This year for the first time we provided a report to a small number of large fund administrators. The report provides an administrator-level view of their compliance with super reporting obligations for all the large funds they administer.

    The reports were based on the data lodged by the administrator for the period ended 30 June 2016. They show where the administrator sits within the super industry compared to their peers. They also show how each administrator fund is performing from a super reporting perspective.

    We encourage administrators to use this report to assess their performance and identify priorities for improvement, as well as to voluntarily disclose any material errors in past reporting.

    See also:

    What's next

    We have asked large super funds to:

    • review their tailored diagnostic report to determine how their fund is performing against the benchmarks
    • investigate and rectify any areas of concern
    • take action to improve their results where necessary
    • email us at LargeFundDiagnostic@ato.gov.au to discuss how we can support improved fund performance.

    Comparative industry results

    We assessed 246 of the 251 large super funds in the 2016 RDF (five were not rated due to insufficient lodgment data). Collectively these funds have 27 million members and $1.4 trillion under management.

    Each fund was rated using a common set of 13 indicators and plotted on the consequence/likelihood matrix.

    A comparison of the results over three years (2014–16) shows a steady positive shift of large funds moving out of the higher- and medium-risk categories into the lower-risk and key-client categories. The three figures below illustrate comparative scatter graphs for each year. Each dot represents a fund.

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    Figure 2: 2016 comparative industry results

    Figure 2: 2016 comparative industry results. The scatter graph represents each fund assessed in 2016's RDF. The X axis measurements span from 1.0 to 5.0 and represent the likelihood of non-compliance with reporting obligations. The Y axis measurements span from 1.0 to 5.0 and represent the consequences of potential non-compliance. The graph is broken in to 4 sections representing the risk each group of super funds presents. These sections are higher-risk funds, medium-risk , lower-risk and key client funds. Individual super funds are represented in their relevant quadrnts by a single dot.  The lower-risk quadrant is in the lower left corner of the graph and extends from 1.0 to 3.0 along the X axis and from 1.0 to 4.0 on the Y axis.  The key client quadrant in the top left corner extends from 1.0 to 3.0 along the X axis and from 4.0 to 5.0 along the Y axis.  The higher-risk quadrant in the top right corner extends from 3.0 to 5.0 along X axis and 4.0 to 5.0 along the Y axis.  The medium-risk quadrant in the bottom right extends from 3.0 to 5.0 along the X axis and from 1.0 to 4.0 along the Y axis.

    Figure 3: 2015 comparative industry results

    Figure 3: 2015 comparative industry results. The scatter graph represents each fund assessed in 2015's RDF. The X axis measurements span from 1.0 to 5.0 and represent the likelihood of non-compliance with reporting obligations. The Y axis measurements span from 1.0 to 5.0 and represent the consequences of potential non-compliance. The graph is broken in to 4 sections representing the risk each group of super funds presents. These sections are higher-risk funds, medium-risk , lower-risk and key client funds.  The lower-risk quadrant is in the lower left corner of the graph and extends from 1.0 to 3.0 along the X axis and from 1.0 to 4.0 on the Y axis. The section contains 229 dots each representing a single super fund.  The key client quadrant in the top left corner extends from 1.0 to 3.0 along the X axis and from 4.0 to 5.0 along the Y axis. The section contains 18 dots each representing a super fund.  The higher-risk quadrant in the top right corner extends from 3.0 to 5.0 along X axis and 4.0 to 5.0 along the Y axis. The section contains 0 dots each representing a super fund.  The medium-risk quadrant in the bottom right extends from 3.0 to 5.0 along the X axis and from 1.0 to 4.0 along the Y axis. The section contains 2 dots each representing a super fund.

    Figure 4: 2014 comparative industry results

    Figure 4: 2014 comparative industry results. The scatter graph represents each fund assessed in 2014's RDF. The X axis measurements span from 1.0 to 5.0 and represent the likelihood of non-compliance with reporting obligations. The Y axis measurements span from 1.0 to 5.0 and represent the consequences of potential non-compliance. The graph is broken in to 4 sections representing the risk each group of super funds presents. These sections are higher-risk funds, medium-risk , lower-risk and key client funds.  The lower-risk quadrant is in the lower left corner of the graph and extends from 1.0 to 3.0 along the X axis and from 1.0 to 4.0 on the Y axis. The section contains 76 dots each representing a single super fund.  The key client quadrant in the top left corner extends from 1.0 to 3.0 along the X axis and from 4.0 to 5.0 along the Y axis. The section contains 10 dots each representing a super fund.  The higher-risk quadrant in the top right corner extends from 3.0 to 5.0 along X axis and 4.0 to 5.0 along the Y axis. The section contains 5 dots each representing a super fund.  The medium-risk quadrant in the bottom right extends from 3.0 to 5.0 along the X axis and from 1.0 to 4.0 along the Y axis. The section contains 30 dots each representing a super fund.

    Key findings

    A comparison of the results reveals the following:

    • There has been a positive shift in fund performance over the three years of the RDF program.
    • The results of the 2016 RDF analysis are very positive, with 99% of funds rated as having a good standard of reporting.
    • An increasing number of funds are meeting their obligations.
    • Sixty-three percent of funds showed improvement in their results in 2016.
    • For the second consecutive year no funds were classified as higher risk.
    • Overall results have improved for 10 out of the 13 indicators, including the three 'key areas for improvement' from the 2015 RDF results – 'lost members over 65', 'duplicate TFNs' and 'timeliness of co-contributions remittance payment variations advices' (PVAs).
    • We saw excellent results in the indicator ‘personal contribution without TFN’, where 93% of funds have met the expected benchmark.
    • Funds meeting the benchmark for ‘lost accounts under $4,000’ improved from 51% to 80%. One of the main reasons for this shift is the re-reporting of lost members and proactive campaign work prior to the lost member report (LMR) refresh.
    • The number of lost accounts dropped from around 1.2 million in 2015 to around 580,000 in 2016. This altered the consequence rating of a small number of funds by moving them from the key client category into the lower risk category.
    • Funds are providing more accurate data. Consequently more of them are meeting the 'correct TFN quoted' benchmark.
    • PVA lodgment indicators have improved.
    Table 1: Percentage of funds that met the benchmark

     

    Indicator

    2016

    2015

    2014

    TFN1

    Personal contribution without TFN

    93.09

    90.87

    91.30

    TFN2

    Correct TFN quoted

    74.10

    69.73

    62.30

    TFN3

    Duplicate TFNs

    86.94

    85.43

    89.68

    TFN4

    Non Individual TFNs

    94.29

    94.09

    68.65

    MCS1

    Timeliness of MCS lodgment

    95.22

    95.79

    93.21

    MCS4a

    Completeness of MCS lodgment

    66.83

    57.53

    38.83

    MCS4b

    Open and lost MCS accounts

    42.23

    41.76

    26.96

    LMR1

    Lost members > 65 years old

    65.71

    63.77

    75.86

    LMR2

    Lost accounts under $4,000

    80.00

    51.21

    43.84

    LMR3

    Timeliness of LMS

    83.87

    88.89

    82.26

    USM4

    Timeliness of USM

    80.00

    87.04

    78.52

    PVA2

    Timeliness of co-contribution remittance PVAs

    77.96

    68.56

    69.70

    PVA4

    Timeliness of LISC remittance PVAs

    74.04

    70.27

    63.72

    Table 2: Indicators explained

    Indicator

    Explanation

    Why is this important?

    Benchmark

    Personal contribution without a TFN

    Percentage of MCSs with personal contribution reported by a fund

    This indicator will help funds identify potential breaches of the contributions standard. It also encourages a best practice approach in accepting and identifying compliant contributions.

    ≤0.05%

    Correct TFN quoted

    Number of quoted TFNs matched with the ATO-derived TFNs

     

    ≥97%

    Duplicate TFNs

    Number of instances where the same TFN is reported for different individuals

    Correct TFN data ensures payments are streamed to the correct accounts and gives confidence to funds in the accuracy of the data provided during contributions and rollovers. It also enables electronic transfers and payments. Additionally, correct TFN data enables members to track their super and consolidate multiple accounts online

    ≤0.1%

    Non-individual TFNs

    Number of instances where the reported TFN is for a non-individual

    Correct TFN data ensures payments are streamed to the correct accounts and gives confidence to funds in the accuracy of the data provided during contributions and rollovers. It also enables electronic transfers and payments. Additionally, correct TFN data enables members to track their super and consolidate multiple accounts online

    ≤0.03%

    Timeliness of MCS lodgment

    Number of original MCSs lodged within the prescribed timeframe

    Timely and complete lodgment of the MCS ensures the ATO is able to assess and pay government contributions in a timely manner. It also ensures member information is displayed in myGov as soon as possible.

    ≥95% within 7 days

    Completeness of MCS lodgment

    Compares the 'number of distinct individuals with open, open & lost and closed pension accounts’ reported on the MCS with the 'number of members’ reported on the fund's income tax return

    Timely and complete lodgment of the MCS ensures the ATO is able to assess and pay government contributions in a timely manner. It also ensures member information is displayed in myGov as soon as possible.

    90%

    'Open and Lost' MCS accounts

    Compares the 'number of lost un-contactable members’ from the Lost Member Register with the 'number of members with account status open and lost’ in MCS

    Accurate reporting of member status of open and lost accounts allows members to view and consolidate accounts in myGov

    90% – 110%

    Lost members >65 years

    Number of members aged over 65 on the LMR

    Accurate and current LMS & USM data is fundamental to the ATO’s important work of reuniting members with lost and/or unclaimed super. This information also ensures members are able to view accurate account information in myGov

    ≤1.91

    Lost accounts <$4,000

    Number of members with an account balance under $4000 on the LMR

    Accurate and current LMS & USM data is fundamental to the ATO’s important work of reuniting members with lost and/or unclaimed super. This information also ensures members are able to view accurate account information in MyGov.

    ≤1.91%

    Timeliness of LMS

    Timeliness of the first lodgment (either a lost member statement or non lodgment advice)

    Accurate and current LMS & USM data is fundamental to the ATO’s important work of reuniting members with lost and/or unclaimed super. This information also ensures members are able to view accurate account information in MyGov.

    within 7 days

    Timeliness of USM

    Number of original USMs lodged within the prescribed timeframe

    Accurate and current LMS & USM data is fundamental to the ATO’s important work of reuniting members with lost and/or unclaimed super. This information also ensures members are able to view accurate account information in MyGov.

    ≥95% within 7 days

    Timeliness of co-contribution remittance PVAs

    Timeframe during which a fund reaches a total of 98% of co-contributions remittance PVAs lodged

    Timely lodgment of remittance payment variation advices (PVA) enables us to redirect payments to eligible member accounts as soon as possible to maximise interest income on member benefits.

    ≥98% within 14 days

    Figure 5: Personal contributions recorded without a tax file number

    Figure 5: Personal contributions recorded without a tax file number. The image shows a bar chart which measures the Total MCS lodged with personal contributions and the number of instances personal contributions were reported with no TFN. The measurements for the Total MCS lodged with personal contributions spans between 1,400,000 and 1,750,000. The measurements for the Number of instance personal contributions were reported with no TFN spans between 0 and 1,600.  The first column is for the year 2014. The bar representing the Total MCS lodged with personal contributions reads approximately 1,720,000. The bar representing the number of instances personal contributions were reported with no TFN reads 1,054.  The second column is for the year 2015.The bar representing the Total MCS lodged with personal contributions reads approximately 1,660,000. The bar representing the number of instances personal contributions were reported with no TFN reads 1,349.  The third column is for the year 2016. The bar representing the Total MCS lodged with personal contributions reads approximately 1,520,000. The bar representing the number of instance personal contributions were reported with no TFN reads 449.

    Figure 6: Number of lost accounts with balances under $4,000

    Figure 6: Number of lost accounts with balances under $4,000. This graph shows the number of lost accounts under the $4000 threshold over the last three years. The measurement along the Y axis spans from 0 to 350,000 and represents the number of accounts underthe $4000 threshold.The measurement along the X axis has three columns for the years 2014, 2015 and 2016.  In the column for 2014 the graph displays the number of accounts under the threshold as 327,164.  In the column for 2015 the graph displays the number of accounts under the threshold as approxiamtely 224,443.  In the column for 2016 the graph displays the number of accounts under the threshold as approximately 17,036.

    Figure 7: Number of lost accounts

    Figure 7: Number of lost accounts. This graph shows the number of lost account across the last three years. The measurement for the number of lost accounts spans from 0 to 1,400,000 which runs along the Y axis. The measurement on the X axis spans the years 2014, 2015 and 2016.  In the column for 2014 the number of lost accounts reads 1,308,237.  In the column for 2015 the number of lost accounts reads 1,223,338.  In the column for 2016 the number of lost accounts reads 580,445.

    Figure 8: Number of lost members over 65 years of age

    Figure 8: Number of lost members over 65 years of age. This graph shows the number of lost members over the age of 65 from the years 2014, 2015 and 2016. The measurement along the Y axis represents the number of lost members over 65 and spans from 0 to 30,000. The measurement along the X axis has 3 columns, one for each of the years 2014, 2015 and 2016.  In the column for 2014 the number of lost members over 65 displayed is 24,112.  In the column for 2015 the number of lost members over 65 displayed is approximately 23,847.  In the column for 2016 the number of lost members over 65 displayed is approximately 10,987.

    Figure 9: Number of correctly quoted and matched tax file numbers compared with the number of Member Contribution Statements with a tax file number

    Figure 9: Number of correctly quoted and matched tax file numbers compared with the number of Member Contribution Statements with a tax file number. The image is a bar chart which has two bars for each column. The first bar represents the Number of correctly quoted and matched TFNs. The second bar represents the number of member contribution statements with a TFN. The Y axis measurement spans from 29,000,000 up to 31,000,000. The X axis has three columns which read 2014, 2015 and 2016.  The first column is for 2014. The first bar representing the number of correcetly quoted and matched TFNs reads 29,634,773. The second bar representing the member contribution statements with a TFN reads 29,767,654.  The second column is for 2015. The fkirst bar representing the number of correctly quoted and matched TFNs reads 30,211,615. The second bar representing the member contribution statements with a TFN reads 30,325,369.  The third column is for 2016. The first bar representing the number of correctly quoted and matched TFNs reads 30,715,789. The second bar representing the member contribution statements with a TFN reads 30,858,222.

    See also:

    Key areas for improvement

    The top three areas identified for reporting improvement:

    1. ‘Open and lost' MCS accounts: The percentage of funds meeting this indicator improved slightly (from 41.76% to 42.23%). This indicator is important because accurate reporting of member status as open, lost or closed allows members to view and actively consolidate accounts in myGov. We acknowledge timing differences can influence the results, and we would like to work with funds to improve this benchmark.
    2. Timeliness of USM: Funds meeting this benchmark dropped from 87% last year to 80% this year. We will educate funds on USM lodgment and payment deferrals to help improve USM lodgments timeliness.
    3. Completeness of MCS: Funds meeting this benchmark rose to 66.83% in 2016, compared to 57.53% last year; a reasonable improvement. Nevertheless, there is scope for further improvement and we will work with funds to ensure they understand our expectations for MCS lodgment.

    Key industry focus areas

    Our analysis of the results highlighted some key focus areas for maintaining good reporting performance.

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    TFN as a primary locator

    Using a TFN as the primary locator is essential to connecting and reuniting members with their super. We encourage funds to be proactive regarding their lost members, and to participate in our provision of updated addresses program.

    Reuniting members with their lost super is one of our highest priorities because of the huge amounts involved. It is also a key reason we ask you to ensure the TFNs attached to your members' accounts are valid.

    • We received more than 33.5 million MCS forms for 2016, and successfully matched 98.15% of these with an individual’s TFN held in our system.
    • The number of MCS forms with a correct TFN quoted increased by 1.17% to 94.42% in 2016. The increase from 2013 was 9.78%.
    • We continue to regularly send provision of TFN notifications to funds as we receive MCS lodgments and amendments, including s299TA, s299TC and ‘please resolve’ (PR) notifications.

    TFN member identity check

    • SuperTICK3 provides real-time reporting of opened and closed accounts.
    • SuperTICK retains its primary function of validating TFNs for new and exiting members. It improves TFN data quality by providing ‘matched’ and ‘corrected’ responses when funds submit member information. Our system can match this to an individual’s TFN. This validation allows funds to identify if a member TFN is correct at the time of their choosing.
    • A recent trend of not including a TFN with new member validations has significantly increased the rate of unmatched responses, reducing the effectiveness of this service. Unmatched and corrected responses have risen by over 1000% and 500% respectively with the growth due to TFNs not being used with new member validations.
    • The number of TFNs validated in SuperTICK doubled during 2016; however, funds reported only 82% of these validated TFNs on their MCSs in 2016 compared to 96% in 2015.
    • Our data analysis does not indicate the cause of this trend, but we suspect the growth in online options for new memberships with an optional TFN field may be a significant contributor. We remind funds of the importance of providing a TFN when registering new member accounts.
    • SuperTICK is continuously monitored. We may contact funds to discuss unusual usage or where we can help improve TFN quality.

    Reuniting super

    Find out about:

    High-value lost accounts

    We regularly review the top 200 lost account balances appearing on the LMR. These accounts belong to members who are uncontactable or inactive. Funds can take action to remove these accounts from the LMR. This will also remove the 'lost' status from the account for both MyGov and SuperMatch display purposes.

    If funds participate in our LMR ‘provision of updated address’ program, they can use their members' updated address and contact details to remove them from the register. They can do this if the following conditions apply:

    • If the updated address is different from the existing address on record and the member is lost uncontactable, the member’s status can be updated from ‘lost’ to ‘found’ without further contact.
    • If the updated address is different to the existing address on record and the member is lost inactive, the member’s status can be updated from ‘lost’ to ‘found’ once contact has been made and the member confirms the new address.
    • If funds have current contact details for the high-value lost accounts, they can contact the members to confirm they wish to remain a member of the fund indefinitely, in which case the member is permanently excluded from being lost and does not need to be reported to the ATO as a lost member.

    Last year's initiative resulted in 55 lost members with the highest account balances being found and removed from the LMR, with an aggregate value of $48.8 million.

    Lost accounts with reported balances under $6,000

    The small lost-account threshold increased to $6,000 on 31 December 2016, and should not be included in the lost member statement. Small lost accounts should have been reported and paid as USM by the due date of 30 April 2017.

    Many funds appearing on the LMR have a considerable number of accounts with reported balances under the threshold of $6,000. These should be transferred to us as USM. Alternatively, if they have already been transferred as USM, they should be reported by the fund on the next LMS as 'transferred'.

    Reporting unclaimed super

    We encourage funds to check the eligibility criteria for USM. Generally, funds should report and pay to us any 'lost' accounts where the account holder is either:      

    • over 65 years old
    • deceased
    • reporting a balance less than $6,000.


    See also:

      Last modified: 30 Jun 2017QC 46621