A Guide for Credit Providers Moving to Participate in CCR by David Grafton

A Guide for Credit Providers
moving to participate in
Comprehensive Credit Reporting
DAVID GRAFTON

CONTENTS
Context & Purpose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
Strategy, Structure and Business Plan . . . . . . . . 3
Undisclosed Liabilities, Servicing
Capacity and Approval Rates. . . . . . . . . . . . . . . . . . . . 6
Operational Considerations
– Data Supply . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
Operational Conderations
– Data Consumption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
Data Integration and Use Cases . . . . . . . . . . . . . . . . . . . 18
Transition Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
Acknowledgements / About the Author . . . . . . . 26
© David Grafton, David Grafton Consulting | 2018

2
This paper provides a practical guide for those credit providers (“CPs”) who wish
to supply and consume Comprehensive Credit Reporting (“CCR”) information.
It is an update and companion guide to an earlier document entitled “Issues for
Credit Providers to consider when moving to Comprehensive Credit Reporting”
(Grafton, 2017). While the 2017 paper can be seen as the “why” of CCR, this
paper is more focused on setting out the “how”.
In November 2017 the Federal Treasurer mandated the implementation of CCR by the “big four” banks (Morrison, 2017)
and imposed a deadline for data supply of at least 50% of all accounts by September 2018, with the remaining accounts to
be supplied twelve months later. Significant movement by the major banks and other CPs is now occurring. Commentary
from Equifax suggests that although only 12% of all accounts were live on the bureau in July 2018, this proportion will
increase to 75% by the end of the calendar year and to over 90% by September 2019 (Yeates, 2018).
For those CPs that are not mandated to supply, there are strong reasons why they should consider doing so. In summary
the main advantages are:
CONTEXT & PURPOSE1
This paper sets out the most important steps that need to be taken by CPs moving to implement CCR and provides
a structured path that details what needs to be done by when, and by which parts of the organisation.
The detailed roadmap set out in the associated spreadsheet to this paper is available at davidgrafton.com.au (Grafton,
2018). It provides a template for a typical CCR roadmap ordered by time (month) and by actions required for each of a
CP’s business units, the executive leadership team, and the board. It can be used as a framework, a checklist or as a
template for a CCR Program of work which can be tailored to the requirements of an individual CP.
A Guide for Credit Providers moving to participate in Comprehensive Credit Reporting | David Grafton
FIG 1 PRINCIPAL REASONS FOR PARTICIPATING IN CCR
RICHER AND MORE POWERFUL DATA. CPs will be
able to see far more information on the bureau(s) than is
currently available. Data from the mandated banks will
cover 75–80% of all credit accounts which should
enable all lenders to make better credit decisions.
COMPLIANCE AND REGULATORY RISK. CCR
participation is seen by regulators as necessary to meet
responsible lending obligations. ASIC’s regulatory advice
note RG 209 (currently under review) clearly anticipates
that new data sources, when available, should be used for
this purpose: “Other tools may become available to you in
the future, which may further assist you in complying with
the responsible lending obligations (e.g. comprehensive
credit reports or a database of small amount credit
contracts). As new verification tools become available to
licensees, what constitutes “reasonable steps to verify”
information may change.” (ASIC, 2014).
CUSTOMER EXPERIENCE. CCR enables customers
to more quickly establish a positive credit profile (especially
“underserved” segments such as younger consumers
and recently arrived migrants) and to recover from
previous defaults that would otherwise damage their
credit score for up to six years.
CUSTOMER BENEFITS FROM WIDER COMPETITION.
Customers that have demonstrated a sound track record
in managing their credit commitments are likely to see
this reflected in their credit score and in turn could
benefit from lower costs of credit as risk-based pricing
develops. CCR stimulates new product innovation, as
seen in other markets overseas. Fintechs in particular
will benefit from access to more complete CCR
information.
AVOIDING ADVERSE SELECTION. Credit providers
that take part in CCR will be able to make better credit
decisions, leaving those not in the system subject to
potentially approving applicants who are in fact poorer
risks and, by being able to see only “negative” data,
denying credit to those whose CCR information would
suggest are creditworthy.
FINANCIAL BENEFITS. By being able to make better
credit decisions a CP can expect to see “swap set”
benefits in either a higher approval rate for the same level
of bad debt or reduced bad debt expense for the same
level of approval, or a blend of the two. The decision as
to where to set this trade-off will be informed by the CP’s
Risk Appetite, to which the broader CCR initiative should
align. “Swap set” benefits are discussed in more detail
in Section 2.2 (and in Grafton, 2017).

2.1 STRATEGY AND STRUCTURE
The first and foundational step is to ensure that CCR
aligns with one or more of the strategic objectives of the
organisation. This requires agreement across the Senior
Executive Team (“Exco”) and endorsement by the Board.
Defining what the business is seeking to achieve through
CCR (see list of advantages on previous page for examples)
is important, as is the linkage between CCR and other
initiatives that will likely be emerging from Government or
regulators in the foreseeable future – in particular Open
Banking and serviceability requirements. Given that there
are many similarities between what Open Banking will
require and what is needed for the implementation of
CCR, it would be advisable to look holistically at how to
cater for both (see Australian Government Treasury, 2018).
Careful consideration should be given as to how the data
will be used and whether CCR provides a catalyst for
systems change, for example to increase auto decisioning
by implementing decisioning software rather than relying
on manual underwriting.
A “Decisioning Roadmap” setting out current v desired
medium/longer term future state will be a useful aid to
strategic thinking and a guide for future investment
requirements. This roadmap should be broader than just
meeting the requirements of CCR and should consider
issues such as risk-based pricing, the sharing of income
and expense data, the automation of ID verification
and fraud prevention, the use machine learning-based
analytics and, for CPs with a secured loan book, the use
of automated valuations. CCR should be seen as a subset
of this broader strategic program of work and no “point-
based” decisions in the CCR project should be taken
that could hinder the achievement of the objectives of
the wider Program.
An important strategic decision for a CP is whether, and
if so, how to implement risk-based pricing. CCR enables
lenders to have more confidence about the risk of each
applicant and experience overseas shows that this
encourages product innovation, especially in pricing for
risk. While one of the benefits of CCR is that customers
with a good repayment record can expect preferential
service and rates, a CP will also have to consider how to
manage consumer communications and possible repu-
tational risks that arise from increasing the cost of credit
for those that have not repaid to terms – especially in
the case of those in hardship and/or on low incomes.
Creating a risk-based pricing strategy will involve
making decisions about which products to prioritise
(generally these will be unsecured rather than secured
loans) and whether to focus on new or existing customers.
Additionally, a test and learn approach, perhaps using a
focus group, will be useful in gaining an understanding
of the shape of the “indifference curve” i.e. the trade-off
elasticity between price and demand.
Having established objectives, it will then be necessary to
develop a CCR business plan and associated operational
processes to maximise execution effectiveness. This stage
will involve most, if not all parts of the organisation and is
most likely to succeed if CCR is seen as a program of work
in its own right, distinct from business as usual (“BAU”). It is
likely that new staff will need to be recruited and existing
staff seconded part or full-time into CCR roles. Consider-
ation needs to be given at an early stage to the future
skills that will be needed to meet the CP’s objectives.
These may be roles new to the CP, for example in data
science and data management, as well as in the more
traditional risk and product policy/strategy areas.
To operationalise to best effect, a Program Steering
Group (“PSG”) should be established. This Group should
comprise representatives from all impacted areas of the
business and will include Product, Risk, Finance, Legal,
Compliance, HR, Operations (including from front line
staff and Collections), Marketing, and representatives
from IT/Data Management. There should be a Program
Manager in place to manage and report on progress
across the various workstreams in the Program, reporting
to the Business Sponsor.
A Business Sponsor chairperson needs to be appointed,
preferably with a direct reporting line to the CEO. The chair-
person will take overall responsibility and accountability
for the Program and may be a senior Risk Management
executive or a Product Executive with Line 1 Risk functions.
The PSG should have a defined written Charter with clear
objectives, accountabilities, risk appetite, and a reporting
framework that includes standing items, delegations,
escalation protocols and a risk register. It will contain
both “Line 1” and “Line 2” risk management functions.
This Group should establish a sound operating rhythm
and meet at least once a month, with a progress and
issues report tabled for Exco with a summary paper
produced for the Board.
3
2 STRATEGY, STRUCTURE
AND BUSINESS PLAN

4
2.2 BUSINESS PLAN
Many organisations adopt a three-stage approach to business case development. The first is a “concept” proposal
that is linked to the organisation’s strategic objectives and explains why the initiative is needed. Assuming there is
strategic fit and executive support, the next stage is to develop an options paper that typically would look at 3–5
possible scenarios, including an option to do nothing. The pros and cons of each option are then evaluated, and a
recommendation made to Exco and the board.
The business plan is best designed as a small number of alternative cost-benefit options from which a decision can
be made as to the preferred path for the organisation to take.
Should the recommended option be approved by Exco, then a fully costed business case with associated “soft”
and “hard” benefits will need to be produced, with input from all impacted business units across the CP. Key to the
financial and customer benefits arising from CCR is the value-add generated by two “swap sets”, as illustrated in
Figure 2.
A Applications currently rejected that can
be approved using CCR data
B Applications rejected by both data sets
C Applications accepted by both data sets
D Applications currently accepted but
which CCR data shows to be too high risk
Source: Grafton, 2017
BUSINESS GROWTH
NO CHANGE
NO CHANGE
BAD DEBT REDUCTION
A
C
B
D
Key
accept
DECISION MADE WITH CCR DATA SET
DECISIONMADEWITHNEGATIVEDATASET
accept
reject
reject
The first swap set – cell “A” – relates to those accounts that a CP has declined or referred in the
past, but which would now be approved given visibility of CCR information. Examples here include
applicants with a good repayment history over the past 24 months counter-balancing an older
default, and younger applicants with a “thin” credit file who can quickly establish a strong credit
profile more quickly using CCR.
The second swap set to consider is cell “D” and refers to applications that the CP has approved in
the past but, with visibility of CCR data, would not now approve due to higher risk being apparent.
The example shown in Figure 3 illustrates how CCR information can improve the quality of lending
decisions, in this case at point of origination where repayment history and credit commitment
information changes substantially a CP’s view of an applicant’s risk.
FIG 2 SWAP SETS

5A Guide for Credit Providers moving to participate in Comprehensive Credit Reporting | David Grafton
M1
M2
M3
M4
M5
M6
M7
M8
M9
M10
M11
M12
M13
M14
M15
M16
M17
M18
M19
M20
M21
M22
M23
M24
$ 200,000 Bank Loan
$ 40,000 Credit Card
$ 20,000 Store Card
$ 20,000 Personal Loan
$ 280,000
COMPREHENSIVE CREDIT REPORTING
M1
M2
M3
M4
M5
M6
M7
M8
M9
M10
M11
M12
M13
M14
M15
M16
M17
M18
M19
M20
M21
M22
M23
M24
$ 200,000 Bank Loan
$ 40,000 Credit Card
$ 20,000 Store Card
$ 20,000 Personal Loan
$ 280,000
Bureau Report
Defaults Enquiries
nil 6
Bureau Report
Defaults Enquiries
nil 6
Application Form
Loans: 240,000
Application Form
Loans: 240,000
COMPREHENSIVE CREDIT REPORTING NEGATIVE REPORTING
NEGATIVE REPORTING
LENDER A participates in negative-only reporting and based on negative information would likely approve a loan to a
client that has no defaults and an apparent capacity to repay.
LENDER B has access to comprehensive credit reporting and can see that the client has a poor repayment record
on their accounts over the past 24 months (M1–M24 in the diagrams). They can also see that the customer’s total
exposure is greater than advised on their application. Lender B is likely to judge the application as too risky to accept.
Source: Johnson, 2013
Although CCR is commonly seen as a “Risk” project, it is
a mistake to run CCR as purely a “Risk” program of work.
Line 1 Risk or Product can take the leading role which
potentially has the added benefit of embedding ownership
in day-to-day business practice. It is important that, from
day one, a wide range of stakeholders is involved, since
the impact of CCR will be felt by most, if not all, of the
CP’s business units. Stakeholder “buy-in” at this stage
is crucial and will result in a more fully engaged PSG
with arguably better (and less politicised) outcomes as
a result.
The CCR business case should consider other emerging
data sharing requirements, for example the likely need
for banks to be able to share income, expenditure and
transactional data under an “Open Banking” regime. The
business case should also reference the “Decisioning
Roadmap” and the CP’s Risk Appetite Statement to
ensure consistency with strategic intent.
While it is likely that financial benefits will eventually accrue
over a three year or longer time period, these are unlikely
to be substantial and may well be negative in the near
term. Accordingly, a CP may decide to position CCR as
more of a compliance and customer experience business
case with “softer” (but still measurable) benefits rather
than “hard” dollars.
The negative impact of non-participation needs to
be understood. The three main areas here are:
• The likelihood of adverse selection at loan
origination, especially for unsecured products
• The likelihood of lower collections recovery rates
and sub-optimal allocation of collections resources
• The possibility of regulators questioning why the
lender is not using CCR to identify or verify a
consumer’s debts in the interests of responsible
lending
FIG 3 IMPACT OF CCR ON CREDIT DECISION-MAKING:
EXAMPLE AT LOAN ORIGINATION

6
THE BUSINESS PLAN IS BEST
DESIGNED AS A SMALL NUMBER
OF ALTERNATIVE COST-BENEFIT
OPTIONS FROM WHICH A DECISION
CAN BE MADE AS TO THE PREFERRED
PATH FOR THE ORGANISATION TO TAKE
A case for CCR can be made based on avoidance of
the negative impacts of doing nothing, as opposed to
the more usual form of a business case justification,
which is built on estimating the positive outcomes that
derive from implementing change.
The business case will need to be approved by Exco
and discussed/endorsed at Board level.
APRA and ASIC will need to be informed of the CP’s
decision to move to CCR.
Consideration should be given to engaging with the
Australian Retail Credit Association – “ARCA” – which has
been the main industry body promoting CCR and provides
CPs with access to useful sources of information on CCR
and broader consumer credit issues. Valuable information
is also available through industry associations such as the
ABA, AFIA (Australian Finance Industry Association) and
COBA (Community Owned Banking Association. ARCA
has developed an award-winning website “CreditSmart”
that educates consumers about what CCR is and what
the impacts will be for customers (ARCA, 2018), which
all stakeholders should be encouraged to read.
While from a credit risk perspective more CCR data
will lead to better credit decisions and potentially higher
approval rates, it is very important to remember that
the application process has to pass another threshold,
which is the assessment of serviceability. Without CCR,
CPs are dependent on the veracity of the applicant to
disclose all other credit liabilities and it is clear from
research findings that not all applicants for credit do in
fact disclose all of their liabilities. For example Equifax
estimated that 20–25% of credit commitments were
not being disclosed (Equifax, 2015).
Given that open accounts and their limits (not balance)
will now become known and that servicing needs to be
calculated relative to the full available limit (in the
absence of information on balance/utilisation), this could
have a significant impact on approval rates. In addition,
recent regulator actions focusing on verifying actual
income and expenses rather than relying on a benchmark
such as HEM (Australian Bureau of Statistics, 2017) will
also reduce approval rates. Further, ASIC’s proposed
requirement to amortise credit card debt limits over a
three-year period would significantly increase the monthly
minimum required payment and again serve to depress
approval rates (ASIC, 2018).
CPs need to anticipate these impacts and be prepared to
handle questions from consumers and from stakeholders
within the CP.
3 UNDISCLOSED LIABILITIES,
SERVICING CAPACITY AND
APPROVAL RATES

There are three main stages to consider in operationalising CCR: CCR data supply,
CCR data consumption, and CCR data integration and use within credit decisioning
and operational processes. This section sets out the steps that need to be taken
in respect of data supply.
4.1 THE PRINCIPLES OF RECIPROCITY AND DATA
EXCHANGE (“PRDE”) AND THE CCR CONTROL
ENVIRONMENT
CPs will need to be familiar with laws and regulations that govern consumer credit provision and credit reporting.
Part IIIA of the Privacy Act 1988 (Privacy Act) regulates consumer credit reporting in Australia. Part IIIA is supported
by the Privacy Regulation 2013 and the Privacy (Credit Reporting) Code 2014 (CR code), Other documents include the
National Consumer Credit Protection Act (2009), ASIC’s RG209 (2014), and a range of Australian Prudential Standards.
CPs will also need to understand the control environment and industry rules that govern the CCR data sharing regime.
These have been developed by industry over many years through the industry Association “ARCA”.
The central component of this control environment is the PRDE which sets out the terms under which CPs can take part
in CCR. The PRDE also references the Administrator Entity (an ARCA subsidiary that oversees the proper working of
the data sharing scheme) and the Industry Determination Group (“IDG”) and Eminent Person whose roles are to assess
and adjudicate on any disputes or actions by a CP that are not consistent with the obligations of the PRDE. The main
principles of the PRDE are:
4 OPERATIONAL CONSIDERATIONS
– DATA SUPPLY
1 The Deed Poll binds CPs
and CRBs to the data
exchange rules in the PRDE.
2 It is necessary to be a
PRDE signatory in order to
exchange Consumer Credit
Liability Information (CCLI)
and Repayment History
Information (RHI) with other
PRDE signatories.
3 Data has to be provided
consistent with the Australian
Credit Reporting Data
Standards and under the
reciprocity conditions set
out in the PRDE.
4 PRDE signatories agree to
adopt transition rules which
will support early adoption of
partial and comprehensive
information exchange.
5 PRDE signatories will be
subject to monitoring, reporting
and compliance requirements,
for the purpose of encouraging
participation in the exchange
of credit information and data
integrity.
6 The PRDE will be reviewed
after three years.
Source: ARCA, 2018
FIG 3 THE PRINCIPLES OF RECIPROCITY AND DATA EXCHANGE

8 A Guide for Credit Providers moving to participate in Comprehensive Credit Reporting | David Grafton
Within the CCR environment, a CP can supply and consume data at a “partial” level (negative data plus CCLI –
consumer credit liability information – which includes account open status, type of credit, limit and account closed “good”
data) or “full” CCR level which includes negative, partial and “RHI” which is 24 months of repayment history information
(see Figure 4). On commencement, a CP can start with 3 months of RHI and only needs to supply CCR information for
50% of all credit accounts to the bureau(s), with the remainder to be supplied within 12 months of initial supply.
A CP cannot restrict supply to selected credit products after the initial 12-month period – all available credit accounts
that can be contributed need to be contributed. This ensures that the fullest possible data set is made available for
those participating in CCR and prevents “gaming” by those who may believe that certain portfolios confer competitive
advantage to the “owning” CP.
FIG 4 CCR CONTRIBUTION TIERS
FULL
(Must be a
licensed credit
provider)
DEFAULTS
AND
ENQUIRIES
NEGATIVE
ONLY
DEFAULTS
AND
ENQUIRIES
ALL CPs
INCLUDING
NON-PRDE
SIGNATORIES
CREDIT BUREAU DATA ACCESSIBLE BY:
ONLY
SIGNATORY CPs
SUPPLYING
PARTIAL OR
FULL CCR
ONLY
SIGNATORY CPs
SUPPLYING
FULL CCR
PARTIAL
DEFAULTS
AND
ENQUIRIES
• Date opened
• Credit llimit
• Type of credit
• Date closed
• Date opened
• Credit llimit
• Type of credit
• Date closed
24 MONTHS
REPAYMENT
HISTORY
A CP can only access CCR information up to and including the level that it supplies to the credit bureau(s). If a CP
only supplies negative data, it cannot “see” partial or full information from other CPs, and if it only supplies at a
partial tier then it can see other CPs’ negative and partial data but not the RHI component. Note that only licensed
credit providers are permitted to share full CCR information – telcos and utilities for example are restricted to the
negative and partial tiers.
The decision as to which tier to adopt will depend on the benefits from higher tier data exchange relative to the
additional cost and complexity involved. There is abundant evidence that RHI is the single most powerful predictor
of credit behaviour (see for example Figure 6 – FICO, 2018) and, wherever possible, full data exchange should be
implemented. However, especially for some less advanced CPs, the data on repayment history may be too difficult
or costly to retrieve from legacy systems. In this case, consideration of partial supply should be made.

Having decided which tier to adopt, the CP will need to engage with ARCA to sign a deed poll that binds the CP
to the same industry rules (the PRDE) as all other CCR participants.
Collections scripting will need to be reviewed to ensure that customers clearly understand whether their current
contract is to be varied (in which case RHI is reported relative to the terms of the new contract) or whether an
“indulgence” is being granted under the terms of the existing contract (in which case arrears continue to be reported
relative to the terms of that contract). This will require careful scripting for collections and hardship teams to ensure
that a customer understands their contractual obligations, and the effect on their credit report.
Some (currently most) CPs will want to supply data from all of their portfolios to meet or exceed the 50% threshold,
while others may prefer to supply information on selected products only. The greatest “swap set” benefits arise from
non-mortgage rather home loan lending portfolios because the risk associated with unsecured lending is higher and
so the benefits from making better risk decisions are greater – see Figure 5.
FIG 5 DIFFERENTIAL BENEFITS FROM CCR
BY PRODUCT TYPE AND CREDIT LIFECYCLE STAGE
INDICATIVE $ BENEFIT AND PRIORITISATION
MATRIX FROM USING CCR
Originations Behavioural Collections
Major Minor
CREDIT LIFECYCLE STAGE
Overdraft
PRODUCT
Personal
Loan
Credit
Card
Home
Loan
Key
A decision needs to be made as to how
the 50% threshold for all accounts is to
be reached, and operational processes
implemented to give this effect. The linkage
with other initiatives such as Open Banking
must also be considered.
The timing of participation for data supply
will need to be decided. Considerations
here include the overall CCR data volumes
in the credit reporting system; the richness
of data in use by comparable institutions;
likely CCR match rates; strategic concerns
over supplying more data (and more value)
to competitors than receiving and, of crucial
importance, the extent to which the CP is
willing to accept regulatory and reputational
risk by not participating. Judging the point of
participation will be informed by the results
from portfolio performance monitoring and
feedback from the bureaus.
Source: Grafton, 2017

A Guide for Credit Providers moving to participate in Comprehensive Credit Reporting | David Grafton10
4.2 SINGLE OR MULTI-BUREAU SUPPLY?
A further consideration in CCR data supply is whether
a CP wishes to contribute to just one or more than one
credit bureau. If more than one bureau is selected, then
the PRDE states the CP must provide the CCR data at
the same tier to all each of bureaus with which it has a
services agreement. Note there is no obligation under
the PRDE for a CP to access CCR from all bureaus it
has a services agreement with.
The main issue to consider with multi-bureau usage is
the benefit to be gained from a more complete picture
of the consumer (for reputational as well as decisioning
reasons) against the increased operational complexity
and cost in using more than one bureau.
The three main bureaus in Australia do not have the
same information – there is considerable data asymmetry
between them. They also vary in their technologies and
price points. While over time it is likely that data equali-
sation will occur, this is certainly not the case today nor
will it eventuate in the near future. A review of the strengths
and weaknesses of the bureaus should be undertaken,
especially if multi-bureau usage is being contemplated.
An evaluation of the strengths of each bureau can be
undertaken through a historical “retro” analysis but note
that in a dynamic environment where data volumes are
building, the results of retro analyses can quickly become
out of date and even misleading. Use can also be made
of the offline CCR test environments provided by Experian
(“TLab”) and Equifax (“Preview”), but again note the
caveat above. These test environments pool CCR
information from a wide range of CPs and allow a CP to
understand, before actually going live on the bureau(s),
what the likely impacts will be on approval rates, match
rates and in estimated arrears and bad debts.
Care needs to be taken when comparing match rates
between bureaus because the important metric is not
the match rate per se (“fuzzy matching” can provide
high match rates of low reliability) but the accuracy of
the match, which is fundamental to making a correct
credit assessment about an individual applicant.
THE MAIN ISSUE TO CONSIDER
WITH MULTI-BUREAU USAGE IS
THE BENEFIT TO BE GAINED
FROM A MORE COMPLETE
PICTURE OF THE CONSUMER

A Guide for Credit Providers moving to participate in Comprehensive Credit Reporting | David Grafton 11
4.3 DATA STANDARDS
It is a requirement of the PRDE that data supply from all
CPs conforms to the standards set out in the Australian
Credit Reporting Data Standards (“ACRDS”). This standard
is owned and managed by the PRDE Administrator and
is intended to ensure that all CCR data is consistently
defined across all CPs and bureaus and that the data
meets agreed quality measures. It also supports multi-
bureau reporting, on the basis that a CP should be able
to send the same file to multiple bureaus, and each
bureau should accept the same file provided it complies
with the ACRDS. This means that whichever bureau is
used for credit reporting, all information will have the
same meaning. The ACRDS is available on request from
ARCA (ARCA, 2018).
The ACRDS is a complex set of standards, definitions
and process steps which may be challenging for an
individual CP to implement using in-house resources.
There are third parties who can cost effectively assist
with this task including Equifax, Experian, illion and Zeal
Solutions. Illion offers a bureau-based service in this
regard while Equifax, Experian and Zeal provide software
for a CP to use in-house (or cloud based) to convert
data into the required ACRDS format.
A CP should consider developing a control framework
to ensure compliance with all aspects of the PRDE
and the ACRDS. CPs that have signed the PRDE are
encouraged to take part in a Data Standards Working
Group, which is open to all signatories, regardless of
whether or not they are also members of ARCA. In
addition, understanding the role of the new external
dispute resolution body (the Australian Financial
Complaint Authority – “AFCA”) and a CP’s obligations to
it, especially in dealing with complaints and corrections,
will be important.
It is likely that the operational process of extracting CCR
data, converting to the required standards, and supplying
error-free to a bureau(s) will take several months and
numerous iterations until successful. It is also highly
probable that, if using more than one bureau, there may
be inconsistencies between them as to what data is
accepted and what is not. Resolving these issues can
be time consuming.
Most CPs will want to contribute their data in test mode
for Quality Assurance and business impact assessment
prior to go-live and the bureaus provide a “Private” setting
that enables a CP to load data as it would if “live”, for
operational testing purposes.
A CP should ensure that, once the initial data load has
been properly made and confirmed as received by the
bureau(s), there are industrial-strength BAU processes
in place, as automated as possible, to ensure that the
successful process can be readily repeated each month.
An approach to data supply that represents low
execution risk is one where, initially, only one bureau is
selected for data supply and data provided from only
one or two portfolios (to meet the 50% threshold). Once
data supply has been successful to one bureau, it is
relatively easy to copy and send the “clean” file to other
CRBs. The lessons learned from supplying one or two
portfolios will be useful when extending data supply to
the remaining account types.
A CP SHOULD CONSIDER
DEVELOPING A CONTROL
FRAMEWORK TO ENSURE
COMPLIANCE WITH ALL
ASPECTS OF THE PRDE
AND THE ACRDS

4.4 DATA TRANSFER
Existing protocols can be followed in order to supply
CCR information, but the currently defined fields will
need to be considerably extended to cater for the
additional data. CPs should consider whether existing
data transfer methods (especially if highly manual) are
to be retained or whether the need to make changes to
accommodate additional information also provides an
opportunity to review current decisioning practice and
implement “decision engine” technologies (from vendors
such as Experian, Equifax, illion, Fair Isaac, Zeal and
others) to increase automation.
The extent to which this will appeal is dependent on the
content of and support for the “Decisioning Roadmap”
referred to in Section 2 above.
Data security is of paramount importance and a review
of current and intended future practice should be under-
taken, either by internal audit/compliance working with
IT or by an independent third party.
Frequency of batch update to the bureau(s) needs to be
considered and while monthly supply is the norm, there
are advantages in more frequent supply that will improve
the accuracy of the credit reporting system.
4.5 FURTHER CONSIDERATIONS
If a CP wishes to supply CCR information to a bureau
then it will be essential to ensure that the appropriate
consumer disclosures are in place. Some CPs have
application forms that name a specific bureau which
is preferable to having a generic statement for data to
be provided to “a Credit Bureau”. This review should
be undertaken by Legal/Compliance and amended
application forms, with appropriate wording, produced
before commencing CCR data supply.
Note that under the PRDE, and unlike the situation in
other countries (e.g. NZ, UK, USA), for customer manage-
ment purposes, a CP cannot send all of its customer
accounts to the bureau(s) to be “washed” i.e. scored
or enhanced with bureau data. Information can only be
supplied from the bureau(s) on those customers deemed
to be at “significant risk of defaulting”, including those
already in arrears. This was designed to prevent the use
of CCR data for target marketing purposes by enabling
a CP to use other contributors’ information to identify
their best existing customers in terms of risk and to offer
them new products and preferential prices accordingly.
Informing CP staff of the rationale for and the implications
of moving to CCR will require training/education sessions
so that all staff, especially those in customer facing roles,
are fully informed and capable of answering customer
questions. ARCA can provide support for such sessions.
Customer communications need to be considered
and at minimum there should be a section of the CP’s
website dedicated to explaining CCR and the benefits
that it delivers. The marketing team should recommend
the form that customer communications should take in
relation to CCR and obtain sign-off from Risk, Legal/
compliance, Product and customer facing staff. Links
to other useful sites for consumer education in relation
to CCR may be provided e.g. ASIC’s Moneysmart,
ARCA’s CreditSmart, and finder.com.au.
THERE SHOULD BE A SECTION
OF A CP’s WEBSITE DEDICATED
TO EXPLAINING CCR AND THE
BENEFITS THAT IT DELIVERS

DATA SECURITY IS OF
PARAMOUNT IMPORTANCE
AND A REVIEW OF CURRENT
AND INTENDED FUTURE
PRACTICE SHOULD BE
UNDERTAKEN

The first decision to be made is to select the tier at which CCR information is to
be received, bearing in mind the rules of reciprocity which require that CPs can
only receive data at or below the tier at which they supply it (see Figure 4). For
the purposes of this paper it will be assumed that “full” CCR will be consumed.
Then follow the operational and IT steps to enable CCR
to be received by the CP. These include:
• the choice of “channel” i.e. will data be consumed as
a PDF and used in a manual decisioning environment
or will data need to flow into an automated decisioning
system and/or a decisioning datamart/warehouse?
• the connection to one or multi-bureau data providers
• deciding on and building the capability to consume
and store the type and volume of data to be used
i.e. the additional fields, the bureau score(s) and any
additional raw data or “data blocks”
• choosing which portfolios and whether originations or
customer management/collections will be prioritised,
and design and build capability for on-line and batch
receipt
Manual credit decisioning may in some CPs be
implemented for all applicants or just for “refers”.
The additional CCR information that a CP will be able
to see on a consumer’s credit report, compared with a
negative only report, means that the CP’s policies to
approve, refer and decline applications will all need
review. They will also need constant monitoring as the
volumes build in the CCR bureau environments. Policies
will need to be developed and implemented that make
best use of this additional information, both for CPs that
use manual decisioning and those that have automated
systems in place. Note that it is likely that in the early days
of CCR there will be increased use of manual decisioning
and higher volumes of customer queries for front line
staff to address.
5 OPERATIONAL CONSIDERATIONS
– DATA CONSUMPTION
5.1 MANUAL DECISIONING
In a manual decisioning environment, there will need
to be systems changes made to accommodate the
additional CCR data fields returned from the bureau(s)
in ACRDS format, and to convert the raw data to make
the consumer’s credit report available to underwriters
in an easily understood form. In addition, collections
functions will benefit from a broader view of customers
in arrears and consideration needs to be given to the
use of CCR in managing delinquency.
Secure Data links (e.g. secure file transfer protocol
– “SFTP”) will need to be created to access additional
bureaus if the CP intends multi-bureau use and security
protocols should be strictly maintained. At application
stage, these changes need to permit the fast transmission
of data online in real time, whereas for behavioural
customer management and collections a batch process
is going to be required.
The online originations file and the batch file from
the bureau(s) need to be designed to accommodate
additional CCR fields and may also include additional
data elements or data blocks pre-calculated by the
bureaus to enhance decision making. An example
would include “every 2+ in arrears in the last 6 months”.
Such additional data for manual underwriting may be
hard to interpret and is more likely to find application
in automated decisioning environments (see next
Section 5.2).
Staff will need to be trained to understand how to use
the additional information especially the bureau credit
score which is significantly more predictive when driven
by CCR information (see Figure 6).

FIG 6 RELATIVE PREDICTIVE POWER OF CCR DATA ELEMENTS IN A
CONSUMER CREDIT SCORE
LENGTH
OF CREDIT
HISTORY
HOW MUCH
YOU OWE
CREDIT
MIX
NEW
CREDIT
YOUR
PAYMENT
HISTORY
Source: FICO, (2018)
Policy development and staff training on how to address
issues raised by potential mismatches between a con-
sumer’s self-declared liability position and that revealed
by CCR will also be required.
Close liaison with the bureau(s) will be needed as the
CCR environment transitions from negative-only data
as there is a risk that during this phase, as new CPs
provide large amounts of new data, the scores may
become volatile and difficult to interpret. Adding to this
complexity, in the case of a CP using more than one
bureau, is the likelihood that a score for an individual
returned from one bureau will not necessarily be the
same as the score for the same individual returned by
another. CPs will need to anticipate this outcome and
have policies in place to manage any such disparities.
This issue is considered further in Section 6.
Procedures need to be implemented that allow for a rapid
and fair response to customer queries about their credit
report, along with efficient processes for error detection
and data correction. CPs need to be aware that a con-
sumer can request any lender to make a correction to
their credit file and that “first port of call” CP is then
responsible to follow up and resolve the issue, even if it
is with another lender. Accordingly, a CP’s complaints
and corrections and dispute resolution processes will
need to be reviewed and updated. Customer complaints
and requests for corrections could have a large opera-
tional impact on a CP. While there are valid concerns
about the potential volumes and overheads arising from
this process, experience in New Zealand, where CCR
was introduced a year ahead of Australia, is that the
worst fears of CPs in this regard have to date proven
unfounded.
Capturing and maintaining accurate data is essential, not
only to be compliant with Privacy Act but also to protect
against the possible activities of Credit Repair Agencies
whose operation may not be in the best interests of con-
sumers and which may serve to weaken the integrity of
the credit reporting system. This is because dispute
handling is costly and time-consuming for a CP which
may lead to a decision being taken to “correct” data on
the bureau(s) rather than continue with the case. Credit
Repair companies may see an opportunity, with much
more data in the system under CCR, to pursue correction
and complaint actions more vigorously, especially if a
CP does not have highly accurate data and robust data
management processes in place.
CPs NEED TO BE AWARE THAT A
CONSUMER CAN REQUEST ANY
LENDER TO MAKE A CORRECTION
TO THEIR CREDIT FILE AND THAT
“FIRST PORT OF CALL” CP IS
THEN RESPONSIBLE TO FOLLOW
UP AND RESOLVE THE ISSUE
15%
10% 10%
35%
30%

5.2 AUTOMATED DECISIONING
Systems changes will be required to enable a CP
to consume additional CCR data from the bureau(s).
These include specifying and building a much broader
data feed that will cater for all CCR fields (at acceptable
performance speeds) plus selected data elements and
pre-calculated “data blocks” that typically are used in a
custom scorecard or added to a “Decisioning System”
to define new policy rules.
CCR may prompt a review of the role of manual versus
automated decisioning more generally, since there
are significant long-term benefits to be achieved from
introducing or extending the latter. The “Decisioning
Roadmap” referred to in Section 2 will guide actions in
this regard.
In the immediate short term, however, it is likely that,
until the CCR environment matures, there will be a need
for continued manual assessments until a sufficiently
rich database has developed from which data-driven
automated decisions can be made with confidence.
Links to additional bureaus will be required for CPs that
wish to adopt a multi-bureau strategy and consideration
needs to be given as to how to combine the information
received from multiple sources. This IT project will require
several months of design, build and testing and should
also provide “future proofing” in terms of a flexible
extension capability for additional data fields and
databases not yet defined or in scope (for example
income and expenditure information).
An important aspect of CCR data consumption and
one that will maximise its benefit is to look beyond the
use of CCR in originations. CCR information is much
more powerful than negative data in customer management,
including collections, and its automated implementation
at all parts of the credit lifecycle should be considered.
This means linking CCR data to behavioural management
and collections systems, with an associated review of
policies and processes (see Section 5.2).
For the evaluation of bureau data, a test and learn
environment will be essential. Ideally this would capture
and store data on applications from each bureau over at
least a 12-month test period. It would include information
on match rates, score, data blocks (if required) and
record the depth and breadth of bureau data received
from each bureau. This data should be matched against
the future performance of each account so that the relative
predictive power of each bureau can be assessed. This
process should be one of continuous monitoring. The
results from analysing this database should be used
to inform the CP’s multi-bureau strategy, both on day
one and going forward.
IN THE IMMEDIATE SHORT TERM IT IS LIKELY THAT, UNTIL THE CCR
ENVIRONMENT MATURES, THERE WILL BE A NEED FOR CONTINUED
MANUAL ASSESSMENTS UNTIL A SUFFICIENTLY RICH DATABASE HAS
DEVELOPED FROM WHICH DATA-DRIVEN AUTOMATED DECISIONS
CAN BE MADE WITH CONFIDENCE

CCR INFORMATION IS MUCH
MORE POWERFUL THAN
NEGATIVE DATA IN CUSTOMER
MANAGEMENT, INCLUDING
COLLECTIONS, AND ITS
AUTOMATED IMPLEMENTATION
AT ALL PARTS OF THE CREDIT
LIFECYCLE SHOULD BE
CONSIDERED

Use cases for the operational integration of CCR data
will vary considerably between CPs according to the
nature of their credit decisioning environment.
For example, the introduction of CCR data to a manual
credit decisioning environment is very different from
implementation into an auto-decisioning process.
Further, CPs will likely have different priorities as to
whether CCR will be focused on originations, customer
management, collections or all three stages of the credit
lifecycle. The strategy discussions referred to in Section
2 should inform priorities here.
CPs will also differ in their chosen timescales to participate,
which means that each will have to manage different
issues arising from the broader CCR environment being
immature and in transition.
A fully-fledged CCR environment is likely to take one or
more years post September 2019 (the date by which it
is anticipated major banks will be required to contribute
CCR for all accounts).
Given these disparate use cases, the next part of this
paper is divided into sub-sections that focus on manual
versus auto decisioning, by credit life cycle stage.
6 DATA INTEGRATION
AND USE CASES
6.1 USE CASE 1 – ORIGINATIONS
6.1.1 MANUAL DECISIONING CONSIDERATIONS
Steps to be taken here in order to operationalise the use of CCR in credit decisioning include:
Staff training to explain the new fields that will appear
on a consumer credit report – PDF or (preferably) PDF
plus data feeds that need to be stored and will provide
a basis for any future development of auto–decisioning
capability
Policy development to apply the new fields for manual
decision-making. For example, a CP may have previously
declined any applicant with a default on the bureau, but
may now change policy to accept such applicants if
they have demonstrated a good repayment history
profile over the past 12–24 months
Deciding how far to rely on the bureau score, especially
in transition
If there is a bespoke scorecard, deciding how to blend in
the new CCR data until such time as the existing scorecard
is redeveloped. Make use of one or more bureau scores
as an interim measure
Creation of a database to include single or multi-bureau
data matched to the accept/reject/refer decision and
subsequent performance if the application is approved
(see next section on a “decisioning data mart”)
If more than one bureau is being used, decide how to
combine the data from each in manual decisioning (e.g.
simply use the report that shows highest risk, or adopt
an approach that would assess the breadth and depth
of data from each bureau and/or blend reports)
Considering creating a champion–challenger “test and
learn” environment, especially if risk-based pricing is to
be introduced (this also links to the design of the
decisioning datamart)
Ensuring that there is a monitoring system in place to
identify bureau (and bespoke if available) scorecard and
policy rule performance, including policy overrides
Reviewing the adequacy of the current data and analytics
teams. Hire quickly and effectively and/or enlist the
support of third parties if future demands look likely to
exceed current capability/capacity. There is currently and
will continue to be a shortage of capable risk analytics
personnel – consider hiring and training quality graduates
Providing front line staff with training and scripting
regarding customer FAQs
Being prepared to see reduced approval rates as a
result of CCR revealing previously undisclosed credit
commitments and developing appropriate scripting
(see above)
Deciding on the timing for participation and on portfolio
prioritisation for implementation

6.1.2 AUTO-DECISIONING CONSIDERATIONS
In addition to the points in 6.1.1, there are additional considerations that arise when introducing CCR into credit
decisioning in an automated environment. These include:
Reviewing the current auto-decisioning systems
(technology and data-bases) to assess their fitness for
purpose for CCR
The database review should cover CCR and open
banking requirements and may require the creation and
maintenance of a decisioning data mart separate from
but feeding into a CP-wide master database/warehouse
For the technology review, close liaison is needed with
current vendor(s) and the timing may be a right for a
wider assessment of multiple vendors’ software and
database(s). This will enable a CP to fully understand the
range of competing decisioning software solutions in the
market and to identify the vendor whose functionality
most closely matches what the CP will require in order
to meet future needs, including CCR, as set out in the
“Decisioning Roadmap” (see Section 2.2)
The selection of the appropriate technology is a very
important exercise, and a CP will need to ensure that
it can provide sufficient time, investment funds and
capability (including third party suppliers) to ensure
success
Specifying decisioning system changes. These include
new data definitions and import functionality for the
CCR fields which will need to be designed, built and
tested, in close liaison with third party suppliers
These changes also include revising and coding into
the decisioning system new policy rules. These may be
driven by individual CCR data elements or data blocks,
as well as scores. Exhaustive testing prior to go-live will
be required
Close monitoring of all bureau scores and associated
performance data will be essential as the CCR
transition phase matures
Creating an automated champion-challenger environment
for, inter alia, multi-bureau evaluation and risk-based
pricing. Both this and the previous point will need to be
supported by the decisioning data mart (or similar
capability)
CPs WILL LIKELY HAVE DIFFERENT
PRIORITIES AS TO WHETHER CCR
WILL BE FOCUSED ON ORIGINATIONS,
CUSTOMER MANAGEMENT,
COLLECTIONS OR ALL THREE
STAGES OF THE CREDIT LIFECYCLE

6.2 USE CASE 2 – CUSTOMER MANAGEMENT
AND COLLECTIONS
The use of CCR information in customer management and collections will differ according to whether the CP’s
environment is manual or automated. In a manual environment the likely use of CCR information will be in referencing a
consumer’s credit report when in discussion with the customer. In automated decisioning, CCR data will be used in
defining and prioritising customer contact strategies and driving outbound dialler calls.
6.2.1 MANUAL DECISIONING CONSIDERATIONS
In a manual environment the most likely use of CCR
information will be in collections. Where accounts are in
arrears they can be sent (in batch) to the bureau(s) who
will return a score and other data that shows how the
CP’s customer is performing with other credit providers.
Bureaus will also offer “alerts” and scores on a batch or
real time basis to inform a CP of any significant change
in a customer’s arrears status with other lenders that
would make the customer at “significant risk of defaulting”
(see Section 3.5). Incorporating this information into
customer management and collections processes along
with the development of policy rules that minimise “false
positives” will require changes to data structures and
policy settings.
To use this information in the collections process there
needs to be a data link between the CP and the bureau
for data transfer, and an automated means by which
collectors can then easily see and understand the CCR
data (PDF or – ideally – on screen as data fields).
Collections strategies will need to be redesigned to take
account of the new CCR data. For example, where a CP’s
customer is in arrears with several other lenders it will
be important to accelerate collections actions even if
the customer is not in the worst arrears status with the
CP itself.
6.2.2 AUTO-DECISIONING CONSIDERATIONS
For CPs that have an automated customer management
system (as distinct from collections) this will need to be
adapted to import the new data fields. Monthly updates
by batch file transfer is the most likely form of data
supply from the bureau(s) to a CP.
Policy rule changes will need to be coded into the
system including how to respond when seeing an “alert”
or a deteriorating score. These may prompt a review of
“pre-collections” actions for those customers identified
as at “significant risk of defaulting”, depending on the
nature of the CCR information returned by the bureau(s).
Policy changes and the rationale for them will need to be
fed into operational collections processes. These include
a direct link to the auto-dialler and a feed into operator’s
screens to assist with customer conversations. A review of
a CP’s collections system should be undertaken to ensure
that it is able to cater for the implementation of CCR.
A review of scripting will also be required as well as a
redesign of contact strategies to take the new data into
account.
Database changes will be required to build a historical
record of bureau and CP data received and used, response
adopted, and subsequent outcome. This will help optimise
collections actions and underpin “champion-challenger”
strategies.
IN AUTOMATED DECISIONING,
CCR DATA WILL BE USED IN
DEFINING AND PRIORITISING
CUSTOMER CONTACT
STRATEGIES AND DRIVING
OUTBOUND DIALLER CALLS

USE CASES FOR THE INTEGRATION
OF CCR DATA INTO OPERATIONAL
PROCESSES WILL VARY CONSIDERABLY
BETWEEN CPs ACCORDING TO THE
NATURE OF THEIR CREDIT DECISIONING
ENVIRONMENT

The main issues to consider as CCR transitions to maturity are volatility in the
bureau data and how to manage RHI for customers in hardship. This paper
summarises these issues as of today. However, recognising that there will be
ongoing change over coming months, an ongoing update to this “Transitional
Issues” section will be available on the David Grafton Consulting website
– see davidgrafton.com.au for news as these issues develop.
7.1 DATA VOLATILITY AND VARIABILITY
As the volume of CCR data being reported grows over
time, it is likely that, as new data suppliers move from
“Private Settings” to “live”, there will be an impact on
the data that CPs receive. When a large CP starts live
supply, this will mean that more CCLI and RHI data
enters the system and this will mean that a consumer’s
credit report will differ pre and post go live date.
Also likely to change will be the customer or applicant’s
score. Compounding this issue is the possibility that
different bureaus will be affected differentially, since
there is no obligation on a CP to provide data to all
three bureaus.
This could become an issue of concern for customers
and CP staff need to be informed and able to respond
appropriately to customer enquiries.
More importantly, such changes could impact decisions
taken at originations, customer management and collec-
tions stages of the credit lifecycle. While bureaus will
endeavour to minimise this volatility, recent experience
in New Zealand shows that bureau scores can shift
significantly following a large volume initial CCR data
supply by a major CP.
Such volume increases will also affect “alerts” that are
likely to jump in frequency as a result of the new data
being available.
CPs will need to maintain close links with their bureaus
and with ARCA to receive advance notice of any such
high-volume contributions and decide how to maintain
reasonably consistent approval rates and customer
management practices given the potential for bureau
score instability.
7 TRANSITION ISSUES

7.2 RHI REPORTING FOR CUSTOMERS IN HARDSHIP
There is as yet no consensus regarding how
to report RHI for customers in hardship. The Office of
the Australian Information Commissioner has published
guidance requiring RHI to be reported against the new
terms of a varied contract, if a contract has been varied
on grounds of hardship (or for any other reason); and
requiring RHI to be reported against the existing
contractual obligations if a customer’s contract has
not been varied, even in cases where an indulgence
has been given. (OAIC, 2017).
Despite that guidance, external dispute bodies continue
to raise concerns about negative RHI that is reported
after a customer has made a hardship notice to the credit
provider (even where the contract has not been varied).
For CPs, the OAIC guidance means that RHI recorded
on the bureau(s) for customers paying to (usually more
lenient) terms under a contract varied in response to
hardship will be indistinguishable from the vast majority
of customers who are also paying to terms but not in
hardship. To assist with this issue, which is fundamental
to responsible lending, industry has proposed that an
additional data field be included in the credit reporting
system. The suggestion is to allow for a “hardship flag”
to be posted against all customers who have had their
contracts varied under hardship.
For customers that have been granted an “indulgence”
but are still bound by their existing contract, RHI needs
to be reported against that contract (see NCCP, 2009).
Currently, industry practice is not consistent in reporting
arrears whether under a hardship notice, a contract
variation, or an indulgence arrangement. Some CPs freeze
arrears, some re-age, some grant temporary relief (with
time periods that differ from one CP to another), some
agree a large number of contract variations and some
do not.
In addition, there has been strong opposition to the
introduction of a hardship flag from consumer groups
fearing that such a flag, being visible to lenders, will
dissuade a customer from applying for hardship relief
even though this may be their best course of action.
These concerns have now taken on a partisan political
dimension, and are arising in political discourse around
mandating credit reporting.
This issue has become the subject of a review by the
Attorney General’s Department (see Senate Economics
References Committee, 2018) and the Australian Banking
Association has proposed a 12-month deferment of the
mandated requirement for banks to provide RHI in order
to give industry time to come to an agreed position
(Australian Banking Association, 2018). Such a delay
would have a serious impact on the utility of RHI.
THERE IS AS YET NO CONSENSUS
REGARDING HOW TO REPORT RHI
FOR CUSTOMERS IN HARDSHIP

CONCLUSION
For those already on the journey it can be used as a
checklist, while for CPs that are not as far advanced, it
provides a framework and outline project plan to guide
lenders to the ultimate goal of successful participation
in CCR.
in CCR.
After many years in gestation, CCR is now becoming a
reality for CPs in Australia. A wide variety of lenders is
now choosing to take part in CCR, even if not mandated
to do so, and are preparing to go live with data supply
and consumption in coming months. For many, this task
appears daunting in its complexity, cost and risk.
This paper is a contribution to making that process
clearer for all CPs, with a view to encouraging as many
lenders as possible to participate in CCR. In pursuit of
that goal, the “roadmap” spreadsheet, available as a
link at davidgrafton.com.au sets out a month by month
timeline to help CPs to identify those actions that need
to be taken by each stakeholder group at each step of
the CCR implementation program.
in CCR.

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(Mandatory Comprehensive Credit Reporting) Bill
2018’
www.aph.gov.au/Parliamentary_Business/
Committees/Senate/Economics/Recent_reports
Yeates, C 2018, ‘Banks ramp up sharing of ‘positive’
data on customers, despite concerns’
www.smh.com.au/business/banking-and-finance/
banks-ramp-up-sharing-of-positive-data-on-
customers-despite-concerns-20180713-p4zray.html

2626
ACKNOWLEDGEMENTS
The author gratefully acknowledges the useful
comments made on an earlier draft of this paper
by Geri Cremin and Mike Laing, ARCA,
Steve Johnson, RCSCS, Mike Lawrence, COBA
and Guy Harding, 86400.
ABOUT THE AUTHOR
David Grafton was a founder member, former
Chairman and for 10 years a Board member of
ARCA. He worked for 8 years as CRO of CBA’s
Retail Bank and has run two of Australia’s credit
bureaus, including their related software and
analytics businesses. David is currently providing
Risk Management consultancy services through
his company “David Grafton Consulting” and
can be contacted at davidgrafton.com.au and
on LinkedIn at linkedin.com/in/david-grafton.

DAVIDGRAFTON.COM.AU | DAVIDGRAFTONCONSULTING@GMAIL.COM

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