25 Ways the Consumer Data Right Can Create Smoother and Smarter Customer Experiences

25
www.adatree.com.au
WAYSThe Consumer Data Right
Can Create Smoother and
Smarter Customer
Experiences
Enabling Industries To Create Competitive
Advantages With Data

CONTENTS
of CDR Use Cases
INTRODUCTION
Consumer Data Sharing Rights
OVERVIEW
1
2
3
4
5
6
7
CATEGORIES
of CDR Use Cases
SMOOTHER
Use Cases
SMARTER
Use Cases
ABOUT
the Consumer Data Right
ADATREE
Open Banking Solutions
1
2
4
5
25
37
39

The Consumer Data Right (CDR), also often referred to as Open
Banking, gives consumers the right to share their personal data with
organisations that they trust. Customer, product, account, and
transaction data are typically held by organisations, such as banks. If
this data (CDR Data) is shared with other organisations, then it can be
incorporated into the recipient’s product and service offerings. This
drives innovation and competition. The CDR is speciﬁcally aimed at
improving customer outcomes more broadly.
Many organisations wonder how receiving CDR Data can beneﬁt
their business. Is the CDR relevant to their industry? How can they
provide better outcomes and experiences for their customers? How
should they go about improving their products and services? What
are the ‘killer’ use cases?
With customer’s consent, organisations can access to CDR Data in
order to provide smoother and smarter experiences to their clients.
Adatree details 25 unique use cases of how the CDR can transform
customer and business experiences. The framework is industry
agnostic and can be applied to any type of data being shared. It is the
starting point for any organisation exploring potential opportunities
of the CDR.
The Adatree team is available to discuss the use cases further and
ideate with any organisation looking to leverage the CDR. We are
driven to grow the Open Banking ecosystem in Australia.
An Introduction toChanges in Consumer Data Sharing Rights
Adatree Consumer Data Right Use Case Report 1

CDR opens a new world of opportunities for organisations and
consumers alike. Simply complying with CDR does not improve an
organisations’ competitive position. The CDR drives organisations to
build end-to-end customer journeys that solve real problems. Just
building APIs will not ensure customer growth. Successful use cases
have to show a clear benefit for both the consumer and the
organisation.
Use Cases Must Benefit Consumers:
Consumers now have the right to share their data but will only give
their consent when they trust the Data Recipient and when they
expect a clear benefit from sharing their data. This is not a given nor
is it easy. The CDR’s strength resides in the fact that it forces
organisations to look for improvements from the customer's
perspective. Control now rests with them.
Use Cases Must Benefit Organisations:
On the other hand, each use case needs to have a clear commercial
benefit for organisations who will expect a direct or indirect benefit
to impact on their bottom line. Becoming an Accredited Data
Recipient is not easy, nor are the actual building and implementation
of the solution. There is no point in ingesting data for the sake of it. If
the business case does not add up, it will not work.
Adatree has identified a simple set of use cases that benefit both
organisations and their customers. They will all improve customer
experiences and help Data Recipients compete.
OverviewOf CDR Use Cases

CDR Use Cases Can Be Relevant To All Sectors:
While the CDR will encourage direct competition between Data
Holders in the same sector, receiving CDR Data can be utilised by a
wider range of sectors. Product data is expected to increase
competition between product providers. However transaction data
could be hugely beneﬁcial to organisations in many other sectors, for
example Government, Utilities and Retail.
Use Cases To Stimulate Strategic Discussions:
Adatree hopes this overview will stimulate thinking around a wide
range of use cases and help drive competition and innovation as the
CDR intends to. As a provider of a Data Recipient Platform, we
remove barriers to participating in the data sharing ecosystem for
any organisation, regardless of size of company, industry or use case.
Our team is happy to chat, share ideas and explore use cases to make
the CDR an economy-wide success.
Overview

SMOOTHER SMARTER
1. Customer Onboarding
2. Financial Verification
3. Account Verification
4. Customer Identification
5. Competitive Pricing
6. Product Selection
7. Payee Switching
8. Customer Loyalty
9. Customer Referrals
10. Streamlined Invoicing
11. Reduced Rejections
12. Account-to-Account Payments
13. Optimised Budgeting
14. Information Updates
15. Switching-as-a-Service
16. Subscription Management
17. Consented Usage
18. Transaction Triggers
Personalised Pricing
Peer Comparisons
Changing Needs
Right-Time Targeting
23. Credit Assessments
24. Outcome Prediction
25. Behaviour Prediction
Categories
Of CDR Use Cases
A. Trusted Inputs
B. Streamlined Processes
C. New Services
D. Actionable Datasets
E. Personalised Offerings
F. Improved Predictions
19.
20.
21.
22.

Data Recipients can provide much smoother experiences to their
customers. In these use cases, the CDR drives innovation in operations. Use
cases in this category benefit from the fact that data is ingested. Through
automation, effort is reduced and scalability of processes increased. These
improvements have a calculated return on investment with enhanced
customer experience evident. Consumers mainly benefit from reduced
friction in experiences; Either directly as they get to outcomes quicker, or
indirectly by reduced cost of services. Data Recipients benefit from higher
quality inputs, increased streamlining of existing processes and enablement
of entirely new services.
Trusted Inputs:
Because CDR Data is ingested using automation, replacing manual data
entry data quality increases.Automation leaves limited room for error and
fraud. Inputs are more reliable and its authenticity can be assumed.
Certification or verification of the data can be shared between
organisations and taken for the truth by the Data Recipient. This can
drastically increase operational efficiencies and reduce fraud related costs.
Verification activities, previously performed manually, through risk
assessment or other means, are made redundant. As the CDR Data can slot
into existing processes, the effort to implement these use cases should be
relatively low compared to others.
Streamlined Processes:
Data is ingested in a consistent, machine-readable format. It can be
processed directly by applications within and across organisations. Manual
processes can be replaced by automated processes. Data Recipients can
find these types of CDR use cases by looking for any steps in customer
journeys where information that currently isn’t available in your
organisation is required to fulfill the step.
Smoother

If this information is part of CDR data, or can be derived from it (Note:
a plethora of insights can be derived from transaction data), you have
found a candidate. For use cases in this category, existing processes
need to be redesigned. This may require changes to customer
interfaces, back-end systems and operations.
New Services:
Lastly, the fact that data can be ingested opens up a range of entirely
new services as additional customer problems can be solved. Initially,
Read Access is only available by Data Recipients, and Write Access
will be introduced at a later stage. Such new capabilities enable the
redesign of core processes. While these use cases will likely be more
effort and time intensive to implement, they are truly innovative in
creating new customer value propositions or even new businesses
with sustainable competitive advantages.
Smoother

Customer Onboarding
Problem:
During onboarding, customers and/or bank employees often enter
information manually and repeatedly. Slow and difficult onboarding
experiences are inhibitors for customers to switch services and adopt
products offered by other organisations.
Solution:
At the start of an onboarding flow, an Accredited Data Recipient asks
for customer’s consent to share different types of data held by Data
Holders. By ingesting customer, product, account, and transaction
data, the ADR can automatically map the required data points and
prefill much, if not all, of the application form. This transforms the
process from onerous to automated for customers and results in
higher quality data for the ADR.
Example:
Shannon is looking for a personal loan and wants the fastest
application and approval. Wombat Bank is a Data Recipient and
receives Shannon’s customer, product, account, and transactional
details. With this information, 68% of the customer onboarding
details can be prefilled, estimated to cut 90% of time off her
onboarding process1
. Shannon feels less stressed about the loan
process as it was done easily, paper-free, and quickly.
Relevant for all sectors that request customer data when starting new
relationships (e.g. banking, energy, non-bank lending, loan broking,
insurance, financial advice, financial technology, telecommunications,
government, education, financial planning, real estate, travel,
wholesale, superannuation, and human resources).
A. Trusted Inputs
1.
This is taken by analysing a third-tier ADI’s application process against data fields shared in the CDR.
1

Financial Verification
Problem:
Businesses often have to assess a customer’s ability to repay. For real
estate agents, they have to see if someone can afford the rent. With
banks and lenders, they are subject to Responsible Lending obligations
for verification of income and expenses. Many companies complete
this manually with bank statements which incurs significant
operational overheads increasing costs to business to assess customers
and which negatively impacts the customer experience.
This is taken by analysing a third-tier ADI’s application process against
data fields shared in the CDR.
Solution:
ADRs can receive the CDR Data digitally in a consistent formats from
all banks, which helps them assess, categorise, and sort through years
of expenses and transactions with automated analysis. Sharing and
assessing bank statements is no longer required, as assessments can
be streamlined with minimal manual work.
Example:
The local real estate agent used to collect payslips and bank accounts
with all tenant applications, sometimes in person with hard copies or
copies being emailed to them. The real estate agency is now an ADR
able to collect CDR Data to assess their affordability for the rent given
their income. Customers share fewer data and can elect for the real
estate agency to delete it after the sharing period expires or is
withdrawn.
Relevant for all sectors that analyse income and expenses (e.g.
banking, non-bank lending, loan broking, real estate, financial advice,
and education).
A. Trusted Inputs
2.

Account Verification
Problem:
When customers set up payments to a company, funds are allowed
to be debited from a card or an account. To avoid debiting funds
from accounts that do not belong to the customer, companies
collecting funds should verify the account’s ownership first. The
current way of doing this is a manual review of bank statements to
prove ownership or no verification takes place at all. Companies run
the risk of collecting funds from an incorrect account or card. This
can lead to chargebacks, fraud, operational overview, and poor
customer experiences. This can apply to direct debits and
card-not-present transactions.
Solution:
Open Banking solves this issue when a customer shares a CDR
account and customer data with the ADR. This verifies account
details and ownership in near real time. This should result in
decreased fraud instances with decreased losses and operational
overheads. This is a better experience for the customer in terms of
efficient means of digital authentication for their own account and
peace of mind as there will be no unauthorised payments debited
from their account.
Example:
Kanga Credit Union used to onboard customers with certified
copies of identity documents and relied on a signed Direct Debit
Request Service Agreement for authority to debit payments from
external accounts. They would occasionally learn that identity
documents had been forged with a fake certification, and
complaints would come in from other organisations about
unauthorised debit payments. Being a Data Recipient, their fraud
decreased with simplified identification of customers with
A. Trusted Inputs
3.

an outcome from another organisation and verification of the
owned account to debit. Their customers were happier and had less
to do manually, and the load on the Fraud, Risk, Complaints, and
Operations teams decreased.
Relevant for all sectors that collect payments from an external
account or payment methods (e.g. banking, non-bank lending,
telecommunications, energy, education, government, retail,
hospitality, wholesale, health, payments).
Customer Identification
Problem:
Organisations have to identify customers according to different rules
and standards that are relevant to their sector. Although it is possible
to identify customers digitally, it is still often necessary to manually
verify documentation, which can be a costly and demand manual
exercise. In particular, for businesses, it can be cumbersome to derive
the company structure and ownership structure and identify
multiple authorised signatories. Manually requesting further
information back and forth can be a negative for the relationship
when additional investigations and exchanging of paperwork can be
required.
Solution:
One of the recommendations in the Review into Open Banking
(December 2017) was that Data Holders should be obliged to share
the outcome of an identity verification assessment performed on the
customer. For this to occur, anti-money laundering laws have to be
amended to allow ADRs to rely on that outcome (Recommendation
3.4). This could solve a major customer identification hurdle. If the
A. Trusted Inputs
4.

amendments were to occur, this would make it easier for customers
to switch between providers as identity assessments could be relied
on instead of performing new ones. This would decrease barriers for
customers switching with a less onerous process and faster process,
while privacy and security would be enhanced with decreased
document sharing. For organisations needing to identify customers,
this would streamline customer onboarding with decreased
customer servicing, decreased operational support requests, and
decreased costs of assessing and verifying customer’s identification.
Example:
Alan is a business owner and has found a new bank that can meet
his needs better. Getting all of the required paperwork is an
administrative nightmare so he has never switched providers. He
dreaded having to resubmit the trust deed, beneficial ownership
details, information, and identification documents for multiple
directors. When the new regulation comes into effect and allows
the new bank to rely on his former bank’s identification of the
business, Alan can simply switch without any hassle and the
innovative bank has gained a new customer.
Relevant for all sectors that are required to verify and identify
customers (e.g. banking, non-bank lending, travel, education,
government, real estate, and wholesale).
A. Trusted Inputs

Competitive Pricing: No ADR Accreditation Required
Problem:
Comparing offers is difﬁcult for customers and it is hard to be sure
that their offer is the best available. Comparison sites often only list
products from selected suppliers that pay for the privilege of listing
their products, and there is not a full dataset of all products, offerings,
and pricing. To compare their offerings to competitors, organisations
often rely on manual Internet searches or cold calling. This is a costly
and repetitive process.
Solution:
Data Holders are required to expose their product information in a
machine-readable form (an API for product reference data). Product
reference data is publicly available so the organisation receiving this
does not have to be an Accredited Data Recipient. Organisations can
receive accurate, up-to-date, and comprehensive information related
to product features and pricing, which reduces manual workload of
employees searching for competitive information.
Example:
Wombat Bank wants its savings account to be the best in the
market. The website compares it to other bank rates that were
manually checked. Business analysts at Wombat Bank spend hours
each week looking at competitor products’ pricing. They did this on
top of paying for incomplete market pricing reports. Wombat Bank
now leverages publicly available product reference data via APIs to
receive real-time product pricing updates from all Data Holders.
Relevant for all sectors that collect market pricing information and
compare offerings or support customers to decide on product
offerings (e.g. banking, comparisons, loan broking, and non-bank
lending).
A. Trusted Inputs
5.

Product Selection
Problem:
It can be hard for a customer to select the right product, particularly, in
financial services where products can be complicated or technical.
Customers often rely on recommendations rather than going through
detailed product specifications and comparisons themselves. This
disconnect between headline rates and actual eligibility can lead to
customers applying for products they are ineligible for, or not getting
the best suitable product.
Solution:
Receiving CDR Data as an ADR enables organisations to make
personalised product recommendations at scale. Consumer data can
be used to assess eligibility and suitability. A personalised product
recommendation can be provided to the customer instantly and at
minimal cost to the organisation. This ensures that customers will take
up products best to them and prevent unpleasant surprises further
down the line.
Example:
Jacob is looking for a personal loan, and usually applies for one with
the lowest rate on comparison sites. He has been unpleasantly
surprised before, when he found out that, based on his financial
situation, the headline rate wasn’t actually applicable to him. He has
now found a comparison site that gives him a personalised
recommendation based on his transactional information. They not
only take his salary and credit score into consideration but also
personal habits like how often he withdraws and salary payment
frequency.
Relevant for all sectors that make product recommendations or
comparisons (e.g. banking, comparisons, foreign exchange, energy,
telecommunications, financial planning, loan broking, and non-bank
lending).
6.

Payee Switching
Problem:
A large downside in opening a new bank account is that any
information relating to payees remains with the old bank. It takes
time and effort to reenter or rearrange all contacts, account
numbers, recurring payments, direct debits, and scheduled
payments. It is inhibiting customers from switching, making it
difficult for new banks to become the customer’s main financial
institution.
Solution:
Customers can consent to share payee information from other bank
accounts with an ADR. This information could include their full payee
address book with Pay Anyone details, recurring payments, BPAY
details, direct debits, and scheduled payments. Customers have
control over selecting the information that they want to transfer over
to the new account. The customer feels independent and organised,
while the ADR is well on its way to establishing a strong relationship
with the new customer.
Example:
Regina just switched to Wombat Bank since they have a better rate
and a more suitable product for her. Instead of having to reenter all
of her payee details in her new banking address book, Wombat
Bank asks Regina if she would like to transfer all or some of the old
payee details. She selects yes to all, and the payee details are
replicated in her new bank account. Wombat Bank uses Adatree’s
Switching module for an out-of-the-box account and payment
switching capability.
Relevant for all sectors that keep records of payee details (e.g.
banking, financial planning, and non-bank lending).
7.

Customer Loyalty
Problem:
Customers can get rewarded for their loyalty through points,
discounts, and personalised offers, among others. The loyalty
programs keep track of the customer usage of their products. This
can be a manual process, particularly, if a product was purchased
through a third party. Customers might not get the correct rewards,
and loyalty programs need to maintain an operations team to
respond to customers' queries.
Solution:
By ingesting customer transaction data, loyalty programs can register
loyalty benefits in real time. Loyalty offers can increase in
personalisation and customers can receive rewards quicker. Loyalty
programs can have deeper insights into customer behaviours,
supplier relationships, and purchasing context with reduced costs.
This can impact cash-back offers, digital receipts, and comprehensive
transactions across banks, cards and payment methods.
Example:
Oscar has an obsession to accumulate points with his airline’s loyalty
program, and he changes where and how he shops to earn more
points. As part of the loyalty program, he shares this CDR Data to
earn more points through his everyday transactions. The loyalty
program can view which companies Oscar spends with, how he
switches between companies, and his spending amounts and
frequencies. Accessing this real-time information enables Oscar to
be granted points and bonuses as soon as the verified transactions
happen.
Relevant for all sectors that drive customer loyalty or provide loyalty
programs (e.g. non-profit, banking, non-bank lending, travel, retail,
hospitality, financial technology, and community).
8.

Customer Referrals
Problem:
Businesses refer customers to each other, often with special offer
codes. Referral programs often rely on link tracking or codes that
customers will have to enter manually. They introduce operational
overhead, potential for errors, and delays for customers receiving the
beneﬁts.
Solution:
When customers are referred from one partner to another, this could
utilise both the Read and Write Access to analyse customer
transactions and open up new accounts on a customer’s behalf
(subject to future rules of Write Access). This automates previously
manual tasks, reduces errors and lost referrals, and ensures instant
and accurate data on referral program results. The customer has a
smoother onboarding experience with decreased inertia if they want
to take up new offers and start a new relationship seamlessly.
Example:
A company specialising in getting group-buying deals used to send
out offers to their customer base and they would only know if the
offer was taken up when a speciﬁc code was entered manually
during onboarding. With consented data sharing, the company can
now automatically track the changes in customer’s providers and
ensure that discounts are applied, decreasing the operations of
tracking offer uptake.
Relevant for all sectors that use referrals of any kind (e.g. loan
broking, eCommerce, travel, insurance, retail, and comparison).
9.

Streamlined Invoicing
Problem:
Invoices are the lifeline of a business’s cash flow. The effort to receive
payments for invoices in a timely manner is costly. Late payments have
to be followed up, payments with wrong references require
investigation, fraud risks can be high, and errors are common.
Solution:
Through the introduction of Write Access, business-to-business
payments can be transformed. It can automate the issuing and
payment of invoices. Payments are debited directly when a certain
milestone or duration passes. Any type of existing payment
methodologies can be used. The ownership of the accounts involved is
verified automatically, reducing risk. The costs of invoicing are drastically
reduced in the ecosystem, benefiting both suppliers and consumers.
Example:
Milan is a sole trader that supplies goods to a construction site. During
the project, he gets paid each time he delivers the goods to the site. In
the contract, the invoicing structure is set, and the payment structure
is agreed with an independent ADR with Write Access. The ADR
manages payments between organisations to automate payments,
invoices, and reconciliation.
Milan knows that when the goods are delivered and checked on-site,
he gets paid automatically. The truck driver he has on contract also
gets paid instantly. Managing these payments safely and securely
ensures that Milan can focus on his business and maintains great
relationships with all parties to deliver the project. Payments between
parties occur faster and with less friction, as they are time- or
milestone-based.
Relevant for all sectors that use invoicing to pay for goods and reconcile
transactions (e.g. banking, payments, wholesale, manufacturing,
accounting, government, healthcare, retail, hospitality, and professional
services).
10.

Reduced Rejections
Problem:
Whether in times of default or everyday billing, payments can often
be rejected when requested to a card or an account. For customers,
this causes possible fees charged to them and additional work to
initiate payments made. For businesses, this increases operational
overhead to chase payments.
Solution:
Organisations that are ADRs owing money can view customer’s
real-time balances to check when available funds are in the account
before triggering a direct debit. This reduces dishonour fees,
allowing the consumer to be notiﬁed of insufﬁcient funds without
the usual penalty.
Example:
Jack used to receive calls often from a company he owed overdue
monies to. He would often forget to send the money when he was
paid, which led to further emails and phone calls. With the
increased customer visibility as an ADR, this enabled the funds to
be pulled from Jack’s account upon agreement and to ensure the
direct debit would not be rejected. This resulted in Jack being on a
personalised and affordable payment plan with less failed direct
debit fees, while the company introduced streamlined and
customer-friendly collection processes with decreased write-offs.
Relevant for all sectors that collect repayments from customers and
often have payments rejected (e.g. banking, non-bank lending,
energy, telecommunications, payments, and collections).
C. New Services
11.

Account-to-Account Payments
Problem:
Customers often pay for services through debit or credit cards whether
at a point of sale or through eCommerce. The surcharges incurred from
using these payment methods introduce transaction costs in the
ecosystem that are either absorbed by the merchant or passed on to
the customer. Transactions can take multiple days to process,
impacting the customer experience and delaying the sending and
receiving of funds. Card payments are an expensive payment method
and introduce a number of fraud and late-payment risks.
Solution:
When Write Access is defined and introduced to the CDR, customers
should be able to authorise payments and other information transfers
in a secure way. At the point of sale, customers could choose to pay
directly from their bank account to the merchant account,
circumventing more expensive payment methods. This could
introduce opportunities for non-bank fintechs to direct movement of
funds. Customers should be able to select the payment method they
want to use (e.g., Pay Anyone, NPP, and BPAY) for the
account-to-account payments. Costs across the ecosystem will
decrease and speed to receive the payments will increase as float
reduces.
Example:
A wholesaler regularly collects payments from its business customers
via card or manual payments from accounts. They used to follow up
manual bank transfers, which would often have an incorrect reference
or have operational over head when reconciling transactions. With
CDR Write Access, the customer could consent to send payments
directly from their bank account. Payments can be initiated and made
immediately with automatic reconciliation and error reduction.
Relevant for all sectors that currently use card schemes for payments
(e.g. retail, hospitality, healthcare, wholesale, eCommerce, government,
and banking).
12.
C. New Services

Optimised Budgeting
Problem:
Staying on top of your money and managing funds are challenging
and endless tasks. Funds need to move between different accounts
and organisations to earn interest and make sure that bills are paid
on time. Payments can fail if balances are not sufficient, and bills
could become overdue. Banks do not manage allocations between
accounts to maximise the interest you receive. It takes time and
effort to stay organised.
Solution:
Combining Read and Write Access with intelligent analytics allows
for automation of funds allocation. Funds can be redirected between
accounts from any bank. Customers maximise their asset allocation
and organisations gain real-time insights into customers’ financial
position and behaviours. Funds can be swept into their own
accounts, investment portfolios, charitable accounts or
superannuation so that microtransactions can power a customer’s
goals.
Example:
After realising that her savings rate was no longer the best in the
market, Bianca wanted her money to work harder for her. She
signed up for a smart budgeting app to help compartmentalise her
spending, savings, and investing finances. It automatically transfers
funds between accounts and invests based on her risk appetite.
Bianca now gets the highest at-call and investment returns and will
always optimise her earnings.
Relevant for all sectors that manage funds for customers (e.g. wealth
management, financial planning, comparisons, financial technology,
superannuation, wealth management, and banking).
13.
C. New Services

Information Updates
Problem:
When a customer has life changes, they need to update the same
information across multiple organisations, whether they change
their address, mobile, name, or bank account information. This is a
manual process and relies entirely on customers proactively
updating their details. This results in organisations often having
outdated, missing, or incorrect customer information, and customers
often miss important correspondence from organisations.
Solution:
When Write Access rules are in place, this can universally update the
customer data of organisations that a customer has relationships
with. The customer will only have to update their data once and has
peace of mind that all of their suppliers and providers have updated
information. Organisations benefit from up-to-date information and
reduced operational costs when responding to requests to change
information and following up returned mail.
Example:
Sarah wants her life to be as seamless and effortless as possible, so
she signed up to a new fintech company that aims to be her virtual
assistant. When she has any personal information updates, from
new credit card details to a change in mailing address, the
company scans her transactions to list all organisations that may
require this information and pushes out an update. Sarah has saved
a lot of time and hassle.
Relevant for all sectors that rely on customers’ personal and/or
payment information to be correct to provide specific services (e.g.
financial technology, banking, superannuation, insurance, energy,
telecommunications, retail, and government).
14.
C. New Services

Switching-as-a-Service
Problem:
Consumers and businesses can see the beneﬁts of switching
providers across many sectors. Though the introduction of the CDR
removes much of the customer inertia, it still requires customers to
initiate or agree to switching providers. Switching still requires
thought and effort for a consumer to look for the best deal and
actually switch providers.
Solution:
When Write Access is introduced to the CDR, switching should
become absolutely seamless between providers. This should allow
permissions to switch across multiple providers, with the appropriate
consent. This brings a true marketplace model to life with a plethora
of offered products and a transparent pricing comparison with
data-driven decision making. This could be implemented by either a
new technology company or an existing provider.
Example:
Switch Me Now, a new telecommunications company, creates a
subscription model business where customers consent and give
authority to the company to switch providers on their behalf. For a
small fee per month, Switch Me Now will switch the customer to
the best rate available in the market, handling all account opening,
administration, and price searching. The subscription fee more than
pays for itself and the customer is comfortable knowing they have
the best price for their desired product set.
Relevant for all sectors that create product recommendations and
provide data-driven services (e.g. banking, telecommunications,
telecommunications, energy, ﬁnancial technology, and
comparisons).
15.
C. New Services

Subscription Management
Problem:
How many times do you look at your bank statement and see those
apps with monthly fees you never use, that gym membership you
cannot be bothered to cancel, and extra streaming service you do not
use? It might be front of mind for a few minutes to cancel, but often
it does not happen. This results in Australians spending more than
they intend to on services they do not want, only because they forget
to cancel.
Solution:
The introduction of CDR Write Access should enable ADRs to initiate
payments and open and close accounts. This would enable a new
type of service to consumers for a third party to manage
subscriptions on a consumer’s behalf. When a consumer shares their
information to provide a full transactional view, this enables total
control to manage providers in one centralised location.
Example:
Chris always loves to try new services with a trial period. Since he
always forgets to cancel services once the trial ends, he has a lot of
subscriptions, from media streaming to PDF fillers. He enrols in a
subscription management service that has ongoing access to his
CDR Data for his transaction account. It analyses all of his direct
debits and recurring payments to create a list of all subscriptions. In
the subscription management app, Chris can toggle the
subscriptions and services on and off instructing the account closure
or pausing. It also proactively nudges him and schedules
cancellations based on payment calendars.
Relevant for all sectors that help customers manage finances and
provide data-driven services (e.g. banking, payments, financial advice,
wealth management, and comparisons sites) and any new or existing
companies wanting to pivot to subscription management.
16.
C. New Services

Data Recipients can provide much smarter experiences to their
customers. In these use cases, the CDR drives innovation in analytics.
Use cases in this category benefit from the ability to access additional
data. With the ingestion of CDR Data, a plethora of customer- and
business analyses can be improved. These improvements are
dependent on the maturity of Data Recipients regarding their
analytical capabilities and the ability to learn from data. Consumers
mainly benefit from better services, tailored to their specific situation
and needs. Data Recipients benefit from truly actionable datasets,
ability to personalise offerings and improved predictive modeling
capabilities.
Actionable Datasets:
CDR Data generates datasets which Data Recipients and consumers
can instantly use. Through its characteristics, the datasets can show
clear benefits even before any additional analyses are conducted.
CDR data is accompanied by customer consent. This clearly lays out
how organisations can use the data. The CDR ensures the right data
ethics practices that so often are not yet followed. CDR Data is also
formatted according to standards and hived off in a separate
environment. A structured dataset that is much easier to work with
compared to original data kept in seperate legacy systems within an
organisation. CDR data, and in particular transaction data can directly
be used as triggers to take a specific action. The only capability
required to benefit from these characteristics is to be able to access
CDR data as an ADR.
Personalised Offerings:
With CDR Data, ADRs can understand the customer context better. A
more holistic view of the customer can be created and the
understanding of customer need improved. When analysed over
Smarter

time, changes in customer context can be found as well. When
combining this with knowledge on available products, customers can
be proactively supported to select the one that will best address their
needs. Also the pricing can be personalised or the right time to
suggest new offers. These types of use cases require mature
customer analytics- and automated product recommendation
capabilities.
Improved Predictions:
With CDR data, any type of predictive modelling can be improved. In
particular transaction data can be used to derive a plethora of
insights. Transactions are breadcrumbs left by the consumer after a
buying decision they made. Organisations that collect these
breadcrumbs can ﬁnd out why the consumer made this decision and
predict what next need they might want to fulﬁll. Personalised
predictions can help inform customers on the next best action to
take, or how to change behaviour. The customer can be nudged to
make the right decision, personalised to their context. These types of
use cases require mature predictive modelling and real-time decision
capabilities, but can have a big impact on revenue and customer
satisfaction.
Smarter

Consented Usage
Problem:
Although consumers own their data, it is opaque how organisations
use it. Some organisations sell consumer data without benefiting the
customer or even asking for their consent. This introduces inequality
into data sharing with more control and power to the organisation
instead of the consumer.
Solution:
When consumers consent for an ADR to receive their CDR Data,
Customers have the right to specify whether their data should be
deleted or de-identified by the ADR upon completion or expiry for the
purpose they consented to. Should the customer select
deidentification of CDR Data, this allows an ADR to store the data
when they can assure the customer can not be identified through it.
Example:
Lyndsey gave consent to sharing her transaction data with ABC
Travel to give her personalised offers and better pricing. She is
willing to let them keep her data after its usage as long as it is
de-identified. ABC Travel grows their customer datasets that have
been procured with Lyndsey’s full knowledge and disclosure.
Relevant for all sectors that want to use consumer data included in
CDR for customer- and business analysis.
17.
D. Actionable Data

Transaction Triggers
Problem:
Data is valuable, in particular transaction data. Traditionally only
banks and payment providers have access to this. However, the lion’s
share of the profit of these businesses are not actually derived from
this data. They are not intrinsically motivated to unlock this value
and hence innovate on such services at a low rate. Customers miss
out on additional solutions to their needs and untapped business
opportunities exist.
Solution:
New players who choose to become ADRs can access transaction
data upon customer’s consent. They can use this to provide insights
to consumers and use specific transactions as triggers for their
services. Any If-This-Then-That algorithms can be used to provide
innovative services to consumers.
Example:
Yin has heard about sustainable investments, but finds it too risky
to directly invest in stocks with large amounts of money. He also
wants to teach his kids some important lessons around
sustainability and good investment practices. He has found an app
that allows him to select what types of micro shares to buy, given
specific triggers. Yin wants to motivate his family members to take
public transportation over the car. Whenever they use their debit
card to take public transportation, they receive micro-shares in
sustainable companies.
Relevant for all sectors that can use transaction data to trigger their
service.
18.
D. Actionable Data

Problem:
Introductory deals exist to reel in new customers. As opposed to
rewarding loyal customers, this approach actually benefits the ones
that are not loyal. Many customers are not aware products with a
better value are available and only savvy customers will actively hunt
and negotiate for deals.
Solution:
A customer can consent to share his total digital wallet with an ADR.
With this information, the ADR can compare their product offerings
to the customers’ current wallet. Organisations can present the
customer with a competing offer, that would fit them better or even
make personalised pricing decisions. This will increase pricing
competition across the ecosystem.
Example:
Andrew has term deposits with multiple banks. He used to
compare rates through web searches whenever he needed to
renew. He can now share his deposit balances and product
information with an ADR that will offer him the highest rate if he
switches the combined balance of funds over to them. The ADR
receives a higher share-of-wallet, and Andrew receives the most
competitive rate.
Relevant for all sectors that provide products that are heavily
dependent on pricing (e.g. banking, comparisons, foreign exchange,
energy, telecommunications, loan broking, and financial planning).
19.

Peer Comparisons
Problem:
It is hard for customers to understand how they are doing with their
ﬁnances. Income is not often discussed and ﬁnancial products like
insurance not a popular topic in the pub. Information on how they
compare and how other people in similar situations make decisions
can be helpful.
Solution:
Peer comparison itself is not new, but with CDR data, it can become
much more common. ADRs who previously did not have access to the
most important indicators (like salary or transaction behaviour), have
the opportunity to access this. Also with a larger number of variables,
contextual knowledge and hence recommendations can be
improved.
Example:
Mary is upgrading from a studio apartment to a three-bedroom
home. She thinks she might have to change her home and contents
insurance. As Mary enters her new address for a quote, the insurance
company asks for Mary’s consent to share more data so they can give
her a broader check up compared to peers. Mary is a bit hesitant
and only shares her current insurance data. As the ADR presents the
quote, which is appropriate for people like Mary who own a similar
property, they also mention that her car insurance might be reduced
as the home has a garage. Pleasantly surprised by this tip, Mary
continues and shares her current car insurance. People like Mary
who own a similar car, go on a relatively high number of trips and
the ADR suggests a credit card with travel insurance offered through
a partnership. If appropriate, they might even let Mary know that
20.

people like her usually look for healthinsurance with pregnancy
cover. Mary is very content with the insurer who realises her needs
before she does and is conﬁdent she continues to be covered
appropriately.
Relevant for all sectors for which customer context can be compared
to other sets of the population to inform product offerings (e.g.
insurance, superannuation, wealth management, banking, and
ﬁnancial planning).

Changing Needs
Problem:
Decisions about products, limits, and amounts are taken at a point in
time and are not often revisited, if ever. As circumstances change
and life events inﬂuence customer needs, customers might have to
reassess their products. Customers often forget to do so and end up
having products that are not suitable to their needs.
Solution:
When receiving additional CDR Data, ADRs can more accurately
identify that circumstances have changed for their customers. They
can proactively inform their customers that their products may not
be the most suitable option for them and suggest to change. This
will increase customer satisfaction with proactive suggestions, and
the company will have prevented a customer from assessing other
options or moving to a different provider.
Example:
Mike and Terry have signed up to AC Capital’s Life Concierge. AC
Capital manages their investments and insurances. The Life
Concierge product bundles their products, gives them reduced
rates and ensures their portfolio of products stays up to date to
their needs. Mike and Terry share their CDR Data, as part of the Life
Concierge product. Mike and Terry recently retired and plan to
travel around Australia. AC Capital recognises that their salaries
have stopped and a new motorhome acquired. They assume their
changed needs and proactively recommend more conservative
investment strategies, and appropriate insurance. Mike and Terry
are grateful they received personalised recommendations that
align to their changed life.
Relevant for all sectors where a life change is likely to trigger
different customer needs (e.g. banking, wealth management,
superannuation, ﬁnancial advice, energy, telecommunications,
insurance, retail, and government).
21.

Problem:
Customers increasingly demand convenience and expect a great
experience. They expect suitable products and services to be offered
to them when they need it. Offers that are random and not tailored
to their preferences are ineffective and can cause dissatisfaction.
Solution:
CDR data can enable businesses to analyse customer transactions
and look for behavioural patterns. The context in which a buying
decision is likely can be found and triggers identiﬁed. If an ADR can
monitor consumers’ transactions, in exchange for discounts or other
rewards, it can identify the right time to target customers for their
product or service. The customer beneﬁts from personalised offers
matching their needs, integrated into one seamless experience.
Example:
A rideshare company knows that their customers are likely to
require their services after a night out. As Camilla is sharing her CDR
data for discounts, they learn that she usually orders a taxi after
having paid in a restaurant. Whenever Camilla dines out, the
rideshare company is alerted to this and sends her an offer. Camilla
is happy to share her data since she gets a better experience and
better deals.
Relevant for all sectors for which buying behaviour can be derived
from transaction data (e.g. transport, hospitality, eCommerce, travel,
and retail).
22.

Credit Assessments
Problem:
Traditional credit assessments are based on credit scores and/or
credit scorecards. Decisions are made based on credit history, basic
profiling, and human judgement. This paints a limited picture of a
customer’s ability to repay their loan and may include biases in
pricing and eligibility. A tricky financial situation may just be
triggered by misfortune rather than consistent bad financial
behaviours.
Solution:
With CDR data, credit decisions can be based on more historical data
and a larger number of variables. Organisations can use up to seven
years of historical data, where previously only a few months’ worth of
data was used. Basic transaction data can give insight into spending
patterns and consistency of savings behaviour. This is a much better
indicator of a customer’s ability to repay compared to their current
financial situation. Credit pricing decisions will be fairer for
customers and organisations will be able to assess their risk better.
Example:
Leanne never had any financial issues; she saved consistently and
was careful to spend her money wisely. She had selected the
schools for her children carefully. They were not cheap. Due to the
coronavirus outbreak, Leanne’s income was reduced. Knowing that
she would be able to repay the loan once Australia’s economy
would pick up again, she searched for a personal loan in which the
credit decision would take her spending behaviours into account.
She was able to get a loan with low interest rate as the bank knew
the risk of her defaulting was low.
Relevant for all sectors that conduct credit assessments (e.g.
banking, non-bank lending, and loan broking).
F. Accurate Predictions
23.

Outcome Prediction
Problem:
When customers take up a product they expect an outcome. In
financial services, customers expect their savings to grow or to pay
off a loan over time. While the customer is using the product,
unexpected events might happen with customers’ behaviour
influencing the outcome. Customers might be unpleasantly
surprised by lower returns, defaults, or even hardships. A similar
analogy holds for other sectors, where customers might be surprised
by unexpected fees, surges in bills, or even termination of services.
Solution:
CDR can provide a whole-of-wallet view. A customer’s financial
situation and overall spending patterns can be tracked in real time.
With the additional data, results can be extrapolated into the future.
Outcomes can be predicted more accurately and potential
hardships or other difficulties can be identified earlier. These
additional insights inform customers early of future costs or
expected benefits.
Example:
Michele has a personal loan with an alternative lender that collects
loan repayments via direct debits. The only indicator of Michele’s
financial health used to be the success rate of the direct debit. The
lender has now become an ADR. Michele consents to share her
financial position. The lender assesses her actual overall financial
health in real time and can predict when there may be any signs of
trouble. This prevents Michele from defaulting or getting into
hardship as the lender can change her payment plan early for a
mutually beneficial outcome.
Relevant for all sectors where the likely product outcome can be
modelled (e.g. banking, non-bank lending, telecommunications,
utilities, government, and non-for-profit).
24.

Behaviuor Prediction
Problem:
Often, a product does not guarantee the outcome a customer is looking for.
The customer’s behaviour actually influences the likelihood to a successful
outcome in a major way. A savings account does not help you save, but the
habit of squirreling away money does. However, it is hard to stay on track
with any new habit. Resolutions can be forgotten and people might simply
give up.
Solution:
Through additional customer data, in particular, transaction data or a
whole-of-wallet view, Data Recipients can dive deeper in customer
behaviour. They can assess whether a customer displays the right behaviour
to get to the intended outcome, or whether it needs changing. ADRs can
provide services where customers can decide to block their bad decisions
and limit certain behaviours. In addition, rightly timed and delivered nudges
can help customers stay the course. In this way customers will be supported
to take the shortest path to the desired outcome, while staying in control of
their actions.
Example:
Simon has debts and bank accounts with multiple providers and feels
helpless, as his finances are out of control. He wants to change his
spending habits and finds AusBudget, a company that helps him stay on
track without having to do a detailed budget. With a total view of all of his
finances, transactions, and income, AusBudget recommends certain
boundaries to help him get out of debt. He consolidates his debts, sets
limits on certain transaction types, and chooses the type of nudges he
would like to achieve to stay on track. AusBudget knows that customers
similar to Simon stay motivated through taking small challenges.
AusBudget actively helps Simon to change his financial habits and get out
of debt sooner.
Relevant for all sectors where the likely product outcome can be modelled
and customer decisions influence the likelihood of getting to the outcome
(e.g. government, non-for-profit, banking, non-bank lending, and financial
technology).
25.

Use Cases By Sector
Sectors
Banking
Wealth&Super
Insurance
FinancialAdvice
Comparisons
Payments
Telecommunications
Energy
Travel
Education
Government
Retail
Hospitality
Wholesale
RealEstate
Other
SmootherUseCases
Customer Onboarding
Financial Verification
Account Verification
Customer Identification
Competitive Pricing
Product Recommendations
Payee Switching
Customer Loyalty
Customer Referrals
Streamlined Invoicing
Reduced Rejections
Account-To-Account Payments
Optimised Budgeting
Information Updates
Switching as a Service
Subscription Management
SmarterUseCases
Consented Usage
Transaction Triggers
Peer Comparisons
Changing Needs
Credit Assessments
Outcome Prediction
Behaviour Prediction

The CDR outlines the rules for which sectors have to share consumer
data. The banking industry has been designated as the ﬁrst sector to
which the CDR obligations apply, followed by the energy and
telecommunications sectors. Other industries are expected to be
obliged to share data thereafter. The Australian Competition and
Consumer Commission (ACCC) has outlined a phasing schedule for
what type of data should be shared and by when, generally starting
with more simple data and entity types, which increase in complexity
over time.
Organisations that hold consumer data are referred to as Data
Holders, and as the industries for Data Holders are designated, they
need to comply with data sharing obligations. They need to allow
consumers to share their data with accredited third parties, which are
referred to as Accredited Data Recipients (ADRs). This ensures that
consumers know what data is shared, with whom, for how long, and
for what purpose.
Being an ADR is optional. Any organising aspiring to be an ADR must
become accredited by meeting speciﬁc technical, business, security
standards and rules, and customer experience guidelines. Any
organisation that meets the accreditation criteria can be an ADR,
regardless of industry. One of the challenges is that the CDR
standards, rules and customer experience guidelines evolve over
time. This requires an organisation to have ongoing resources
dedicated to aligning to the changing standards or engage an Open
Banking platform to help future-proof compliance.
AboutThe Consumer Data Right

Deﬁned Terms:
Consumer Data Right (CDR):
The right of consumers to have control, access and share their
data. The Australian Government has legislated this right and is
implementing it sector-by-sector.
Open Banking:
The application of the CDR to the banking sector.
Data Holder (DH):
An organisation collecting and holding data held related to
consumers to which the CDR will apply. They are obligated to
share this data on consumer’s request.
Accredited Data Recipient (ADR):
An organisation that has met the accreditation criteria set by the
ACCC and accredited is hence allowed to receive CDR Data in
the Consumer Data Right regime.
Australian Competition and Consumer Commission (ACCC):
The co-regulator for the CDR regime with the ofﬁce of the
Australian information commissioner.
Read Access:
The ability to access and receive information about a consumer.
Data can be viewed but not changed by the Data Recipient.
Write Access:
The ability to access and change information about a consumer.
At the time of writing, this is not (yet) included in the CDR
legislation. Write Access could extend to payment initiation and
updating customer information.
Consumer:
Individuals and businesses that have the right to share their data
that is held by Data Holders with Accredited Data Recipients.
CDR Data:
The types of data that Data Holders are obligated to share with
Accredited Data Recipients. Data types include product,
customer, account, and transaction data. The types of data are
also referred to as data clusters.

Adatree Provides Australia’s First Data Recipient Platform:
Adatree is a CDR-focused regtech. We believe in a world where data
is democratised and leads to better consumer outcomes and
experiences.
Adatree Removes Barriers To Entry:
Adatree removes barriers for organisations seeking to participate in
the CDR ecosystem through our modular Data Recipient technology
platform. Adatree helps Data Holders comply and Data Recipients
compete, enabling organisations to send and receive data in line with
the latest CDR standards and rules. Organisations can focus on
developing their customer propositions and use cases, while Adatree
ensures they conform to the constantly evolving and challenging
technical standards.
Adatree Offers Out-Of-The-Box CDR Solutions for ADRs:
Adatree’s modular Data Recipient Platform is the ﬁrst platform
created for Data Recipients in the Australian market. Adatree offers
three out-of-the-box solutions:
The Adatree Team Is Experienced In Regulated Australian
Environments:
The Adatree team has built start-up banks in Australia. They
understand the high standards of building technology in regulated
environments. The team has a deep understanding of technical and
regulatory CDR requirements and can help organisations explore and
deliver on the use cases that are most beneﬁcial to their customers.
AdatreeOpen Banking Solutions

Open Banking Data Sandbox:
The Open Banking Data Sandbox enables organisations to explore
the data included in the CDR, which would become available to them
as an ADR. With one simple API, they can access CDR-conformant
datasets and assess if the data types and formats meet their needs.
Open Banking Industry Sandbox:
The Open Banking Industry Sandbox enables organisations to
explore a full ADR experience. It is an implementation of the full Data
Recipient Platform with mock CDR infrastructure, CDR conformant
datasets and Data Holders. Aspiring organisations can test their use
cases and propositions, before deployment in production.
Data Recipient Platform:
The Adatree platform has all of the technical components needed for
ADRs to participate in the CDR ecosystem. This includes the Consent
API, customisable Consent Dashboard, customer notiﬁcations and all
other required APIs. It is easy to migrate from the Industry Sandbox
to production.
AdatreeOpen Banking Solutions
1.
2.
3.

Whatever the use case or industry, Adatree’s Data Recipient platform
removes barriers to being part of the Open Banking ecosystem;
Adatree solves technical needs for organisations looking to explore or
be part of the CDR regime. Businesses can focus on customer
propositions while Adatree will take care of technical and security
conformance, both current standards and any future changes, with its
modular ‘plug and play’ Data Recipient platform.
Whatever your use cases are, Adatree can help bring them to life.
I Have My Use Case - How Do I Bring It To Life?
The Open Banking Journey

The primary purpose of this paper written by Adatree Pty Ltd (ABN 94 636 766 320)
(Adatree) is to provide potential participants in the Consumer Data Right ecosystem with
ideas and pertinent information in order for them to consider the impacts and
opportunities of the Consumer Data Right. This paper is for general information purposes
only, and information shared in this paper is not all-encompassing.
Adatree advocates a careful review of the Consumer Data Right rules and standards. Certain
statements, ideas, scenarios and examples featured in this paper are forward-looking
statements that are based on and take into consideration certain known and unknown
rules, standards, capabilities, and information.
Any examples are meant to be explanatory in nature and do not represent any actual
businesses. Any similarity to businesses, models or use cases is entirely coincidental and
unintentional.
The Adatree Data Recipient platform does not guarantee accreditation as an Accredited
Data Recipient. The Adatree Data Recipient platform and this paper are not endorsed by
the ACCC or any other Regulator. Adatree, its directors and employees makes no warranties
or representations as to the successful development or implementation of Consumer Data
Right use cases. Adatree disclaims all liability for any loss or damage of whatsoever kind
(whether foreseeable or not or whether caused by negligence) which may arise from
anyone acting on any information and opinions relating to the Adatree Data Recipient
Platform contained in this paper, other information contained in this paper or any
information which is made available in connection with any further enquiries.
The information contained in this paper is provided in good faith, but no warranties or
guarantees, representations are made by Adatree with regard to the accuracy,
completeness or suitability of the information presented. Some of the use cases require
other licenses and accreditation, including but not limited to, an Australian Financial
Services Licence or Australian Credit Licence.
Adatree does not have any obligation to amend, modify or update this paper or to otherwise
notify a reader or recipient thereof in the event that any matter stated herein, or any
opinion, projection, forecast or estimate set forth herein, changes or subsequently becomes
inaccurate.
This paper is conﬁdential and is only available through www.adatree.com.au. The paper
may not be redistributed, reproduced or passed on to any other entity or person or
published, in part or in whole, for any purpose, without the prior written consent of Adatree.
Disclaimer:

Let’s Drive
Better Consumer Outcomes,
Together
(02) 8005 1826
hello@adatree.com.au

25 Ways the Consumer Data Right Can Create Smoother and Smarter Customer Experiences

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