A move to e-invoicing could potentially transform the economy to the tune of $7-$10 billion a year according to the Federal Government.
e-invoicing means the automated exchange and processing of invoice-related documents between suppliers and buyers in a structured electronic or digital format. The ATO has estimated that only about 10-15% of invoices sent in Australia currently use e-invoicing, noting there are currently around 500-800 different ERP/accounting systems in use in Australia.
About one billion invoices are sent between Australian businesses each year, compared with about 130 million superannuation contribution messages sent using SuperStream.
Following the Budget, the Government commenced a joint private-public sector detailed cost and benefit study on widespread Government adoption. The ATO has estimated that there could be savings of $3 billion for government agencies alone.
Following on from this, the joint private-public sector council has approved a framework for implementation of national standards for the processing of e-invoices. The framework prescribes the use of universal business language (UBL) XML specification for messaging and eBMS 3.0 AS4 standard for transport. This is the Standard required by SuperStream for employers to make superannuation contributions using e-commerce.
What’s more, the implementation model currently proposes a “4 corner model” whereby invoices are sent by the seller to the receiver (buyer) via sending and receiving gateways. This structure and associated technical details are being looked at by a technical working group.
The successful implementation of SuperStream has been a driving factor in this area, and it is noteworthy that the ATO has been a key participant in this process as well. Gateways and other organisations with SuperStream expertise and capabilities are also involved in the process, and they will be able to leverage off their SuperStream learnings.
The small business community, as represented through the Council of Small Business Australia (CoSBA), were at best ambivalent to the introduction of SuperStream – although most small businesses are now SuperStream compliant. However, they are far more enthusiastic about e-invoicing.
Peter Strong, who is both the CEO of CoSBA and Chair of the Digital Business Council, can be quoted on its website : ‘eInvoicing is a transformational step in Australia’s digital business movement to a streamlined, integrated and productive economy, and is an exciting step forward especially for the small business community in Australia.’
This week’s blog is a re-published post from The Conversation, an independent source of news and views, sourced from the academic and research community. Today’s blog takes a look at “What if Intelligent Machines could learn from each other?” In this age of ‘robo’ it’s food for thought. Here is what Raja Jurdak, Research Group Leader, Distributed Sensing Systems, CSIRO believes we could expect.
Take a look around and you’ll see evidence of the widespread adoption of wearable sensors for health and fitness, such as the Fitbit, Garmin or other devices.
With the rapid growth of the Internet of Things (IoT), tens of billions of sensor devices are projected to connect in the next decade. These connected sensor devices will automate processes across a broad range of economic sectors, from industrial plants to healthcare management, delivering productivity gains and hopefully quality-of-life improvements.
The core of these sensor devices that will be deployed across this broad range of applications is largely the same, featuring a microprocessor, memory and a wired or wireless communication interface to the internet, along with a battery or other energy source.
Each application and IoT device will bring its own unique context, such as its location, the conditions of the surrounding environment and the behaviour of people in the area. Individual devices will observe and adapt to their unique contexts.
Enter artificial intelligence
So what happens when we introduce artificial intelligence (AI) into the mix? With AI, these devices can evolve their behaviour in response to changing contexts. Just like how living beings optimise their behaviour to their surroundings, even smaller IoT devices around us can run AI machines that evolve their software over time.
Consider a portable mobile device, such as a smartwatch or a smartphone, that typically ships in large volumes with one-size-fits-all features and apps for all users.
To personalise them, users have to manually configure each app individually, and keep updating these configurations as their preferences change over time.
What if the device itself could learn our preferences, simply by observing our usage patterns? This could help automate the personalisation process.
What about situations that our device has not yet experienced? Is it possible for this device to learn what our preferences could be in an unknown situation?
This is where AI machines can help each other learn faster, effectively by sharing information from each other’s experience, resulting in a multiplier effect for how quickly these devices can learn.
As an example, we have demonstrated how smartphones that are in proximity to each other can both run their own AI machines and share logic blocks from their programs to accelerate learning how to maintain battery life.
There are two reasons behind these benefits. First, each phone learns independently, developing its own genetic material of program logic – an evolution of sorts.
This is known as the “island model” in evolutionary computing. In the IoT, each device becomes its own “island”. Occasionally, the devices share what they’ve learned.
This adds to the diversity of their genetic pool, which can be beneficial in a system that learns or evolves. It also means that both devices know how to react better to new contexts that may have originally been observed by other collaborating devices.
Animal tracking provides a similar driver from collaborative AI among IoT devices. Devices are frequently placed on collars or ear tags to track the position and activities of livestock, pets, or wildlife.
In order to deliver accurate tracking information, each device needs to learn the specific movement features of the animal it is tracking – such as the species, age and gender – which AI can help with.
Then, when two or more animals meet, the IoT devices can share what they’ve learned about their animal’s movement, which can speed up the learning process for other devices on animals with similar features.
In many instances, these devices would not have a communication link to the internet due to cost and remoteness, but they can gather information locally and learn the specific patterns in observed sensor data that may predict faults.
Because faults are relatively rare, shared learning with neighbouring devices provides a larger pool for training IoT devices that may have not yet encountered a fault, on what to look out for.
Some open questions remain on the road to making shared IoT device learning a reality. Does a device compromise the privacy of its owner if it participates in a shared learning environment? The answer is it depends on whether the AI approach shares information that has intrinsic meaning or not, such as in genetic programming.
An IoT device also needs to ensure that it continues to deliver on its day-to-day tasks as it learns how to respond to new situations. Appropriate safety controls would need to be devised, such as placing hard constraints on what a device can learn and what should not change in response to learning.
Another question is how does a device know which neighbouring devices to trust when deciding which ones to collaborate with? What if a malicious entity enters a network with the aim of injecting disruptive logic into a shared IoT learning environment? Methods still need to be created to fully address these issues.
So where are we headed with IoT devices that can potentially learn from each other? While their applications are still considered to be in their infancy, the potential opportunities warrant attention, debate and investigation.
There’s a potential disconnect between how governments of all persuasions say they’re going to implement change to super and how they actually go about it. Simple, with time to consult is their theory; potentially complicated, with possibly shorter consultation time is their practice. What the Government is doing with the Budget super measures is certainly food for thought.
The Government accepted the recommendation of the 2015 Final System Inquiry to give industry appropriate time to implement regulatory change however this may not be the reality for this round of changes.
The shorter time than expected the super industry is being given to implement complex regulatory change may lead to a range of unplanned challenges, including the risk of increased costs, complex outcomes and unanticipated speed bumps – and this may mean less than optimal outcomes for Australian super fund members.
Immediately after the Federal Budget, the Government went to the election taking a raft of superannuation policies with them; with super having a high focus throughout the campaign.
Post-election legislation action
After the Federal Election, the re-elected Government undertook the process of negotiating with interested parties, resulting in a staggered release of draft legislation.
The first stage was released in early September, and covered:
the legislated objective of superannuation;
extending tax deductions for personal superannuation contributions,
extending low income spouse contributions to spouses earning up to $37,000pa (phasing out at $40,000);
introducing a Low Income Superannuation Tax Offset (LISTO) to replace the Low Income Superannuation Contribution (LISC); and
Removing the work test for those aged 65-74.
However, following negotiations with the backbench, there will be no changes to the work test for those aged 65-74, and is delaying the catch up provisions (see below) until 1 July 2018.
In an unusual scenario, the Government only gave eight days for the consultation on this package of legislation.
Second stage of legislation
The second stage was released in late September, and covered:
the introduction of a $1.6 million transfer balance cap and transitional arrangements for individuals who already have retirement phase balances above $1.6 million;
lowering the high-earners extra contributions tax (Division 293) income threshold to $250,000 and reducing the concessional contributions cap to $25,000);
allowing catch-up concessional contributions for those with balances less than $500,000;
removing regulatory barriers to innovation in the creation of retirement income stream products;
introducing earnings tax for transition to retirement income streams; and
Removing the anti-detriment provision.
The nine day consultation period on this has just closed, with the Government providing draft regulations to accompany the draft legislation. This is not something to celebrate because the explanation of the $1.6 million transfer balance cap alone takes 64 pages, and that’s followed by 37 pages of associated draft legislation. The complexity of the legislation will be reflected in the complexity of its implementation.
The third, and probably final, stage will cover the new $100,000 annual after-tax contributions cap that replaces the previously announced – but now withdrawn – $500,000 lifetime cap. Draft legislation on this is expected in the next fortnight.
The Government intends to introduce all of this legislation to Parliament during the current session, that is, 1 December, and hopes to get it to the Senate by then. Almost all of the legislation is scheduled to come into effect from 1 July 2017.
Given that the legislation also has to pass through the Senate, may be referred to a Senate committee, and the positions of the ALP, Greens and many Senate cross-benchers may challenge these bills; the time frames are potentially becoming very tight, and may pose a challenge for the industry, software developers and the ATO to successfully implement process and system changes.
In a previous blog, I asked if the Productivity Commission had underplayed the importance of cost efficiency in superannuation in its review of competitiveness and efficiency in superannuation.
IQ Group didn’t just leave it there, but has written a submission to the Productivity Commission showing how back office efficiency and cost efficiency can be given a higher priority in assessing the efficiency of superannuation using a simple model.
Measure more but keep it simple
The Productivity Commission identified 84 items of evidence required for the system assessment, and concludes that 43 of these items require new data sets. We think this both overestimates the usability of the 41 existing items and underestimates the administrative burden and the design and consistency challenges in gathering the 43 new data sets.
IQ Group has suggested focusing on a small number of basic efficiency measures, and proposed basic indicators for cost efficient superannuation administration. The core elements for administration are record-keeping and member details, contribution processing, and rollovers and benefits. The efficiency of these can be assessed by their cost, timeliness, accuracy, and usefulness to a member.
This may seem basic but this information is not collected, collated or assessed on a standardised basis across the industry. A lot of information is collected through APRA data reporting but not this information.
Despite the introduction of the three day transaction processing requirement through SuperStream, there are widely differing levels of transactional performance in superannuation, and this is likely to continue – and measurement of SuperStream efficiency itself is not standardised.
A framework for cost minimisation
We proposed having system-wide benchmarks together with a comprehensive and focused set of functional level benchmarks identified above (eg, timeliness of benefit payments).
This model can built from a baseline understanding of existing best practice examples from throughout the superannuation industry, and across administration.
As a starting point, IQ Group suggested to the Productivity Commission a model based on:
1. Consulting with superannuation industry for the collection of this administrative information on a consistent and cost-effective basis for super funds and their products;
2. Introducing a requirement for this over a reasonable period;
3. building a best practice administration measurement framework based on this information;
4. Populating information collected into the framework, using a simple rating;
5. Including assessment against the best practice administration framework as part of the APRA prudential supervision process;
Not only will this potentially help drive cost efficiency and cost minimisation, it would also contribute to promoting competition, as funds could ideally promote their ‘best run’ status.
Understanding the cost of different types of superannuation products
The use of such a potential framework and standard measurement would allow consumers to see that one of the most efficient levels for service provision may be on a mass market basis, but will also allow them to see the premium cost for tailoring to meet their particular needs.
The framework could be developed for each of main superannuation product types, to avoid the risk of just setting a lowest common denominator. This would also give an informed retail consumer the opportunity to better understand the reason for differences between products and product types, and whether or not it is sufficient to justify any additional cost.
This approach has the value of potentially greatly increasing fee and cost transparency in superannuation, and could have the systemic benefit of encouraging providers to concentrate on cost minimisation just as much as return maximisation.
The Productivity Commission report is also already making a positive contribution to the debate about the efficiency of superannuation, and IQ Group looks forward to being part of that debate.