The science of money: how data controls your financial life
Whether you know it or not, there's a new currency that underpins your financial life.
Source: Paul Wallbank
These days everything from how you earn and spend money, not to mention the tax you pay on it, is driven by data.
The Centrelink debt notices sent to tens of thousands of Australians just before Christmas highlights the use of "big data" – the practice of overlaying and analysing data from multiple sources. Yet it's not just benefits recipients having their information tracked, as marketers, banks, retailers and the tax office match various sources of public and private data on all of us.
Big data is also difficult to get right – that so many Centrelink debt notices contained numerous errors highlights what can go wrong. Data company Experian estimates as much as a third of the information companies hold on their customers could be inaccurate.
Despite the drawbacks, big data is being increasingly used by government agencies and private companies to understand more about us. As artificial intelligence develops, we'll see computers increasingly deciding whether we should have our tax returns reviewed, insurance premiums increased or get an offer for a discount at our local pizza shop.
In the government sector the Tax Office is the most aggressive user of data matching, although many of the state collection agencies, such as the NSW Office of State Revenue, also keep a close eye on people's behaviour.
"Initially data matching in the ATO was a manual activity," an agency spokesman told Fairfax Media. "From 1936 onwards, when the ATO first received statements of interest from the banks, it was able to check if individual tax payers correctly reported interest earnings. During the 1970s the ATO moved to computer-based data-matching systems."
Today the Tax Office accesses a huge amount for data from other agencies such as Customs, the financial intelligence agency Australian Transaction Reports and Analysis Centre (AUSTRAC), state government departments such as motor registries, private credit agencies, banks, and online marketplaces such as eBay.
Even when they don't have direct access to information about individuals, the Tax Office can use general data to identify problem areas, such as Uber drivers not reporting income.    "We obtain data from financial institutions to identify ride-sourcing drivers to help them meet their registration, lodgement, reporting and payment obligations," the spokesman says.
The Tax Office doesn't just see data matching as being a net for errant tax avoiders. The office also uses it to help taxpayers navigate the complexities of the tax system, for example with its pre-fill function for the online forms.
One of the biggest users of data-matching techniques is the insurance industry, which pulls in information from many disparate sources to decide what premiums to charge.
Home insurance providers can access the National Flood Information Database to determine flood risk to an individual address level, look at bushfire maps produced by local governments, or data held by the Bureau of Meteorology to shed light on a property's exposure to storms or cyclones.
However, Campbell Fuller, general manager communications for the Insurance Council of Australia, says data matching is only one tool used by investigators to identify fraud, and is rarely sufficient in isolation.
While insurers have always charged different premiums according to the customer's risk profile, banks have more typically lent money at one rate for all customers. However, that's starting to change with the rise of online marketing places for personal loans, such as Ratesetter, Society One and MoneyPlace.
These companies are setting themselves apart from the banks by charging risk-based pricing, where people who are a good credit risk will get a better deal than those considered more likely to default by analysing broader demographic trends as well as individuals' spending and income patterns.
For investors, the new peer-to-peer lending platforms also use data-matching technologies to bring together lenders and borrowers with compatible risk profiles.
Loyalty cards have been a powerful tool for the marketing industry.
In the For Love or Money 2016 survey last year The Loyalty Point found nearly two out of three consumers were happy to receive discounts or special offers based on their purchasing habits, while just one in two were happy to give their personal details to loyalty card providers.
That loyalty card information is essential for data collection companies such as Dublin-based Experian, the world's biggest company in the sector. Andrew Black, Experian's managing director for data quality and targeting, says consumers benefit from data insights as well as marketers. "The most obvious benefit to consumer is that the communications they receive from businesses are correct, timely and relevant," Black says.
Protecting your data
Ensuring data-matching services comply with the Privacy Act is the Office of the Australian Information Commissioner, which oversees government agencies and business.
The office has issued voluntary guidelines for government agencies, and encourages government agencies and businesses to conduct a privacy impact statement to assess privacy risks.
The Loyalty Point chief executive Adam Posner warns data needs to be kept up to date.
"Data hygiene and privacy compliance is an ongoing requirement and people are changing their lives regularly – moving, marriage, death etc and if data is to be relevant and personal, it needs to be continually maintained and cleaned," Posner says. "The risks with hyper-personalised data is the potential to move from cool to creepy and being sensitive to where, when and how often brands use the data to be relevant in the daily lives of their members."