There are three main reasons why people share their data with organisations.
The first is proximity of purpose: they’re in the moment and need to share data at that point in time. They want to access that service there and then – that is their motivation to share. Experian research looks very closely at the time and the point of the application that organisations ask people to share their data, because it can have a huge effect on their state of mind and willingness to share information.
The second is day-to-day value. An example is using PayPal: you share your data with PayPal to pay for something online. This transition is something that’s part of our daily needs; our daily behaviours.
The third level is of obligation. In the case of telematics in insurance, the conditions of the exchange have already been established: you accept that you must share your data to get access to the service, such as insurance. Issues of trust and privacy become fundamentally important in this and contribute greatly towards the decision to share.
In our research study, we took several data-sharing apps into the market as an experiment.
One example we live tested was an app that pre-populated a credit application using a person’s Open Banking information. We asked people whether they would be prepared to share their bank statement data, immediately, as part of that process.
The next one was around debt-free days. We asked people to share their amount of credit debt they had at that moment, plus information from their bank transactional behaviour. We could then tell them: ‘based on your current behaviour, it will take you X number of days to pay off your credit debt. However, if you stopped paying £50 a week on coffee and put that towards your credit debt, you could pay off your debt a lot more quickly.’ People loved this idea because it demonstrated some real, immediate value. The icing on the cake was when we said to them: ‘by the way, if you switch your credit card to a cheaper deal, you could repay your debt even more quickly.’
The third one used an individual’s bank account data as well as savings and credit information to find products and services that would make a good investment for them or give them a better deal.
The experiment was a success: 60% of those who used our apps agreed to share their data. The point to make here is that, when you pin down the value exchange, most are very willing to share their data.
The challenge you have is how to convince somebody to share their data? That’s the issue that we’re all going to face, because much of society isn’t used to giving data such as their bank statement data as part of an insurance application or any application as it stands through a digitised exchange.
If you think all this is new the advent of sharing transactional data, think again. Several million people already share their bank statement data in the UK through screen-scraping applications. If you’ve got a mortgage, you will have shared your bank-statement data during some stage of your application – most commonly in paper format. So, when we talk about sharing bank-statement data, we shouldn’t think of it as something new. We’ve all done it; we’re just not necessarily conscious of when. Is it, then, such a big shift forward to share it digitally and in real time as opposed to doing it in an insecure or paper-based way? If you look at this in its purity what you can start to see is not new and not unwanted. It is in line with what people are demanding: digital, instant, easy decisions and transactions.
What data will be available?
Looking at what data will be available through Open Banking, there are two sources of information: personal and commercial data.
The data that’s to be available is everything that you see on your bank statement.
– People can share three, six or twelve months’ worth of transactional data with a third party. It’s up to the customer and the third party to question why they want the data and for what purpose.
– For a SME (small, medium, enterprise business), it’s three years’ worth of data. This will be useful in understanding the payment performance of that small business.
If you’re an insurer, you’ll immediately be able to understand the premiums that are being paid from a person’s account and to who. You’ll be able to see or identify renewal dates, payment performance, whether a payment is made in instalments for example.
You’ll be able to see if a customer has made a claim in the last 12 months and what that claim was for. You might be able to infer something about their lifestyle and behaviours. Do they have a risk-averse lifestyle? You’ll get a general feeling of their financial wellbeing. For example; do they dip into their overdraft on a regular basis, or do they always have something left in their account each month?
This level of detail can be hugely beneficial for not just pricing and acquiring custom, but to help you have more meaningful, more informed dialogue that’s therefore hugely beneficial for the person who has agreed to share their data.
What can you do with the data?
At Experian, we have some initial thoughts on how this data could be used. Imagine a scenario where you can access data to help you:
• create personalised risk scores, which could outperform the sorts of scores currently used in the market
• verify certain questions within a quote process by understanding more about the applicant’s income and expenditure – or lifestyle
• analyse transactional data to identify if an individual has, or has had, a gap in terms of their insurance cover
• look at where a customer has policies with different providers for different services and combine policies to provide a new offering – a better, more personalised offering.
• use payment behaviour to predict, prevent, or detect fraud.
We have to be honest and acknowledge that there are some challenges in terms of what would motivate a person to share data. My initial response to this is the opportunity to provide a more personalised price, which could lead to cheaper premiums. We also need to be sensitive to this, and help those customers (all customers in fact), who we’re asking to share their data with the upmost transparency in the request. What it is being used for, why, and where it will be stored – and until when.
In terms of how you get to the value from the data, work needs to be done. It’s about testing and learning: being able to combine that data with other information that you have as an insurer to generate new models, new scores. Again, the opportunity sits around better priced premiums and the potential to reduce switching and increase loyalty – better serving people based on their actual needs and circumstances.
How do you use this data in real time? We believe insurers are well placed to do that because you already have that infrastructure in place. You do it already with quotes and with price comparators. So, there presents an opportunity to plug in a new, richer set of information. Combine it, mine it, and use it.
We think it can be beneficial to the broker channel, but there is work to be done there.
Finally, what will the aggregator’s role be? GoCompare broke cover a few months ago and said that they are opening APIs to access this data, and they’re going to be using it in a variety of use cases for product comparison – not just for insurance, but across their whole portfolio of products. We are working with them to enable this. Price aggregators are already gearing up to use Open-Banking data in product comparison. It’s a market people are already looking at and moving into with new propositions and new thinking.
Work carried out on Open Banking with the FCA demonstrated that a customer sharing bank statement data to switch current account providers could make significant savings, particularly if they’re someone who regularly dips into their overdraft. We’re talking savings of up to £150. The use cases and the role of aggregators are starting to gain traction and we anticipate seeing more activity in that market. It may be driven by one area; banking, but will soon transcend across their comparison site.
At Experian, we believe data matters and that data drives decisions, if the insight is unlocked from within it. How lenders are integrating Open Banking data is using our categorisation engine. Here, transactional data is categorised into taxonomy.
The engine is powered by Machine Learning capability and can make the raw data, from transactional data, into something meaningful and usable. We have invested heavily in this capability and while many lenders are benefiting from it in credit applications the uses for it far surpass credit markets. We believe that by applying the right analytics to data such as transactional data, you can receive signals from the data that are defined by specific behaviours, that can benefit various areas of opportunity and growth moving forwards. More importantly it can help you make much more informed decisions and help you better understand, and therefore better serve, your customers.
While Open Banking may seem conceptual and, until now, hasn’t presented itself as so opportune, the important thing is you start to understand what it could bring to you and your customers. What this looks like and what breadth of opportunity exists – and where.
The opportunity is here to start looking at what the future could enable.