This is probably the conundrum we are in right now in financial services: banks have lots of data, but how much are they refining this data into actual products that can be sold and generate profit? In this context, we are glad to have the opportunity in this article to interview Luke Kennedy, co-founder and CEO at Fable Data which has built a business to process and sell access to anonymised bank data and thereby generate a new revenue stream for them.
Getting a company started is almost impossible. There are many times you think about giving up, especially in the early days, and in fact I keep a list of these times and it stands at 17. When I have a bad day, I look back at these moments and they remind me that there is always a way through.
So tell us more about Fable Data. What exactly do you do?
We partner with banks and credit card providers (data suppliers) to build data and insight products based on anonymised retail transaction data to help decision makers in government and the private sector understand economic trends in real time. Basically, unlocking the value of this raw data.
Essentially, our technology reads spend data to understand what is going on in the world. We are not interested in what one person is doing, and we can never identify anyone, but we are interested in what whole populations are doing.
How do you protect the banks’ data in this process?
Our compliance programme with all data suppliers starts from our first interaction, most of them with no previous experience in monetising their data.
As such, onboarding with Fable is not a quick process; we have to value quality over quantity. We will often have compliance workshops, legal experts involved and ongoing data scrutiny to ensure we are receiving truly anonymised data.
We offer a proprietary tool (at no cost to our data suppliers) called the masking and redaction tool (MRT). This is refined and reconfigured, in collaboration with every new supplier we onboard and who can use it in their own environment. At heart we are a compliance technology company – our machine learning algorithms understand transactions and identify personal data.
Personal data can be defined differently in different countries, so the technology is localised and works in all European languages. Thanks to local language experts, we ensure that our MRT removes data that we identify as sensitive such as charitable or political associations. We’ve worked closely with European regulators, and we are frequently complimented on building a solution that creates value from data in a way that always puts compliance and ethics first.
Could you give us an idea of the markets in which you process banks’ data?
Right now, we’re pan-European: Germany, France, UK, Spain, Austria and Italy and we are ever looking to expand our geographic reach.
Working across all of these territories means we get to work with people all across Europe and visit some beautiful places and have made many friends and stories along the way. We’ve learnt a tremendous amount about the nuances between the different markets within Europe. For instance, at a macro level, even if increasing, credit card use is substantially lower across Europe than in the US and cash is still being more widely used in Germany and France than in the UK (in terms of transaction volumes).
Then there are inter-regional differences too which we perceive through the data. For example, city dwellers such as Parisians behave differently to those in rural areas.
Each market has its own dynamics and reactions to the wider economic and geo-political environment and we’re constantly monitoring this and always looking to expand our volume and reach in order to continue understanding these nuances.
Who are your customers? Who is buying this anonymized bank data from you? What do they do with this data?
Central banks and finance ministers are big users, and our data is used in interest rate setting and economic decision making. During Covid, we built a dashboard that tracked spend patterns at a city level for the UK Finance Minister. During this time it was great to feel useful.
Academics use our data to understand social and economic trends, such as how the energy cost crisis impacted household expenditure.
And the private sector uses the data to understand their customers better – for example where do they shop, what do they spend, how often and how far do they travel to get there.
Critically we have six years of historical data so users can track shifts against that historical context.
We understand that convincing a bank to give you access to their data is a long and difficult endeavour. Why?
A bank’s most important asset is the trust of its customers, so banks have to trust Fable not to get anything wrong that could impact this banking relationship.
There are many small steps to building confidence, but we’re seven years in and nothing has ever gone wrong; in fact, we win awards for how much we get right with patience and diligence!
An Italian banker once took me for coffee and told me that only two things matter: Relationships and Money. So, we try to get both of these right.
How do you see the market evolve?
We’re seeing more and more banks explore and launch revenue-generating data initiatives, and the demand from government and industry for the data and insight that banks can offer is only growing.