As many of you know, Open Banking Excellence was formed by myself and associates to exacerbate the quality of discussion taking place on the subject of Open Banking and open data.
It was therefore, a real pleasure, to travel to London recently to give a presentation at the newly formed London OBE grouping.
As expected, the turnout and quality of both presenter and questions from the audience were excellent. It was refreshing to see the appetite for the use of bank data has only grown over the last year.
My thanks go out to Helen and Tatyana who are doing a sterling job of moving the debate forward within the London grouping.
I was fortunate that colleagues were able to assist in videoing my presentation, and I present it below. I have had the text transcribed and attach it below the video.
“I’m James Varga, the Founder and CEO of The ID Co. I’m a bank data addict. I've been surviving off bank data for the best part of ten years I think now, since Gavin and I worked together at Money Dashboard.
We’re a FinTech company who focus on using bank data and we’ve been doing that for a while. We do that as a global B2B Open Banking SaaS platform, called DirectID. We do that over multiple countries and we live and breathe bank data. When Helen [Child - OBE organiser] asked me to come and talk, I asked myself: What should I talk about?
I thought I would share a little bit of our prospective. As someone who’s on the opposite side of the scale than infrastructure and regulators, we’re living and breathing bank data every day.
Really that’s part of the purpose for Open Banking Excellence, bringing industries together and trying to figure out where the next hurdles are and how to collectively overcome that.
Helen also suggested I give a demonstration of something we’ve done recently, so I thought I’d share where we see the value in a product we’ve launched recently. It’s not a sales pitch, I’m not here to do that but I think there’s a real insight when you start connecting lots of this data together, as I was saying earlier.
The first thing to talk about is that Open Banking and bank data is changing the world.
One of the reasons we’re all here is that there is a huge movement all over the world. Around 10 countries have launched Open Banking programmes.
The regulators have put in a huge amount of effort and the banks - sometimes reluctantly - are being forced to give up this data and give access to it. Open Banking is all about empowering consumers with that data. There’s a huge amount of effort being placed in something that, I honestly think, will change the way a lot of us interact with financial services.
It’s hugely exciting to be at a place where this is all happening under our fingertips. That’s part of the challenge, right? With the change that’s happening, how do we take advantage of that? With September and PSD2 deadlines coming in, depending on what happens with the RTS and forcing banks and timelines and so forth. I think it’s going to be an interesting year.
But to a certain extent, I think it’s all pointless!
As an industry or regulators, we talk about educating the consumer. We talk about trust marks and all sorts of things that have to go into Open Banking: market acceptance, promotion, media etc.
Data in Context
I think a lot of that is great, including the regulatory drive for opening up access to data.
The risk adversity still has to be there, but it’s the data that counts.
At the end of the day, Open Banking is just open data, it’s what you do with it that counts.
I think in a lot of conversations that we get involved in this is still a missing element. We’re still focusing to a great extent on how to push data out, give it away, support the liability models or the frameworks for that and the infrastructure around it.
We always operate on a very consumer focused level and for us it’s about how you apply that data. I think it’s that part of the challenge that’s still there as an opportunity for a lot of us. Half the room are FinTechs, so a lot us are there to consume this data and help people do things with it. I think that’s the big challenge. There’s so much left to do!
In terms of the top-down approach, I think there’s a focus towards providing this data, and there’s still a lot of challenges.
One of the challenges around the regulatory structures is that they don’t preconceive all the different use cases. In the UK, we still face challenges around how this data can be used; who needs to be the AISP? Who’s the regulated entity? Can you share this data with another 3rd party? What consents are involved?
You have to look at some of the use cases for which Open Banking in the UK was designed on.
I’m going to choose one called price comparison. I don’t think we’ve figured out yet how Open Banking is used with price comparison.
To be a regulated entity, to be an AISP, you have to do account information services, which means you have to give that data aggregated back to the consumer.
In a price comparison use case it’s not about that. It’s about taking that bank statement data, understanding the individual and suggesting products. If we look at GDPR, we’re not supposed to use any data we don’t need to use.
Regulating the Data
But there’s a gap that the regulators haven’t quite got their arms around yet, and there will be lots of these gaps because price comparison doesn’t fit the model that exists today. Somebody has to be regulated to be the AISP, so you’re forcing the price comparisons sites to do that, in order to access the directory to get the data and do things with it.
On the other side of the coin, we’ve got banks where performance is still a bit of an issue. Currently, we see a lot of downtime in banks. There’s a huge amount of effort that has gone into this. There’s a lot of work that’s involved and it’s going to be challenging to implement these APIs and structures.
For us to rely on this data we need ‘up-time’. Shutting down for maintenance for two hours is challenging for people like us. Inconsistencies in data is a big challenge and account holder name is a hot-topic which has also been a challenge. Lots of use cases involve validating that person back to the individual with account holder names. The regulated entities, AML, Compliance Officers, KYC etc., can’t always tick a box, so they can’t use the data as much as we would like them to use it.
Balances, inconsistencies and data quality are still challenges, so there is still lots of work to do. It’s all about providing that data to the industry.
Where I get much more excited is the consumption, it’s that ‘bottom-up’. It’s the opportunity to use this data to change lives, put consumers in control of that data and get them to do things with it. It’s the two-sided model that is one of the important lessons we’ve learned over the years.
DirectID has been up and running for three and a half years now and we’ve seen at least 100 use cases. Those that are successful tend to have a benefit on the business side as well as the consumer.
If you can’t create that network effect between the two, the chances of being successful are hugely diminished.
From a business point of view, this is a big opportunity for all the disruptive start-up banks, FinTechs, Wealth Advisers, through to insurance companies. If you can consume that data, you can compete with the bigger propositions out there.
Accessing this data is not quick and easy and it’s becoming more and more commoditised.
But it’s that opportunity to consume and use that data that really gets exciting.
Of all the 100 use cases, it’s the lending that has seen the balance between the impact to the business pains and consumer benefit.
We’ve seen conversions rate increases by 20-30% for some of our customers.
If you’re a lending company, mortgage, credit card, insurance, anything like that, and I could turn around and say your conversion for good transactions is going to increase 20-30%, that’s game changing!
That then puts you a step ahead of what most of the big banks can do.
Therefore, as a credit union there’s a huge opportunity for all the small banks to use this data; to compete with the big ones in an increasingly competitive space.
At the same time, it’s a safer process.
Our customers who have shared statistics with us, have seen an average of 7.5% reduction in fraud.
If you think about any large-scale business or financial institution, that’s a huge impact!
Convenience through Data
But if we can’t create convenience for the consumers, they won’t go through it. It’s about finding use cases where a 30% or a 7.5% would completely change a business, while still at the same time giving something for our consumers.
Bank data is nothing new; we’ve been using it for 25 years. I know somebody who worked at JPMorgan Chase & Co. 20 years ago. They were using aggregators to connect people to bank accounts, grab the data and use it to underwrite loans.
The difference here is that we have a much more regulatory driven and accepted infrastructure for it.
We’ve been using bank statements for 50 years on mortgages, none of that is anything new.
It’s about how to get to that next level; how can we commoditise this and make it accessible for the consumer?
There’s only one thing that motivates individuals and that’s convenience.
We all like security and privacy and they’re good to have, but the convenience is the one thing we see, time and time again, that will change behaviour.
One of the things I thought I’d talk about is Income Verification. A lot of our focus, especially in the lending space, is applying this data to business problems.
Every credit-based decision has a fundamental need for affordability checks, of which, one half of that question is income.
Income is a seemingly simple challenge, but when you dig deeper it becomes increasingly complex. Fundamentally, the reason for that, is not everyone is like me.
Not everyone applying for loans, credit cards and mortgages is like me. Most of them don’t even get paid monthly.
In the world we live in, from gig economy to cash workers, students, graduates and retirees, one of the most interesting things we have seen is that income is not ubiquitous; there’s no consistency with it and there’s far less consistency.
In the US, only 10% of people that apply for loans on our platform have a monthly income.
I originally estimated it would be between 40-50%. But there as so many people who work zero hour contracts, gig economy etc., that lots of them don’t have a steady income.
Some may have a steady income, but not with the same consistency or from the same person. In the UK it’s about 20%, so a little different.
DirectID Insights, on which we built verified income on top of, is all about making that data accessible.
This is a journey just as much for us as any business that wants to use bank data.
You have to start to know it, use it and get comfortable with it. A lot of that starts in a manual underwriting process and then starts to migrate up into your automated decisioning.
Income is one of the interesting points. We made a mistake when we started this. The first product we launched flopped; nobody used it. It was because of the preconceptions and the assumptions we were making from a non-underwriter point of view.
So, we went back to the drawing board. We started sitting down with underwriters, credit risk teams, prospects, customers etc., and really tried to unpack what it was in that number that they were really interested in.
We found out it was context that was missing. Just looking at a bank statement and recurring transactions or regular payments and then assuming it was income isn’t enough.
These are teams of people that only deal in negative data.
Everyday they’re looking at bad stuff, they’re trying to disprove everything. What they need is a bit of context. They need to understand what that salary is. They need to be able to cross reference it and ask: Is there recency? Is there volatility? Is it up and down? Is it consistent?
It’s all these questions that an underwriter needs to understand. For me, this applies to any aspect of bank data, whether you do credit risk, fraud prevention or price comparison.
You have to go all the way back down to a simple question and build from there. This is an extremely complex issue and we spent a year coming up with calculated income and confidence scoring because there are all those variations with a huge tail of use cases and different aspects.
That’s the same for me with price comparison. When we talk to banks about generating price comparison engines or real time pricing engines for customers to price products at point of application, it always comes back to the same thing; how can we take that complex problem and make that simple answer out of it?
That’s a little bit about us and our product. The key lesson here is context. Open Banking is about data and we have to start applying it as an industry to the different pains that we see.
If we can do that, we can start solving those problems. If the solutions have benefits to consumers, then we can change the behaviour and get them to use this and things will start to roll on from there.
It’s going to be a lot of work. It’s going to be a pain for lots of us and there’s going to be a lot of failures, but we all have to have faith in the innate belief that there is value in this data.
Q: Really interesting presentation James. I’d like to get your view, is there more than just pure belief? The example with income is very interesting as it’s a really complex problem and you need to go step by step and you have to untangle the issues gradually. Do you expect this pattern to follow over the years, so, in the next ten years will you be able to precisely show the income to solve the price comparison problem? Or do you expect there to be some sort of step change in the next couple of years?
A: I think the industry is so used to doing things that it does today, that a step change in financial services - an incredibly risk adverse industry - is very unlikely.
I was on a panel a year and a half ago and there was an Oxford debate, which I love and hate at the same time. It was based on cryptocurrencies and will they change the world overnight? My argument was that it wouldn’t. For us to displace the capital markets with a cryptocurrency is almost impossible, there’s too much immersion already there. I think we do have to take a step-by-step approach.
Credit risk decisions have been performed the same for so long that sitting down with a Credit Risk Officer and asking them to do something completely differently is difficult.
It’s like buying an autonomous car. Are we all going to start buy autonomous cars? No. We’re going to wait until a friend buys one and probably hold the steering wheel until we’re used to it! I think we’re asking the same thing here in the industry.
There is a fundamental change that Open Banking and bank data can provide for a lot of these businesses and processes, but doing this overnight isn’t going to happen. You have to break it down into chunks, build it back up, and do things in a different way.