The following was produced by Tearsheet Studios. We worked with payments provider Fiserv to create a podcast series about open finance and the work of empowering fintechs, brands, and FIs to collaborate and innovate together.
In our third conversation in the series, we’re speaking to Jamie DelMedico, General Manager of Fiserv’s Aggregation and Information Services Unit. As we continue talking about the evolution of open finance, Jamie gives us some insight into the evolution of data processes within the fintech environment.
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The following excerpts have been edited for clarity.
Jamie DelMedico, Fiserv: I’m Jamie DelMedico, and I’m the general manager of a business unit within Fiserv called Aggregation and Information Services. Essentially, it is a data platform where we enable connectivity to over 18,000 different data sources, including small and large banks, credit unions, a full biller network, cryptocurrency wallets, as well as brokerage and wealth management houses. The underlying foundational element of the business unit is providing access to those data sources via consumer-permissioned access.
On top of that, we layer a whole host of different capabilities: personal financial management tools, wealth advisor tools (so wealth advisors can see a 360 degree view of their customer), and verification of both identity and account verification for users and government for the purposes of making payments, via ACH or otherwise. So, the platform starts with that underlying data platform, then we create unique use cases on top of that.
Our customer bases span from financial institutions to fintechs, to merchants, to governments looking to do an identity verification prior to making a disbursement of some kind.
As part of this series of conversations that we’re having with Fiserv, we are talking about the evolution towards open finance. Speaking of the foundational elements of open finance, what is the role of data and aggregation in your view?
Ultimately, data powers all of open finance, which is the ability for a consumer to be able to safely and securely access their data, and engage with whoever they want to engage with – whether they’re moving money from their bank account to fund their brokerage account or chasing a high-interest yield on a savings account with a neobank.
A financial institution has the greatest understanding of who a customer is because they go through very robust AML and KYC processes. And so, another use case is using the data held by the financial institution to verify a customer’s identity for any number of use cases.
The whole idea of open finance is powered by data, whereby the data from a financial institution or brokerage is pulled into an experience that then powers some other third-party experience. Data is the crux of open finance.
I’d love to jump into some of those use cases now. Particularly one which I know Fiserv is working on: biller data. So let’s start there.
One of the really interesting use cases that we’ve started to explore with biller data is that there are hundreds of millions of Americans – and maybe 50 to 100 million of those Americans have a very thin credit file. While those individuals may be paying their utility and cell phone bill on time every single month and building creditworthiness with those different pillars or with those merchants, they don’t have any traditional credit instruments, like a credit card, mortgage, or loan of some kind.
One of the use cases we’re exploring is leveraging biller data into another data source that a lender could use to assess creditworthiness. Then, those bureaus can look at that data at the bureau level, and see that a consumer pays their bills on time every month, and potentially use that to increase their credit score, which makes one more creditworthy from a financial institution perspective.
Or, leveraging that data by the financial institution itself to assess one’s creditworthiness in addition to their FICO score. That data can be really helpful to both expanding who is creditworthy, as well as the universe of creditworthy customers for financial institutions that are looking to perhaps move a bit down market and grow their business by what would traditionally be a subprime loan – but ultimately becomes a subprime loan when backed by real data on that consumer and how they pay all types of different bills.
That type of biller data seems more ubiquitous than other traditional types of credit data. What are some of the hurdles in expanding the pie to be able to include this type of biller data in credit decisioning?
There are many thousands of billers, so one hurdle is just getting access to that data and finding consistent and safe consumer-permissioned ways to access it; and there are not a lot of providers out there that can do that.
At Fiserv, we’re in a unique position in the marketplace where we have the largest bill pay platform with CheckFree. That positions us in a way where we have unparalleled access to that biller data.
The second hurdle is normalizing that data. Most billers don’t have an API available, so it’s a challenge to get access to the data with consumer permission, and then bringing that data into a platform that normalizes it and looks at all the different line items in a bill to power that experience. There’s a lot of magic and artificial intelligence that happens behind the scenes to normalize that data in order to make it useful for a bureau or for a financial institution in leveraging that data to make a credit decision.
I want to shift gears a little bit. At Tearsheet, we talk quite a bit about alternative data sources. But I’m quite curious to hear what Fiserv is doing with traditional FI data. What are some of the use cases there that you’re working on?
The most traditional use case was in personal financial management. Most financial institutions and fintechs are offering some kind of personal financial management use case, where the consumer can see how and where they’re spending their money, where they may have potential cash flow shortfalls based on historical spending, future bill pays, and ACH money movement out. That’s where we grew up as an organization.
Now, we’re starting to leverage that same data to power other types of use cases. For example, in Q4 this year we’re deploying a subscription management service where we can actually identify where a consumer has different music or video streaming services, or any other random subscriptions, and offer a view of what those subscriptions are with the ability to actually cancel those subscriptions.
Another use case is looking at transaction data in an account, and identifying all of the taxable expenses. That can be then fed into your annual tax prep that you do with a CPA or an automated online platform. There, a consumer can choose where their charitable contributions or home office expenses may be tax deductible, pull them out, and use them in that way.
Those are the traditional FI use cases; then we’re using that same data to power merchants. For example, a lot of merchants want to move away from payments with credit cards or debit cards, so FIs are developing wallets for those merchants. The idea is to make it really easy for a consumer to make a payment at checkout, because merchants tend to see large abandonment at checkout when it’s time for payment, because the consumer doesn’t have their credit card or debit card on them at the time, so they abandon the order. A lot of these merchants are looking to create a wallet experience where their consumer’s credit and debit card may be part of that wallet, so they can offer a pay by bank experience.
The same data that powers personal financial management from the FI can also power a pay by bank experience, where you can pay directly from your financial institution safely, securely, and consumer-permissioned. Ultimately, this enables a really seamless experience for the consumer, which also reduces a whole bunch of interchange fees that merchants are paying to the large credit card providers.
That’s an interesting use case. You mentioned Fiserv growing up in PFM, and I guess this feels like the logical maturation of PFM; as opposed to just a small loop feedback to a customer, you’re actually enabling customers to do things they haven’t been able to do before with PFM. So let’s zoom out and talk about where we are with PFM in this context.
PFM continues to be a really critical use case for financial institutions. Financial institutions are competing with neobanks and very niche players in the fintech space providing services to a gig economy worker. And they’re competing not only for those assets, but also for keeping those eyeballs engaged.
Personal financial management can play a key role in keeping that consumer engagement in the financial institution. The way they do that, ideally, is by providing actionable insights to the consumer that help them in their financial journey. It’s not just pie charts with ‘here’s how you spend your money’ – it’s also proactive alerts like, ‘you’re going to run out of money’, ‘you can save money by doing this’, or ‘we recommend that you pay down this third-party credit card debt – here’s a loan consolidation offer for you.’ It’s all those things that keep the eyeballs and engagement on the financial institution.
Even though PFM has been around for 20 years, it’s still the core use case for most consumers at financial institutions: help me manage my money better and more automatically.
The other thing I’d say is that when you’re a financial institution that has a consumer connected with multiple held away credit cards, or a held away mortgage, that actually powers the financial institution to do some very targeted marketing to that individual. They can have a full picture of everything about that consumer and use that.
In the example of the thin credit file, the FI could potentially use that for making credit decisions, offering credit to a consumer, or offering a high-yield savings account as a promo to a consumer – because they know they’ve got two, three or four held away savings accounts with other FIs that are offering them high yield. They can use that data to then do power marketing. That’s obviously really important to financial institutions looking to drag back some of those assets that have left the FI over the years.
Let’s shift gears and talk about another use case: cryptocurrency. Clearly, it’s early days there, but I’d love to hear how increased data sharing and connectivity is empowering new use cases with digital assets.
There are a couple of different angles on this. One is the emergence of a whole host of cryptocurrency wallets that give access to buy, hold, and sell all types of different cryptocurrencies. One of the key elements of those crypto wallets is the ability to move money securely into the crypto wallet, so the consumer can then leverage those funds to buy cryptocurrency. Again, access to data and open finance enables that money movement from a financial institution or a neobank into a cryptocurrency wallet, to be able to then facilitate that buy, hold, sell experience in the wallet. That’s the first use case open finance opens cryptocurrency, and we’re at the center of that today and do power a lot of money movement for various coin wallets.
The other piece, that’s becoming even more important, is that wealth advisors and financial brokers are starting to see their customers slice off a bunch of their investable assets and move them away from traditional stocks, bonds, and mutual funds into cryptocurrency. These advisors and brokers then lose visibility of their customers’ or clients’ financial standing. From a financial data aggregation perspective, it’s really important to have the ability to have a provider that can go into those various coin wallets and pull back the data on a customer’s holdings, losses, gains, what percentage of an overall portfolio is invested in cryptocurrency, how safe and aligned that is the investment objectives – then be able to provide advice based on those cryptocurrency holdings.
That’s one area today that is fairly blind for most wealth managers. So we’re starting to see financial data aggregators and platforms like ours start to go into the Coinbases and Krakens to pull that data out and make it available to financial institutions as well as brokerages.
What I’m hearing, Jamie, is that as new use cases become empowered by open finance, there’s almost a convergence. Instead of thinking of billers, FIs, and crypto in distinct areas, there’s a re-bundling of all these different types of services. Do you see it similarly?
Absolutely. Frankly, I see a lot of growth potential in those different data types and use cases. Just a couple that come to my mind are income and employment verification; you can see some income data at a financial institution statement like transactions or payroll – but now there’s the emergence of direct access to payroll providers. You can now look into everything from base salary, to bonuses earned, to employment history, and actually pull very granular W-2 level data that can be used for a relationship with a broker or wealth manager for use in a lending use case.
Another great example is small business data. If you’re a small business and want to go apply for a small business loan, you can plug into your accounting platform and leverage that data. Instead of having this cumbersome process of collecting paperwork, scanning it, and faxing it, you have direct access into a platform to power that lending process, as touchless and automated as possible. That’s really compelling, both for the small business owner that doesn’t have time to send all this paperwork in and track it all down, and for the financial institution that’s trying to take as much paper and process out of lending as they possibly can from an efficiency perspective.
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