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APIs have evolved from simple data connectors to the fundamental architecture driving financial innovation. In this episode of the Tearsheet Podcast, I speak with John Pitts. Plaid’s John Pitts reveals how they’re driving open banking and empowering consumer control. He is the Global Head of Policy at Plaid. With a career spanning regulatory and policy roles, Pitts brings a unique perspective to the table. He discusses the evolving role of APIs in financial services. From his role at the Consumer Financial Protection Bureau (CFPB) to leading policy at Plaid, Pitts shares key insights on open banking. He explores how APIs are shaping the future of consumer financial data rights and fintech innovation.

Reflecting on his journey, Pitts shares, “I didn’t realize at the time that I was stepping into this nexus of innovation.” He explains how his role at the CFPB allowed him to witness the early stages of non-bank financial services. He shares how these experiences now inform his work at Plaid. He highlights the critical role APIs play in fostering open finance and enhancing consumer control.

Pitts explores why APIs are essential for modern financial infrastructure. He explains how Plaid is working to bridge gaps in financial data connectivity. Pitts shares his expertise on improving fraud prevention and enabling embedded finance. He emphasizes practical steps to align innovation with consumer needs. His insights highlight the evolving role of APIs in modern financial services.

The Highway Analogy: APIs as the Backbone of Financial Services

Pitts compares the role of APIs in financial services to the construction of a national highway system. “It’s like moving from dirt roads to paved highways,” he says. Pitts emphasizes the necessity of modernized data-sharing mechanisms. Screen scraping once led financial data transfers. But Pitts highlights how APIs now provide faster and safer solutions. Their reliability is transforming how financial data moves securely. “Consumers’ ability to share their data securely is fundamental to unlocking innovation,” he adds. He stresses that the adoption of APIs by financial institutions is critical for open banking.

Consumer Control and Open Banking

A core theme in Pitts’ discussion is consumer control over financial data. He explains how APIs empower consumers to move their financial data seamlessly between platforms. This fosters open banking.

Unlike in other countries where open banking is largely regulated, Pitts notes that in the U.S., market forces have driven API adoption. “We have more open banking in the U.S. than anywhere else,” he states, citing the high number of connected accounts as evidence. Pitts also touches on the regulatory landscape. He highlights the importance of the recently introduced 1033 rule in accelerating API adoption.

Embedded Finance: Beyond Financial Institutions

Pitts highlights how non-financial companies are using Plaid’s APIs for embedded finance. These examples show the growing demand for integrated financial solutions. Landlords are using APIs to enable digital rent payments. Tesla is streamlining car purchases with embedded finance. These examples highlight the rising demand for integrated financial services. “Businesses like John Deere and Tesla are early adopters. They’ve embedded financial tools to improve user experiences,” Pitts explains. This gradual adoption, he suggests, will soon speed up as regulatory clarity improves.

Digital Fraud and Risk Management

Digital fraud is a growing concern in the financial services industry, and APIs offer a potential solution. Pitts describes how banks and fintechs can leverage APIs to share data and build network-level defenses against fraud. “Fraud prevention is one of the biggest opportunities in open finance,” he notes. Pitts emphasizes its importance for consumer trust. Banks can also use APIs to provide consumers with tools to monitor and manage their connected accounts. Pitts argues that these innovations can strengthen relationships between banks and their customers.

The Strategic Opportunity of API Adoption

Pitts urges financial institutions to see API adoption as both a compliance need and a strategic opportunity. It’s a chance to enhance innovation and engagement. He highlights how APIs can help banks deepen customer engagement by becoming the “home base” for financial activity. “When a consumer picks one account as their linked account, their usage of that account increases,” Pitts observes. He suggests that banks can leverage APIs to solidify their role in a consumer’s financial ecosystem.

The Big Ideas

1. APIs Are the Backbone of Modern Financial Services. They serve as the foundation for modern financial services. This enables secure, efficient, and scalable data sharing. “It’s like moving from dirt roads to highways,” Pitts explains.

2. Consumer Control Powers the Future of Open Banking. APIs empower consumers to access and share their financial data across platforms. This fosters innovation. “The U.S. has more connected accounts than anywhere else,” Pitts notes.

3. Embedded Finance Is Becoming a Key Use Case for APIs. Companies outside the financial sector, such as Tesla and John Deere, are adopting APIs for integrated financial services.

4. APIs Enable Stronger Collaboration to Prevent Digital Fraud. They facilitate data sharing among financial institutions, creating stronger defenses against digital fraud. “Greater data sharing protects consumers,” says Pitts.

5. API Adoption Is Both a Compliance Need and a Strategic Opportunity. Financial institutions can use APIs to increase consumer engagement and maintain account primacy.

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Tearsheet Podcast · Building the digital financial infrastructure of tomorrow — A conversation with Plaid’s John Pitts

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Tammer Kamel is CEO and co-founder of Quandl

What is Quandl and what was the inspiration for creating it?

Tammer Kamel, Quandl

Quandl is a service that delivers financial data. We bring together over 25 million financial datasets on a single website, and make this data available to analysts in any form they want.

The inspiration for Quandl was my own frustration working with data when I was an analyst. I’ll give you one example among many: I was working in Python trying to create a simulation around oil and uranium prices for the past 20 years. It was a struggle to find the datasets on Google. When I finally did, the formats were a mess. It took half an hour to get them formatted and merged. Then I had to repeat the same exercise every day to update the model.

This lead me to think, “Why isn’t there a platform where I can type in ‘uranium prices’ and get a clean dataset? Why can’t I get it directly into Python, or other tools I use, like Excel, R and Matlab?”. I knew that a platform that could take a tedious, half-hour process and cut it down to 10 seconds would make life easier for me and probably millions of other people. That was the inspiration.

Why has it been hard historically for everyone to access financial data? Analysts had it but the rest of us didn’t. Why?

Data used to be a scarce resource: hard to produce, hard to acquire, hard to use effectively. As a result, only the largest institutions could afford to pay for data. Individuals and smaller firms were priced out.

But that era is gone. Today, we live in an age of data ubiquity: there’s data everywhere. Anyone with a web connection can get stock quotes, or currency exchange rates, or company financials, or demographic forecasts. Access to raw data is no longer a limiting factor. That’s a huge contributor to the rise of solutions like Quandl.

Of course, raw data can only take you so far. For data to be usable, it needs to be cleaned, structured, documented, and quality-controlled. This is where the democratizing power of the internet comes into play. Quandl’s marketplace model replaces inefficient, pre-internet, “factory-style” data production with a network of specialist data creators and vendors.

Quandl gives these publishers a distribution channel and a transaction mechanism. It lets them make their databases available to the whole world. Thanks to Quandl, they can start competing with larger publishers. And they’ve been amazingly effective at that, as our success shows.

A second dynamic at work is unbundling. It’s similar to what is happening to cable TV. With traditional terminals, users were forced to pay an annual subscription to every database, whether they needed it or not. It was the business model, just like cable’s business model forced consumers to buy a bundle of 500 channels when they only watched an average of 15. With Quandl, users only pay for the databases they need so we’re much more accessible.

Both of these trends – scarcity replaced by abundance, and bundles replaced by choice – are patterns we’ve seen play out time and again in other industries. Internet platforms tend to disrupt pre-internet businesses. We’re seeing it happen now in the world of financial data.

Looking at Product Hunt, it appears a lot of new apps are being built using your APIs. What’s happening here and what does this ecosystem look like years down the road?

Yes, this is an exciting development for us. Currently, there are over a hundred financial and analytical apps built on top of Quandl’s API, all created by the Quandl community.

You can get Quandl data into scientific and mathematical tools like Mathematica, Matlab, Maple, Octave, R, SAS and Stata. You can also access our data from general purpose languages like C/C++/C#, Java, Julia, Python and Ruby. There are integrations for trading platforms like AmiBroker, Money.Net, Quantopian, Tiingo and TradingView; and for analytic tools like Mode, Plotly and Statwing. Finally, there are dozens of business intelligence and financial apps with sophisticated, next generation analytics – like Ayasdi, Domo, Kensho and Premise – that use our data.

Our philosophy here is quite simple: “Let a thousand flowers bloom.” Instead of getting into analytics or visualization ourselves, we empower other businesses to build their own solutions. We’ve seen other data providers restrict users to the analytics already present in their terminals. This means that users can’t choose the tool they want to work in.

It also means that they’re paying for all the analytics, just like they’re paying for all the databases, even if they only use a fraction of them. It’s the 500 channel problem all over again – they have to pay for a bundle of analytics, everything from yield curve calculators to option pricers to commodity shipping analysis, just to get the few that they actually need.

If you’re a bond trader, you don’t need an equity valuation model. If you’re a commodity trader, you don’t need a yield curve calculator. If you’re a market technician, you don’t need a fundamentals-based screener. But if you’re a terminal subscriber, you’re paying for everything.

The app model, on the other hand, allows each analyst to select their own tools. Not only is it more economic, it’s also more powerful: it allows users to pick and choose the precise apps that are optimal for their needs, instead of a generic one-size-fits-all approach.

Our vision for a few years down the road is a rich ecosystem of apps powered by Quandl. Each app is hyper-focused and tailor-made for each specific use case: a tool for every task, and every task with its own tool. We believe this is the future of data analysis.

Are there particular challenges you’ve encountered in building out Quandl — how did you overcome them?

Building a delightful user experience is always a challenge. There are so many ways that people use data. We want to give our users maximum flexibility, but we also want to keep it simple. We think we’ve achieved this balance well so far, but there’s always a temptation to add more complexity. We’ve put a lot of resources into making the experience faster, easier and more intuitive. This hasn’t been a priority in the data industry, but it’s a priority for us.

While the front end might appear simple, the back end is anything but. We’ve had to solve some hard technical challenges to build Quandl. Bringing together millions of datasets from thousands of different publishers on a single, unified platform requires non-trivial advances in data parsing, structuring and delivery.

Another challenge is scale: building infrastructure to serve 50 million data downloads a month with a millisecond response time. Our clients include many of the biggest banks, asset managers and hedge funds in the world, so speed and reliability are crucial. We’ve done some exceptional engineering around the Quandl API to make this happen.

What’s next for 2016?

Our highest priority, for 2016 and probably forever, is adding new data to the platform. The more data we have, the better we become for all our users.

In addition to expanding our time-series data coverage, we will be adding a couple of new data types to Quandl: non-time-series data and intraday data. We’ll also be adding more vendors and unique databases.

The Quandl website, ecosystem and feature set will continue to evolve as we listen to what our customers need. That’s a never-ending process of improvement for us.

Photo credit: Glyn Lowe Photoworks. via Visual hunt / CC BY

Tag: financial data

APIs: Building the digital financial infrastructure of tomorrow — A conversation with Plaid’s John Pitts