How Open Finance Fuels Innovation and the Democratization of Data
May 3, 2022 | 2 min read
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January 13, 2021 | 0 min read
In our company press release announcing $300 million in series C funding and a $1.9 billion valuation, we announced that the investment will be used to help organizations everywhere automate the money experience. This experience entails using aggregated and enhanced data to make interacting with money as intelligent and personalized as shopping on Amazon, streaming music on Spotify, or driving a Tesla.
This post unpacks these examples in greater detail, illustrating what the money experience consists of.
The retail giant Amazon uses an intelligent network of robotic pods to quickly sort customer orders, enabling fast and efficient delivery. What enables this efficiency isn’t just the hardware, though that’s impressive in its own right, but the software itself — which uses machine learning and artificial intelligence to predict incoming orders.
“These robots do a neat little bit of machine learning,” says material scientist Zoe Laughlin, “which is to realize that if they happen to have on board an item that is currently trending and is somehow proving popular, instead of returning back to the middle, they actually hang around the outside, expecting them to be called upon again.”
Similarly, the money experience uses predictive analytics to highlight potential red flags for users around savings and expenses. It might be that a recurring payment is coming up and there’s insufficient money in an account, or it might be that a loan is nearing payment completion, freeing up money that can be put to use elsewhere. Whatever the case may be, real-time tracking backed by machine learning enables each customer to optimize their financial health, just as Amazon’s robotic pods anticipate what’s coming next and automatically change their behavior.
Despite heavy competition from Apple Music, Amazon Music, and other players, Spotify is still holding strong in the music space. One of the major reasons for this is that they’ve invested heavily in machine learning and artificial intelligence to create a predictive recommendation engine. This engine uses natural language processing and metadata to scan blog posts, articles, lyrics, and more — learning which songs are likely to meet each user’s individual tastes. The result resonates with users, who find new jams they love.
What makes this engine particularly compelling is its ability to surprise and delight users. “For music, it’s pretty easy to get someone to consume by giving them what they consumed yesterday. It’s kind of table stakes,” says Spotify’s machine learning head Tony Jebara. The trick — and the true benefit of AI in this instance — is knowing when to throw in something new to keep the experience fresh.
Likewise, by crunching aggregated and enhanced financial data coupled with machine learning algorithms, organizations can give customers an intelligent and personalized money experience. In practice, this looks like a personalized feed including updates, news, notifications, and educational tips that are geared for each specific user, similar to what Spotify does with their curated playlists. It’s like having a financial advisor on their phone.
“There is one aspect of Tesla where it is miles ahead of the competition,” writes technology consultant George Paolini, “and that is in its use of data to build what might just be the world’s most sophisticated, cutting-edge neural network anywhere.”
Tesla uses real-world autopilot data based on more than 1 billion miles of driving to improve the experience for end users. “When one vehicle learns something,” says Tesla CEO Elon Musk, “they all learn it.” Tesla’s site adds that “a full build of Autopilot neural networks involves 48 networks that take 70,000 GPU hours to train. Together, they output 1,000 distinct tensors (predictions) at each timestep.”
Given how many millions of people are struggling financially, it’s clear that people need autopilot for their finances. They don’t want an enormous set of tasks they have to complete to get their finances in order — manually categorizing transactions, managing the details of a budget, or figuring out what their best next action should be. Instead, they want an automated experience.
“People need to know where to go next,” says Nate Gardner, Chief Customer Officer at MX. “It should be, in essence, like a GPS for finances, detailing what true financial strength looks like and laying out general principles to get to that destination. This means that people should be able to quickly sign in, see their savings goals, get recommendations on what route to take and then be able to quickly choose or simply confirm automatic funnelling of money toward those specific goals, without having to pause their busy life.”
Amazon, Spotify, and Tesla are on the cutting edge of using big data and artificial intelligence to transform experiences in their respective industries.
MX is doing the same with banking and fintech.
We employ 50,000+ connections to 16,000+ organizations to give our clients access to exponentially more data than they’ve ever had. We then take that data and cleanse it, augment it, categorize it, and add enhanced metadata so that our clients can actually put it to use.
This results in a range of solutions that are otherwise impossible to offer.
One of these solutions is MX Pulse. With Pulse, organizations can use MX's data engine and predictive capabilities to provide insights via a financial feed in a vein similar to popular social media sites.
All of this enables you to provide intelligent and personalized guidance to keep users moving in the right direction toward financial strength, building user loyalty in the process. It also protects users from potential fraud or security risks by flagging anomalies — all while presenting updates, news, notifications, and educational tips that are geared for each specific user, similar to what Spotify does with their curated playlists.
Here’s a snapshot of what one user’s feed might look like when using Pulse.
Each customer will see something unique to them, all without realizing just how much number crunching and machine learning goes on behind the scenes to provide them with a simplified view of the transactions that deserve special attention, the reports that best fit their needs, the subscriptions they may have forgotten they were still paying, and more.
This is just a small example of what’s possible when the money experience is automated.
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