MX with David Whitcomb | E243
Aggregation of Aggregation and Data Enhancement.
On today's Fintech Impact episode, Jason Pereira will talk to David Whitcomb, VP of Product at MX. It is an aggregator of aggregators in that it provides technology companies and traditional finance companies with a way of accessing a common data format across multiple different data aggregation companies and kind of pulls in data from all above and spits it up. It's a silver simplification.
Episode Highlights
1.00: MX not only connects with other aggregators, but the company also connects directly with some of the biggest things in North America for direct API access.
2.01: Once we get the data, that is really where the magic of MX begins. We like to argue that we have the reading connections in North America. But on top of that, we take the data that comes into our ecosystem, and we have been doing enhancement of that data for 10 years. So, we take an account in a transaction and take that transaction from the bank, explains David.
4.05: David says that they are able to create a high level of intelligence with that data set for both computers and humans.
6.08: Once the data is refined and is in enhanced state, MX creates personal financial management tools out of it.
9.00: To the average consumer, they are starting to see if they haven't seen the new grading technology in MX which allows them to put the transactions from the bank account or their investment accounts into an app or into a dashboard or into some other places, says Jason.
10.15: MX has created a ton of intelligence around grabbing different data models, specifically around the council transactions. Normalizing the transactions, adding content to it around categorization classification of what the transaction is so that when a user of MX product or service gets the output of that account transaction we have, we've normalized all of it and have an often typically enhanced it so that it's more readable and more usable for whatever is being built, says David.
14.04: Everyone is getting into the payments world, which means a person is connecting their accounts at lots of different places and in many cases those connections then create a council of their own, says David.
16.02: Some people try to jump in and say we are just going to use machine learning to do it all and because the data that comes in is so dirty, it's been really hard for just machines to do it without having a clean set of data, says David.
18.06: David talks about data enrichment and other data insight. He also talks about the insight that he provides to the vendors that he is utilizing.
19.00: David talks about rules and regulations followed by different countries when it comes to data sharing.
22.01: David and his teams are working on creating tools that help banks and credit unions and fintech. They are planning to offer banking services and have secure tokenized API's.
24.01: David and his team are also creating intelligence for the holders of the data because they think that the trust and security belong in the data provider.
25.02: With the shifts in the way payments are being made or with the shifts in the way consumers are engaging payments, we sell a lot of data that can enable our stuff. We are accessing account numbers, routing numbers to enable some of those use cases. We see that by ensuring that the data we have is leveraged in the right ways. You can actually create a more secure sharing environment so that if fraud is occurring and account connection, we can actually help stop it or mitigate it with banks and credit unions and fintech along the way, says David.
26:01: David wishes everyone to have modern authentication ecosystems so that secure data sharing is just the norm.
3 Key Points
David and Jason discuss about data normalization. When data is received directly from the bank, VN, API, or via another aggregator, the data is in often different formats. It often has different values added into it or different parameters to that transaction; David explains how MX helps to simplify the entire process.
David shares how they use multiple layers of analysis. They have been using human intelligence in conjunction with computer analysis for the past decade.
David explains what proprietary and competitive advantage is and what is the consumers data.
Tweetable Quotes
"If I wanted to use multiple sources for very good reason, I would have to then take that data then and then fix it, because I am basically getting quote UN quote the same language but with two different accessible always. So, you guys basically solve that problem." - Jason
"From a bank and credit union perspective it's something that we believe they have they still have the competitive advantage on basic credit unions have the trust of their consumers because they have historically had money available at all times. Their payments network networks typically don't fail. They race each processing has been hardened because of regulatory reasons." - David
"As a user, if I really trust my bank the most and I can see all of my data in one place, it gives me the ability to be in more intelligent about where it's going and how I am spending it beyond just what's coming out of my debit card every month." - Jason
"So historically, personal financial management has been what I would say is called financial literacy." - David
"If I look at typical personal financial management tools, they don't tell me what actions are needed now or you did next two days. They help me understand the lay of the land, and so we think that they are in a shift from financial literacy to advocacy, because insights allow me to, in a timely way, engage with my finances and know what I need to do and when I need to do it." - David
"I think a lot of people are finding themselves there, and with tools like MX offers, it allows you to centralize that stuff more quickly so that you can effectively manage." - David
Resources Mentioned
Facebook – Jason Pereira's Facebook
LinkedIn – Jason Pereira's LinkedIn
Woodgate.com – Sponsor
LinkedIn – Jason Pereira's LinkedIn