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Taming the Data Demon: DataSeers and the Value of Actionable Data

By PaymentsJournal
September 5, 2019
in Data, Emerging Payments, Featured Content, The PaymentsJournal Podcast
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Taming the Data Demon: DataSeers and the Value of Actionable Data

Taming the Data Demon: DataSeers and the Value of Actionable Data

Data is a vital part of any business in the payments industry. Almost all divisions within a company must deal with data in some capacity, yet many companies lack a uniform, comprehensive approach to data. With faster payments becoming more common, the need for better data handling is even more pronounced, especially for a company’s treasury executive.

Therefore, fintechs are trying to fill this gap by offering products which process, tag, and handle data across the company. One such fintech is DataSeers, a Georgia-based company offering an array of data-centric products.

PaymentsJournal sat down with DataSeer’s CEO, Adwait Joshi, to discuss money management, fraud prevention, and a data-centric approach to business. Joining us in the discussion was Tim Sloane, VP of Payments Innovation at Mercator Advisory Group.

PaymentsJournal
Taming the Data Demon: DataSeers and the Value of Actionable Data
PaymentsJournal Taming the Data Demon: DataSeers and the Value of Actionable Data
PaymentsJournalTaming the Data Demon: DataSeers and the Value of Actionable Data

 

Using data for better money management

If you’re the CFO or controller at an issuing bank, there are a lot of details regarding money management you need to keep track of each day, said Joshi. You’re “dealing with cut off times, processors being in different time zones, networks being in different time zones, some money not posted today, it’s going to post tomorrow, and so on,” explained Joshi.

All this complexity can make it hard to track money flows. But Joshi pointed out that at its heart, all this complexity boils down to a math problem which can be solved with data. “Everything is in the data,” said Joshi.

There’s data for how much is loaded onto a card. There’s data for all the relevant time zone information and cut-off times. The trick is building a system that can automate the data collection and processing.

Joshi pointed out that although creating such a system is not easy, it’s still straightforward. There are already set rules of when the cut-off timing is, what the timing is for the networks, or whether there is a hold in funding. Therefore, DataSeer’s reconciliation product uses these well-defined rules to automate the process, giving the CFO or controller the ability to just verify if everything looks correct, rather than having to manually assemble the requisite information from multiple programs.

Sloane agreed with Joshi and noted that by automating the process of collecting and normalizing data across multiple sources, tools like those offered by DataSeers are set to change the way financial institutions staff and manage their businesses.

“Today you probably have 15 to 20 people working in the analytics area, [but] that’s probably going to increase to maybe 40,” said Sloane. On the other hand, the amount of people working on operational processing would go down.

Data & fraud protection

The biggest challenge facing a CFO, according to Joshi, is the deceivingly simple question; am I transferring this money to the right person? And a corollary to that is the question of whether the money is coming from a legitimate source.

Joshi said that the process for determining the legitimacy of a transaction is often delayed, even occurring after the money gets transferred and deposited. In such a system, a bank does its job by eventually identifying fraud, but someone, somewhere in the payments lifecycle, still ends up losing money. “So when you talk about money movement, you have to do the fraud prevention right in line at the time,” said Joshi.

An example of this can be found with card-not-present payments such as Visa’s Direct Pay, Joshi pointed out. Since you’re using an external API to push and pull money, those transactions “go through a certain set of rules or algorithms to make sure that the money is kosher,” said Joshi. But he added that as payments get faster, the need to quickly assess for fraud is heightened.

Sloane offered an optimistic take by highlighting how machine learning platforms can harness a wider range of data to make an informed decision about the fraud risk of a given transaction. He mentioned, for example, services that monitor the dark web for clues that fraud is happening.

The data-centric approach: “Think of it as a water filter”

A data-centric approach is predicated on the idea that data should be governed properly. That consists of making sure that all the data coming into, or already existing within, a business is usable, said Joshi. To be usable, it needs to be accurate and labeled appropriately.

A failure to adopt a data-centric approach can hurt a company’s profits. Moreover, it can also make a company in violation of different regulatory bodies. Joshi pointed out that many regulatory bodies are now requiring data to be handled in a certain way.

Despite this, “Most of the time, people don’t have a data-centric approach,” he said. He reasons that part of the problem is that many people simply don’t have an intuitive sense of data; they don’t know how to handle all the data pieces.

“That’s where we come in,” said Joshi. “We are a fintech that only deals with data… in such a way that it can be made extremely useful, can be turned extremely valuable.” DataSeers approach starts with making sure everything reconciles, which is one of the biggest challenges with data.

“You can think of [our product] as a water filter,” for a whole house said Joshi. No matter which tap you drink from, you’re guaranteed that the water coming out is clean. “We tame that data demon,” he said, “by handling those data assets very efficiently, tagging them with metadata, tagging them with compliance and other rules, tagging them with quality and governance aspects, and letting our clients use it in a much more efficient way.”

This approach makes data actionable, which is what Joshi believes is the ultimate point of using data. He said it’s great to have fancy graphs and charts, but unless you know how to interpret them, and whether the interpretations are justified, these fancy graphics are useless.

DataSeers seeks to deliver crisp, actionable insight derived from all of a company’s data. So far, the company has worked with multiple fintechs and banks, as numerous CFOs and Chief Risk Officers welcome the chance to have better data.

“We are changing data from an expense to an asset by taming the data demon and driving value through it,” said Joshi.

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Tags: AIBig DataDataseersMachine Learning

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