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Big Data Manipulation In Finance Technology Via Python Tools

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Big Data Manipulation In Finance

Written by: Paul Matthews

Technological development as a whole, in 2019, was pretty much tunnel visioned towards machine learning, deep learning and everything in between. Fintech wasn’t an exception, in fact, this year, the professional figure who’s now known as “data scientist” was highly sought after in companies like Santander, where a brand new blockchain-based division is being built as we speak. With this in mind, it’s safe to say that Python developers and machine learning experts are going to shape the future of fintech, let’s analyse why.

Big Data Manipulation, But Why?

Big Data and cookies have found their way through business sectors such as eCommerce (with website personalization), by tailoring each part of the online portal on projections automatically generated by the machine learning algorithm. In this second half of 2019, we will see such applications being installed onto legal portals and “inquiry-based” ones, such as the fintech-related providers mentioned above. In this particular sector, the “manipulation” part happens when the tool (or tools, really) applies prefixed guidelines and behaviours to each individual user who’s contacting the site, presenting a certain output who’s tailored to the user’s gathered data. In the UK, this is already moving, especially when it comes to legal applications, where residential conveyancing solicitors found their way through a sector which very much required automation in order to fulfil “bulky” applications online.

The Process

Once the well-tailored output is presented to the user, the tool automatically calculates all the variables which are likely to impact the success of the practice. Imagine a financial application or a loan: the tool will calculate the success of such practice relying on the data gathered (even confidential) from the user. This, if properly applied, could speed up every process ranging from mortgages applications to small loans. Paypal Credit, in fact, is relying on a very embryonic front-end based Python tool, but they’ve announced how they will apply better algorithms in the nearest future.

The Market Value

Of course, it’s safe to say that anything Python-related has a significant market value in 2019, either if applied to development or business-related sections. With this in mind, given the rapid progression in terms of technological development which is currently applying to Fintech as a whole, we will see the request of Python savvy professionals growing in 2019, given what LinkedIn is currently saying. The market value for Machine Learning development applied to Fintech is already a million dollar segment, and it’s very likely to grow even further in the nearest future.

To Conclude

Machine Learning and Python in general, when applied to big data will most likely dominate the future of fintech and also finance in general. The needs for automation in order to speed up long, frustrating processes, in 2019, is extremely important and will lead us to the same old question which pops up every time this topic is covered: will machines take control over us?   

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