Let’s say you have a working product, a growing number of customers, and a lot of ideas for new features.
Great! Now what?
It’s one thing to hit the ground running, and something else entirely to keep going. You need a plan for the future and you need it fast, otherwise your lucky streak will end before long.
So where do you start? What do you do first?
Two words: product analytics. Use product analytics tools to develop your software product the right way.
At some point in the creation of your fintech start-up you will have to make decisions that are very hard to un-make. One such decision is your choice of tech stack. If you go wrong here, your costs may skyrocket down the line, putting you in the red despite best intentions.
Your fintech needs a programming language that is easy to handle, scalable, mature, high-performance and coupled with ready-made libraries and components.
Luckily, Python is there to deliver it all. Read on and you’ll find that it’s quite easy to make a case for Python in this industry.
Poznań, Poland—STX Next is pleased to announce that their CEO, Maciej Dziergwa, will be speaking at a Bloomberg Live event in London called Charting Poland’s Future: Spotlight on Growth & Innovation.
Let’s be honest, when was the last time you were excited to purchase insurance? Or the last time you discussed openly with friends about your most recent coverage?
If you’re drawing a blank, I’m not surprised. Most people hate the idea of shopping for insurance. But this state of affairs may change very soon.
Data analytics, AI and machine learning are driving the emergence of insurtech.
And insurance being an antiquated industry is precisely why insurtech companies are worth your attention. The top players in the field are spurring big changes in an industry ripe for innovation.
Python is a hot topic right now. So is machine learning. And ensemble models.
Put the three together, and you have a mighty combination of powerful technologies.
This article provides an extensive overview of tree-based ensemble models and the many applications of Python in machine learning. Read on to learn why these models are the perfect solution for numerous machine learning problems.
Note: this article is available in two versions: here and on the STX Next GitHub.
In fintech, you can’t afford to fly blind.
This is especially true for your choice of tech stack. You don’t want to fall behind the competition due to lack of proper research.
Or maybe you’re just plain curious about the top solutions for fintech companies. Python is a great choice in this industry - but which companies use it, specifically?
We’ve got you covered with our list of top 17 fintechs that use Python in their tech stack.