"We Can't Do Everything Ourselves". New Business Models and $1 Trillion Segment. Fintech Insights from Q3
Tomasz Grynkiewicz
How the quarter flies! Since this year, it became a sort of a habit that at the very last week of each Q I put my headphones on, turn off Slack, and dive into hundreds of transcripts of earnings calls. What for? I look for any meaningful references to fintech. Why? Because at Netguru, beyond developing and designing fintech apps and solutions (API, backend, frontend, SEPA, you name it), we are always keen to see the broader picture.
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A huge amount of a developer’s daily work is debugging. It’s the thing that makes this job really annoying, however on the other hand, resolving an issue and making something work is such an amazing feeling that being a developer is still worth it.
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We’re seeing a shift in consumers’ financial habits. The world is moving towards fintech products. The companies and traditional banks that don't capitalise on this trend will get left behind. On top of that, the game is no longer just about creating any fintech product. It’s about creating a product that will make a difference. A product that users will use on a daily basis. What’s the recipe for a product like this? What aspects should be taken into consideration when creating a fintech product? How will the fintech industry change in the next couple of years? Is it going to disrupt itself? Let’s try to find the answers to those questions together during Disruption Forum London Fintech.
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There are various types of testing and many quality assurance techniques. You can choose from methods ranging from manual testing, through unit tests, functional test, to end-to-end tests. Each testing technique brings additional value to the project and improves its quality. Some of those techniques are also confused or misused.
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"The fastest-growing use of Python is for data science, machine learning, and academic research" - says a StackOverflow analysis. These are actually the biggest trends in tech recently. As we can read, "Python became a common choice for that purpose". Why did people fall in love with this general purpose programming language for research and data science? Well, there are many reasons.
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Machine Learning vs. Data Science – Key Differences and Similarities You Should Know
Grzegorz Mrukwa
As organizations strive to make the most out of the data they collect, they turn to different techniques for generating insights from vast data sets. The market for data science and analytics is on the rise – according to IMB, the number of jobs for all US data professionals will increase by 364,000 openings to reach smashing 2,720,000 by 2020!
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Machine Learning in Anti-Money Laundering. Can Advanced Algorithms Be a Silver Bullet for Banks?
Mateusz Hapon
One of the major European banks has recently admitted that it has failed to spot money laundering in its books for years. As a result, the bank has agreed to pay $900 million in a settlement with prosecutors. While it was a man (the CFO) who stepped down for this negligence, would a machine (Machine Learning) be a better guardian to prevent such situations in the future?
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As the UK’s Brexit secretary Dominic Raab continues to assure us that the UK will not crash out of the EU next March, while simultaneously preparing for that very eventuality, the minds at the Confederation for British Industry have been looking at the effects Brexit will have on daily working life.
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Machine learning is undoubtedly one of the hottest topics in software development right now. And for good reason. Machine learning opens up a whole world of new possibilities for developers, exciting app owners and end users alike. From greater personalisation to smarter recommendations, improved search functions, intelligent assistants, and applications that can see, hear, and react – machine learning can improve an app and the experience of using it in all manner of ways.
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Why Banks May Be Losing the Talent War for Software Developers. Four Leading Fintechs Share Their Tips
Tomasz Grynkiewicz
Access to capital? Naaah. Corporate tax rates? No biggie. Sponsored by the fintech company Stripe, a global survey of 1,000 bank and financial firm executives found that they are way more concerned with access to skilled software engineers. Some key statistics from the survey:
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