We're here to make money work for everyone and we're doing things differently. For too long, banking has been obtuse, complex and opaque. We want to change that and build a bank with everyone, for everyone. Our amazing community suggests features, test the app and give us constant feedback so we can build something everyone loves. We're focused on solving problems, rather than selling financial products.

This year is expected to be one of immense change. The pandemic is becoming a memory, a recession is looming, and tech hiring is experiencing a period of flux. In this environment, there aren't that many things that you can rely on, but there are a few tech sectors that are holding fast and hiring for roles, such as cybersecurity, data science, and artificial intelligence (AI). AI, as well as careers in machine and deep learning, will continue to be safe bets this year. Thanks to the applicability and demand for AI-driven technologies across multiple sectors, including banking, healthcare and education, the global AI market is one of the fastest-growing sectors within the wider tech industry, which means there are still plenty of roles hiring.

It feels as though 2019 has gone by in a flash, that said, it has been a year in which we have seen great advancement in AI application methods and technical discovery, paving the way for future development. We are incredibly grateful to have had the leading minds in AI & Deep Learning present their latest work at our summits in San Francisco, Boston, Montreal and more, so we thought we would share thirty of our highlight videos with you as we think everybody needs to see them!. We were delighted to be joined by Dawn at the Deep Reinforcement Learning Summit in June of 2019, presenting the latest industry research on Secure Deep Reinforcement Learning, covering both the lessons leant in the lead up to her presentation, current challenges faced for advancement, and the future direction of which her research is set to take. You can see Dawn's full presentation from June here. Reinforcement Learning is somewhat of a hotbed for research, this year alone we have seen several presentations that have broken down the ins and outs of RL, that said, Doina's talk just last month gave us some new angles on the latest algorithmic development.

In 1981, Bill Gates said "640KB will be enough for anybody". Surpisingly, it was hard even then, to imagine where we would be today; in a sea of data. Big data is fuelling the world of business, driving human decision making and building machine learning systems which have become essential components to success in the growing sector of digital banking, particularly in Monzo and Revolut. Big Data, as the name suggests, provides 1000's of metrics which inform many real decisions, so how has Data ensured the continuing success at these innovative companies? Monzo, founded in 2015 by 5 young men who had previously worked for Starling Bank provided a truly unique banking experience than any on the market at the time.

Banks lost about $4 billion to account takeover (ATO) fraud attempts last year and the losses are set to further increase as this year progresses. ATO and other fraud methods that rely on using stolen credit card numbers or other personal information are becoming decidedly popular with bad actors, who are employing increasingly sophisticated technologies to execute their scams. The majority of ATO attacks are automated, meaning that fraudsters are becoming more comfortable utilizing advanced learning tools such as deep learning, artificial intelligence (AI) and machine learning (ML) to target financial institutions (FIs) and their customers. About 40 percent of all ATO attacks now count as high-risk, meaning banks of all shapes and sizes must reexamine how they think about data protection, security and the tools they use to guard against emerging threats. In the latest Digital Banking Tracker, PYMNTS analyzes the ways in which FIs are working to keep fraudsters from accessing customer information and funds on their mobile and digital channels. FIs worldwide are fending off fraudsters from all angles, with many FIs trying to prevent new attacks while still resolving the aftermath of others.

Banks lost about $4 billion to account takeover (ATO) fraud attempts last year and the losses are set to further increase as this year progresses. ATO and other fraud methods that rely on using stolen credit card numbers or other personal information are becoming decidedly popular with bad actors, who are employing increasingly sophisticated technologies to execute their scams. The majority of ATO attacks are automated, meaning that fraudsters are becoming more comfortable utilizing advanced learning tools such as deep learning, artificial intelligence (AI) and machine learning (ML) to target financial institutions (FIs) and their customers. About 40 percent of all ATO attacks now count as high-risk, meaning banks of all shapes and sizes must reexamine how they think about data protection, security and the tools they use to guard against emerging threats. In the latest Digital Banking Tracker, PYMNTS analyzes the ways in which FIs are working to keep fraudsters from accessing customer information and funds on their mobile and digital channels.

Banks lost about $4 billion to account takeover (ATO) fraud attempts last year and the losses are set to further increase as this year progresses. ATO and other fraud methods that rely on using stolen credit card numbers or other personal information are becoming decidedly popular with bad actors, who are employing increasingly sophisticated technologies to execute their scams. The majority of ATO attacks are automated, meaning that fraudsters are becoming more comfortable utilizing advanced learning tools such as deep learning, artificial intelligence (AI) and machine learning (ML) to target financial institutions (FIs) and their customers. About 40 percent of all ATO attacks now count as high-risk, meaning banks of all shapes and sizes must reexamine how they think about data protection, security and the tools they use to guard against emerging threats. In the latest Digital Banking Tracker, PYMNTS analyzes the ways in which FIs are working to keep fraudsters from accessing customer information and funds on their mobile and digital channels.

I recently gave a couple of conference presentations about how we are thinking about speed when developing machine learning systems at Monzo. This post covers some of the background to the points I was making in my talks, as well as what we're doing in the Monzo machine learning team to speed up our own work. Speed is not a word that is regularly associated with machine learning teams. When we talk and write about accomplishments in machine learning, there is often a focus on the problem, the algorithmic approach, and the results - but no mention of the time that it took to get there. I remember once speaking with a machine learning researcher who worked at a large company.

App-only fintech startup Monzo is scaling up its machine learning capabilities and plugging employees into the rich stream of data generated by 1.7m global users as it forges ahead of the high-street banks in the digital space. Neal Lathia, data scientist and machine learning lead at the UK-based bank, told The Drum how his team is delivering "incremental gains" in the app, in customer and the product using smarter automation since he took the role on one year ago. Lathia, previously lead data science at SkyScanner, commands a team of more than 20 people. When he joined there were only two. Now he is looking to harness the wealth of data at the company's disposal to get Monzo "working faster". It contrasts with the role a data scientist at a high street bank may have, weighed down by legacy systems, corporate baggage and sheer scale.

The global tech startup scene is a noisy, crowded space. And then there are the stories of those entrepreneurs whose ideas have endured into something truly transformational; Amazon, Apple, Google, you name it--many of the biggest and most influential companies in the world today were born of this heritage. "One thing we can all agree on: The key attribute of a startup is its ability to grow," wrote Forbes' Natalie Robehmed. And as my former CEO, Mark Jones, used to say, "All big companies were once small companies too. The only difference is that they grew up."