Apr 9, 2021

Scottish AI firm secure £1.2m in funding

Tilly Kenyon
3 min
Level E Research, an AI firm, has raised £1.2 million in seed funding
Level E Research, an AI firm, has raised £1.2 million in seed funding...

Level E Research, an Edinburgh-based fintech company that specialises in artificial intelligence-powered investment solutions, has just closed a seed funding round raising £1.2m from a group of private investors. The funding will be used to bring on new staff and continue investing in research, according to Founder and Chief Executive Dr Sonia Schulenberg.

Commenting on the funding round, Schulenberg said: “This very successful fundraising backed by well-known industry leaders is a huge vote of confidence in our technology and business model, enabling us to scale up our business development efforts as well as continue to invest in cutting edge research and attract the most talented people.

“Our hedge fund clients have been quick to realise the benefits that our unique machine learning platform can provide, and we are in discussions with asset managers who are keen to integrate our AI into their investment process or seeking to launch the next generation of AI-driven funds.”

Founded in 2018, the business combines machine learning, data science and behavioural economics, enabling institutional investors to develop, test and implement smart investment strategies at the highest levels of automation and at a significantly lower cost than traditional investment management business models.

Nicola Anderson, CEO of FinTech Scotland, said: “I am delighted to hear that Level E Research has secured a significant amount of seed funding. This is a testament to the strength of its innovation and capability and will lead to the creation of more highly skilled jobs in Scotland.”

The Growth of Fintech

COVID-19 has had a huge impact on different sectors, fintech being one. The Global COVID-19 FinTech Market Rapid Assessment Study gathered data from 1,385 fintech firms in 169 jurisdictions from mid-June to mid-August. It found that most types of fintech firms reported strong growth for the first half of 2020 compared to the same period in 2019, which was prior to the pandemic.

The fintech industry seems to keep growing in the UK. Recently a cross-border payment platform based in Singapore called TranSwap, launched in Scotland - the company’s fifth market after Singapore, Hong Kong, Indonesia and Malaysia.

The UK office and global R&D centre are in Edinburgh, where they expect to create 54 jobs over the next three years, including high-value digital roles such as machine learning engineers, full-stack developers and product owners to support TranSwap’s market expansion and R&D plans. 

Moving forward, the UK will need to become a more tech-friendly environment if the current fintech boom is to be sustainable through the provision of better training and facilities for technology companies. If the country fails to change its infrastructure, as suggested, we risk falling behind our European competitors, just as we did in electric vehicles. But, right now, the future's looking bright, and courtesy of the agility and dynamism of the UK’s economy, and its recent break from European bureaucracy, it should be a very tempting hotspot for tech investors in the coming years. 

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Jun 15, 2021

The advantages and disadvantages of AI in cloud computing

3 min
AI is being used in cloud computing, which works by allowing client devices to access data over the internet remotely, but are there pros and cons?

Cloud computing offers businesses more flexibility, agility, and cost savings by hosting data and applications in the cloud. AI capabilities are now combining with cloud computing and helping companies manage their data, look for patterns and insights in information, deliver customer experiences, and optimise workflows.

We take a look at some of the benefits and drawbacks of AI in cloud computing. 

The benefits of AI in cloud computing


Lower costs

A major advantage of cloud computing is that it eliminates costs related to on-site data centers, such as hardware and maintenance. Those upfront costs can be restrictive with AI projects, but with cloud enterprises you can access these tools for a monthly fee, making research and development related costs more manageable. AI tools can also gain insights from the data and analyse it without human intervention, reducing staff costs.

Deeper insights 

AI is able to identify patterns and trends in large data sets. Using historical data, AI compares it to the most recent data, which provides IT teams with well-informed, data-backed intelligence. AI tools can also perform data analysis fast so enterprises can rapidly and efficiently address customer queries and issues. The observations and valuable advice gained from AI capabilities result in quicker and more accurate results.

Improved data management

AI enables extensive data management, and cloud computing maximises information security, making it possible to deal with massive amounts of data in a programmed manner to analyse them properly, allowing the business to leverage information that has been “mined” and filtered to meet each need. AI can also be used to transfer data between on-premises and cloud environments. 

Intelligent automation 

Businesses use AI-driven cloud computing to be more efficient and insight-driven. AI can automate repetitive tasks to boost productivity, and also perform data analysis without any human intervention. IT teams can also use AI to manage and monitor core workflows. IT teams can focus more on strategic operations while AI performs the mundane tasks. 

Increased security 

With businesses deploying more applications in the cloud, security is crucial in order to keep data safe. IT teams can use different AI-powered network security tools which can track network traffic, they can flag issues, such as finding an anomaly. 

The drawbacks of AI in cloud computing


Data privacy 

 Enterprises need to create privacy policies and secure all data when using AI in cloud computing. AI applications require a large amount of data, which can include consumer and vendor information. While some data can be anonymous and can't be tied to personally identifiable information, knowing who the data belongs to makes it more valuable. When sensitive information is used, data protection and compliance is a major concern.

Connectivity concerns 

IT teams use the internet to send raw data to the cloud service and recover processed data. Poor internet access can hinder the advantages of cloud-based machine learning algorithms, as cloud-based machine learning systems need consistent internet connectivity. 

While processing data in the cloud is quicker than conventional computing, there is a time lag between transmitting data to the cloud and receiving responses. This is a significant issue when using machine learning algorithms for cloud servers, where prediction speed is one of the primary concerns.

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