Artificial intelligence for the practically minded

30 Aug 2021

Artificial intelligence for the practically minded

By George Ralph

The phrase ‘artificial intelligence’ still conjures images of household robots that can predict our every need and carry out chores reactively while we go about our day. The reality of AI is that it is maths based programming which provides machine learnt results based on the data provided to a specific platform. While this doesn’t have the glamour that can often be linked with the moniker ‘AI’ it is actually a really impressive solution, developing all the time, that can help us process vast amounts of data in order to make deal decisions, reporting and data management more fluid and easier to process, allowing us to manage a much greater amount of information than ever before.

Globally, $37.9 billion has been invested in AI startups in 2021 so far, on pace to roughly double last year’s amount, according to data from PitchBook. There have also been a number of exits for investors in companies that use and develop AI, with $14.4 billion in deals for companies that either went public or were acquired. From these figures, there is no hint that the particular strand of AI that is developing into a useful tool is losing pace or that interest is waning. There are some obvious disappointments to progress in the field of AI: the still not-quite-ready driverless car or the aforementioned robots with ability for natural human interaction. But these are the media facing elements to a structured and advanced business that is helping nearly every industry on earth.

Machine learning, in basic terms a computer algorithm that can improve it’s output automatically with continued use, remembered experiences and use of data, is seen as part of AI. Building a model based on some initial training data, machine learning algorithms make decisions based on knowledge from that data rather than being explicitly programmed to do so. In our industry, this ability helps us in two main areas: data management and cybersecurity.

Data, however it is delivered, is only as good as how it is processed, read and reported on. The value comes from harnessing robust processes and procedures as part of a firms overarching data management strategy and that is where machine learning becomes such a valuable tool. The second step is delivering data in a format it can be read and reported on by managers. A central dashboard which provides a view of data from multiple sources, all stored within your data warehouse, making your data more accessible and manageable. Having the correct tools in place to not only manage data but keep data safe are key deliverables for every firm, but also for investors and regulators too. Keeping a holistic grasp on data assets as they grow is vital not only to harness the value of the data but also for investor and regulatory reporting purposes.

Data management goes hand in hand with cybersecurity, and RFA’s Managed Detection and Response (MDR) service and proprietary Securities Operations Centre (SOC) are innovative and still almost unique in the hedge fund space. Our machine learning applications offer a 24/7/365 ‘eyes on’ service, meaning there is no requirement for our clients to monitor their own networks for cyber attack. The MDR applications include endpoint protection, user behaviour analytics applications (UBA), email security, business continuity and resiliency planning. UBA monitors users from their endpoint, reporting back on changes in behaviour to flag potential security incidents. This might be access unusual files, changes to logging on behaviour, password changes, data downloads. By building a picture of each user, our team can identify threats more easily, be they from inside or outside an organisation. The applications are effectively learning your business structure and modelling a response to what is usual or unusual within your day to day business activity.

With reports this week in the Wall Street Journal that Apple, Amazon and Facebook have all pushed returns to the office to early 2022, using ML applications for your cybersecurity strategy can help assure you, your managers, your investors and the regulator that, regardless of the fractured working environment, you have a robust data and cybersecurity solution in place.

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