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– Jonathan Barrett, SVP of EMEA and APAC at Dataminr


The rapidly evolving data landscape continues to change the way that banks and financial institutions are operating, redefining their business models and opening up new revenue streams. In fact, Gartner anticipates that data volume across businesses is set to grow 800% by 2022, with 80% of it residing as unstructured data.


Artificial Intelligence (AI) and the subset of AI known as machine learning in particular has been recognised as a solution for extracting the value from unstructured and structured data. Using this technology, businesses can bring together and analyse disparate data sets for banks and financial institutions, creating competitive differentiation that can drive growth. But this is easier said than done: first and foremost, institutions must put some structure around unstructured data.


Understanding unstructured data

One of the key problems for financial institutions trying to extract value from unstructured data is that the majority of such data isn’t set up for machine processing. Historically, computers have not been able to understand context when analysing data, with emotions, accents and human behavioural details not captured. This represents a major challenge, as banks look to collate and use content such as social media, weather data, blogs and even the dark web to inform financial decisions, invest in new markets and define business strategy.


Artificial intelligence allows organisations to seamlessly bring together these disparate sets of data from a multitude of sources that are currently running through the business network. As banks look to cut through the noise, they are using AI to distill these data sets into the most useful insight that can create real competitive differentiation.


By drawing multiple disparate data sets together and narrowing down the relevant information, AI is giving financial professionals critical awareness over a whole situation. Furthermore, it is empowering them to make the right decisions at the right time with the confidence that there are fewer gaps in knowledge and that the insights are of the highest quality.


Hedge funds – paving the way for AI in finance

Hedge funds are often foreleaders when it comes to these kinds of new techniques and processes and in many respects are paving the way for the use of AI in the financial sector. Traders in this field are increasingly turning to various disciplines of AI to make optimal use of the structured and unstructured data that they have at their disposal. For example, AI is enabling hedge funds to streamline their ways of working and collaborate across teams and locations, as well as, better understand the market they are focusing on, the strategies they are implementing and inform crucial investment decisions. In addition, research by BarclayHedge highlighted that more than 60 percent of hedge funds are now using AI and machine learning to develop new trading ideas, opening up opportunities for creativity and business growth.


Putting AI into action

Taking advantage of artificial intelligence that gives clarity to a complex web of information, allows financial professionals, such as investors and traders, to make decisions quickly, safe in the knowledge that the reasoning comes from real-time, relevant data sets. Likewise, real-time, publicly available data at scale is helping banks and financial institutions strengthen their ability to analyse circumstances faster, sharpen their understanding of breaking events, and accelerate their decision making processes


Banks and financial institutions that embrace new technology such as AI can better connect relevant dots, cut through information chaos, and act on fresh insights to drive their companies forward. Understanding the use cases, business benefits and complex relationships between structured and unstructured data is one way to stay ahead of their competitors, now and the future.



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