Banking Solutions Trade

How artificial intelligence can leverage Big Data to boost trade

How artificial intelligence can leverage Big Data to boost trade

Artificial Intelligence isn’t going to replace company CEOs any time soon. But what is already doing is pretty impressive: uncovering unique connections and relationships that can help businesses become more efficient at what they do, developing insight into new products and solutions, and creating a more frictionless trading landscape.

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SwissCognitiveAdopting the advantages of can also present challenges, from providing customers with “explainability” for decision-making to ensuring that users understand both its disruptive possibilities and its limitations. That’s why developing to leverage Big Data has been a major focus for HSBC Global Banking and Markets (GBM). “Our access to more than 400 sources of information can help us find natural trading partners who may never have been aware of each other,” Matthew Sattler, director of business development & data science, data analytics at HSBC Bank plc (“HSBC”), GBM in London, has been working on a far-reaching plan to collect, organize and synchronize the bank’s data assets across all of its global markets, then leverage that data to create value for HSBC and its clients.

“Generative , where we picture machines programming themselves without a human intervention, doesn’t exist in practicality,” says Sattler. “But we are pushing the envelope of using , a subset of where we can derive tremendous value for our clients. For example, we can deploy models where a machine is weighing the importance of hundreds of features in large and complex data sets to build new insights through hidden patterns that are perfect for a machine to mine. With enough data, it can then make decisions similar to a person and becomes more accurate over time, using more data. The result means data becomes a strategic business asset and helps put the client first through more tailored products and services and allowing the data to speak to us in new ways.”

Sattler’s team at HSBC GBM, for example, deployed as it sought the most accurate way to create natural groupings for HSBC’s clients across the globe. The results were surprising. Instead of grouping clothing manufacturers together, for example, revealed commonalities among clients that went beyond the market segment they served.

“It’s not just about assets under management, sales turnover or market sector,” says Sattler. “Data and allowed us to see beyond those conventional tierings and find that the products and services these companies needed were different from what we might otherwise have thought. We like to call this behavioural fingerprinting, which means we are able to recommend the right product at the right time given the current client financial lifecycle. If a buyer enters a new jurisdiction to do business with a new supplier then it is important that they have the right liquidity and currency management structure in place. Our tools are able to mine for these insights and thus build a tailored product, based upon our client’s current and projected business model.”

Sattler notes that and depend on solid and accurate data to derive worthwhile conclusions. Bad data simply makes organizations susceptible to bad decisions. […]


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