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The Trading Mesh

AI & Machine Learning – Working smarter, not harder

Tue, 02 Oct 2018 00:03:00 GMT           

Many of the most promising applications for Artificial Intelligence (AI) and machine learning technologies are about protection and establishing control. Fraud detection, risk management and compliance, for instance, are some of the ways these technologies are starting to make a real difference for banks and other financial institutions operating in global markets.

It helps to start by differentiating between AI and machine learning. Put simply, AI is the broader science of computing that aims to emulate natural intelligence and cognitive functions, whereas machine learning is the subset of AI that uses learning algorithms, based upon data and information in the form of observations and real-world interactions, to perform autonomous functions. So process-based functions and optimisation lend themselves well to machine learning.

Take data governance, for instance. This is an example of a function where machine learning can enable firms to more efficiently meet local regulation requirements by identifying and tagging what information is allowed to be used and then establishing compliance safeguards. This is particularly useful for firms adapting to new regulation, like the General Data Protection Regulation which was introduced in the European Union earlier this year.

Spotting anomalies in data is another task where machine learning can benefit firms. Once an anomaly is identified, firms can decide – based on human analysis, machine learning applications or AI – what to do about it. But the first step is to spot it amid all the noise.

For firms, identifying anomalies can lead to more than just trade opportunities. They can be used to detect fraud or to highlight emerging risk. Some firms are also experimenting with auto-decision making for hedging. Another area where machine learning can outperform human analysis is data cleansing given the volumes involved. This is also the case for natural language processing, sentiment analysis, surveillance, voice-based analysis – the list of applications goes on.

As data becomes increasingly important across global markets, financial firms are under pressure to embrace the technologies that allow it to be utilised. Already, tech firms like Google, Apple and Microsoft are looking at what they can do in this space and they are much further along the AI learning curve than some financial players. That said, forward-looking financial firms and providers are in a good position to compete because they best understand how data can be leveraged within the specific market structure.

At Telstra, we help firms across the financial markets data logistics chain to adopt machine learning and AI – whether they are looking to enhance their operations, find new business opportunities, or ensure regulatory compliance. Not only do we put in place the necessary data and connectivity infrastructure to assist in facilitating a wide range of use cases, such as customer 360-degree views, sequential event reconstruction for trading and various types of video analytics –we also work with a number of partners specialising in data analytics for the finance sector.

By bringing all of these elements together, Telstra enables firms to make the most of these new technologies.

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This article was previously published here