In recent years, artificial intelligence (AI) has become more widespread in both the public and private sectors. As data is collected from multiple sources and analysis becomes increasingly complex, it seems that those employed to identify trends and meaning are less able to keep up. Indeed, in its Tech Trends 2017 report, Deloitte identifies an exponential growth in data that is taking place, asserting that the digital universe is doubling in size every 12 months. The available human resource qualified to analyse this ever-increasing volume, then, is falling shorter as each year passes.
Many businesses are therefore looking to AI – and more specifically, Machine Learning (ML) – to help with big data management and analytics. Machines with the ability to reason and act could mean much faster processing and analysis than humans are capable of achieving, which in turn indicates the potential for a significant shift in the way data is handled and business is conducted. Here, we take a look what the benefits and risks of ML are, how it is currently being used and if it will really lead to a data revolution.
The benefits of ML in relation to data
The very nature of Machine Learning is suited to big data: with high volumes and complexity readily available, the algorithms can improve themselves quickly and efficiently. This has exciting implications for businesses in an array of sectors, from retail and telecoms to banking and finance.
1. Faster analysis
As mentioned above, ML enables advanced data storage, retrieval and analysis. Their capacity to improve simply through contact with data means the systems no longer need to follow programmed instructions, and can identify patterns in much the same way as a human might. Although the goal of ML is to mimic the human thought process, its own processes for retrieval and analysis and trading risk management are much faster, allowing patterns to be recognised and acted upon more quickly.
2. Accurate recommendations
With fast analysis comes an ability to make increasingly accurate predictions and recommendations, whether internally or to customers. Upselling and cross selling are a key part of sales, and unsupervised ML enables the discovery of obscurer behavioural or purchase patterns, which can be turned into sales opportunities. This inevitably creates a better customer experience and has the potential to positively impact business revenue. Such ML application is already being seen on sites such as Amazon, in their customer recommendations function.
3. Frees up employee time
Another advantage of ML is that it lifts time-consuming tasks from the shoulders of fintech employees, enabling them to focus on more important jobs. This is already proving beneficial for financial institution JP Morgan, whose programme interpreted commercial loan agreements in a fraction of the time it previously took the firm's lawyers and loan officers to decipher. Not only can ML speed up the mundane work that consumes hours of employee time, it can also reduce the possibility of errors occurring.
This isn't only true for financial services. In retail, using ML to automate processes such as order placement, transaction lookup and answering basic customer queries can free up a huge percentage of staff time (up to 80% according to deepPIXEL), resulting in greater value for both customers and companies.
Risks of ML in relation to data
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