Experts from the banking sector speak to Douglas Blakey and Evie Rusman about key trends to watch out for in 2021
A change in mindset
The most important change that we will see in the financial services landscape next year is a change in mindset. Hani Hagras, Chief Science Officer, Temenos, writes
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Decision makers in the sector are increasingly accepting the fact that artificial intelligence can be central to delivering the best services for customers and the pandemic has accelerated this trend, as those in the financial sector have hastened plans to automate certain processes.
Staff shortages, working from home and the requirement to provide improved digital services have all demonstrated the importance of adopting artificial intelligence. Take, for example, SME loans. The huge demand for Government backed loans for businesses impacted by Covid-19 exposed gaps in many banks’ technology and service capability.
Banks that were able to deploy artificial intelligence to assist application processing were able speed up the progress and alleviate pressure on call centres. Decision makers have seen that AI can minimise risk, save time and cut costs.
Regulation
However, as AI becomes increasingly prevalent within the industry, regulation will catch up. As we have seen this year the European Banking Authority identifying key challenges in the roll out of Big Data and Advanced Analytics in banking. Regulators and customers will continue to demand greater transparency and for banks to be able to explain the decisions that their AI technologies have made.
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By GlobalDataWe are likely to see a spike in adoption of Explainable AI (XAI), as bankers will be required to explain their automated decisions to customers and regulators. XAI systems are highly transparent models which describe, in human language, how an AI decision has been made.
Crucially, they do not solely rely on data, but can be elevated and augmented by human intelligence. These systems are built around causality, creating space for human sensibility to detect and ensure that the machine learning is ethical, fair, safe, un-biased and course-correct if it is not.
