The use of artificial intelligence (AI) and machine learning (ML) is revolutionising the world of advanced analytics for financial services and telecoms providers. These sophisticated technologies can be used for multiple purposes, benefiting both companies and customers alike.
Advanced analytics help businesses improve their operations by increasing task automation, reducing human error, and more importantly, enhancing the ability to analyse and interpret vast amounts of data. Research published in Experian’s 2022 Business and Consumer Insight Report found that 62% of companies believe that AI and ML are already radically transforming the way they do business.
Improved business performance translates as the ability to drastically improve the accuracy of models and deliver a more convenient and personalised customer experience, which is of paramount importance in an increasingly digital world.
The challenges of advanced analytics
Francois Grobler, Chief of Decision Analytics at Experian Africa says that building the expertise, knowledge and infrastructure necessary to reap the benefits of advanced analytics can pose problems for many businesses. The increased IT complexity required to manage AI and ML was pinpointed as the biggest challenge by 48% of businesses in Experian’s latest report.
“Explainability is another issue that needs to be addressed. As companies accelerate the adoption of advanced analytics, they must ensure the outcomes produced by AI and ML can be understood by those providing regulatory oversight as well as being able to explain to customers why a specific design has been made. Tellingly, almost a third of businesses stated that the explainability of complex ML models is a major challenge. Given the complexity of the processes underlying advanced analytics, ensuring transparency and explainability in AI and ML is not an easy accomplishment – but when done right it can greatly improve the accuracy of creditworthiness and risk assessments.
“The third key challenge businesses are facing is integration. While AI and ML can support businesses in finding new solutions to problems, integrating them into pre-existing systems is causing headaches for some. Embedding ML requires scalable computing power and sufficient infrastructure to enable it,” says Grobler.
The solutions to the challenges
How can financial services and telecoms providers overcome the challenges of AI and ML to maximise their benefits? Implementing the three tips below is a good place to start for businesses looking to harness the power of advanced analytics.
“Upskilling teams on the multiple facets of advanced analytics is crucial. By upskilling their teams in AI and ML, businesses can build an analytics team that can maximise the use of advanced analytics to improve operational performance. At the moment, the gap in IT expertise is currently one of the main impediments to a wider and more effective adoption of these cutting-edge technologies,” adds Grobler.
“Training workers in AI and ML is not a one-day activity, but a long-lasting investment, as advanced analytics is evolving at a relentless pace, with new regulation being implemented to bring governance to its use. This means that companies need to provide continuous training, empowering employees to keep up with the latest advancements in the sector.”
Secondly, businesses need to be smart about where to drive investment. Grobler says that scalable computing power greatly contributes to the effective functioning of ML and should be prioritised.
Experian’s report reveals that Financial Services and Telecoms providers recognise the importance of this factor. 79% of the companies that have already invested in cloud-based software applications stated that access to more computing power was a major reason for deciding to make such investments.
“And finally, the easiest way for companies to take concrete steps towards a better implementation of advanced analytics is by establishing partnerships with organisations that can provide expertise in AI and ML – both the technology and the regulatory requirements. It can be daunting for any business to master every aspect of advanced analytics. Forging the right partnership can help them find direction in this broad and complex area, complimenting and supporting existing analytics teams,” he adds.
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