As the financial services industry continues to leverage machine learning and predictive analytics, the volume of data being generated is ballooning. This has massive implications for data security
Artificial intelligence (AI) has become integrated into our everyday lives. It powers what we see in our social media newsfeeds, activates facial recognition (to unlock our smartphones), and even suggests music for us to listen to. Machine learning, a subset of AI, is progressively integrating into our everyday and changing how we live and make decisions.
Machine learning in finance
Business changes all the time, but advances in today’s technologies have accelerated the pace of change. Machine learning analyses historical data and behaviours to predict patterns and make decisions. It has proved hugely successful in retail for its ability to tailor products and services to customers.
Unsurprisingly, retail banking and machine learning are a perfect combination. Thanks to machine learning, functions such as fraud detection and credit scoring are now automated. Banks also leverage machine learning and predictive analytics to offer their customers a far more personalised user experience, recommend new products, and animate chatbots that help with routine transactions such as account checking and paying bills.
Machine learning is also disrupting the insurance sector. As more connected devices provide deeper insights into customer behaviours, insurers are enabled to set premiums and make payout decisions based on data. Insurtech firms are shaking things up by harnessing new technologies to develop enhanced solutions for customers. The potential for change is huge and, according to McKinsey, “the [insurance] industry is on the verge of a seismic, tech-driven shift.”
Few industries have as much historical and structured data than the financial services industry, making it the perfect playing field for machine learning technologies.