In recent years, artificial intelligence has been gaining significant traction within the financial services industry. Today, AI and ML based solutions are being leveraged by financial institutions for a variety of use cases – such as customer segmentation for improved marketing, cross-and up-selling, campaign management, client-facing chatbots, credit score prediction and augmented products recommendations to name but a few.
Let’s take a look at how some financial organisations are already using these technologies to improve the customer experience and gain competitive edge.
Visa – reducing online fraud with real-time transaction analysis
Having the ability to track transactions in real time has historically been an issue for major banks that have a huge amount of legacy IT infrastructure. However, getting data in place to be able to track transactions at low latency would not only give the banks a better view of their customers, but would also give them the dataset required to apply AI and deep learning to provide personalised, value-added products to customers as they learn about spending habits over time.
Real-time analysis also enables financial organisations to verify the authenticity of transactions instantaneously. Visa are already using real-time transaction analysis to reduce online eCommerce fraud. Used at the checkout phase, Visa’s service performs a real-time risk analysis of the transaction in question. This analysis takes several factors into account, including transaction history and device information. With this solution in place, eCommerce merchants can stop verifying customers through intrusive or cumbersome methods, like redirecting users to the Verified by Visa page to enter an account password.
RBS – utilising AI-infused chatbot technology to improve customer service
Did you know that 51% of customers would prefer for their questions to be answered by chatbots infused with artificial intelligence, rather than wait for a response from a customer service agent?
It comes as no surprise then that so many financial services organisations are looking to embrace chatbots in the next 12 months. Thanks to the use of big data and machine learning, these bots are armed with the ability to respond to customer queries around the clock – from onboarding concerns to transaction-specific questions. The technology can also manage customer requests and make product recommendations.
One bank already embracing technology is the Royal Bank of Scotland. They have recently launched a customer service chatbot called Luvo. Luvo is a natural language processing AI bot which will answer RBS, Natwest and Ulster bank customer’s questions and perform simple banking tasks like money transfers. If Luvo is unable to find the answer it will pass a customer over to a member of staff, increasing efficiency for both RBS and their customers.
UK hedge fund Man Group – using AI algorithms to define investment strategies
UK hedge fund Man Group has been using AI algorithms to define investment strategies for some of its funds over the past year, with extremely positive results.
A report by Bloomberg in September 2017 stated that “by 2015 artificial intelligence was contributing roughly half the profits in one of Man’s biggest funds, the AHL Dimension Programme that now manages $5.1 billion, even though AI had control over only a small proportion of overall assets.”
The machine learning system works by scouring millions of data points, including granular trading information on companies around the world and comes up with moneymaking strategies autonomously by spotting patterns humans can’t see. It also self-corrects to keep on improving and adapting to changes in the market.
Monzo – offering customers personalised finance advice using big data insights
Monzo Bank, the mobile-only banking service launched in 2015, is taking personalisation to a whole new level.
The bank is gathering data on customers spending habits to advise them on how to save money. For instance, its algorithms can tell when a customer has moved from paying a monthly money-saving tariff for gas and electricity on to a more expensive standard variable rate. It can then suggest to the customer that they could save money by finding a new supplier. The bank also uses geo-location data from customers’ phones, so when they go abroad it can offer them a competitive currency exchange rate.
Tom Blomfield, chief executive and co-founder of Monzo believes analysing data to help customers improve their finances will be the future for financial services brands over coming years.
How can the smaller financial players keep pace with the speed of innovation?
There’s no denying the financial giants do have an advantage when it comes to adopting the latest tech trends – they have both deeper pockets and the resources to make it happen.
A recent report by the World Economic Forum on the projected impact of AI on the banking and finance industry warns that small and midsize financial firms may struggle to find their footing in this rapidly changing environment, but this needn’t be the case.
Smaller firms that want to stay relevant and grow need to think of an investment in their core systems as an immediate need, not a future improvement. Along with that technology, they need a partner that knows the financial market and has the capability to both work with organisations to build the foundations on which to drive digital transformation, and the capability to deliver innovative digital services.
To discover how you can get started on your digital innovation journey with ANS, click here.