As banks, financial services providers and brands predict and plan for the way consumers will manage their money in the future, artificial intelligence (AI) is high on the business development strategy for 2016 and beyond. Gideon Hyde, co-founder of Market Gravity, explains how and why artificial intelligence (AI) could hold the key to standing out in banking and financial services.
AI is already around us and used everyday within payments, money management and for robo-advice, particularly in the area of intelligent digital assistants that handle regular customer service enquiries and tasks. It can process “big data” far more efficiently than humans and can recognise speech, images, text, patterns of online behaviour, for example to detect fraud as well as appropriate advertisements for upselling
Smart machines and technology can turn data into customer insights and enhance service provisions, bringing the digital experience closer to the human interaction for consumers.
Santander announced it is to provide secure transactions using voice recognition via its banking app, while RBS has trialled “Luvo” AI customer service assistance to interact with staff and potentially serve customers in the future. In Sweden, Swedbank’s Nina Web assistant achieved an average of 30,000 conversations per month and first-contact resolution of 78% in its first three months. Nina can handle over 350 different customer questions and answers. Several other banks in the UK and internationally have similar systems in place or are trialling them. These organisations, alongside new challenger banks and payment providers, are leading the way in intelligent banking, with other traditional banks and financial institutions expected to follow suit.
Machine learning technology has advanced rapidly over the last ten years, and there are now more flexible and cost-effective solutions that banks can implement, even with their often legacy-burdened IT systems. The computer analyses new information and compares it with existing data to look for patterns, similarities and differences. By repeating the activity, the machine improves its ability to predict and classify information making it easier to make data-driven decisions. Banks and fintech companies already use machine learning to detect fraud by flagging unusual transactions, as well as for other purposes. It’s far more efficient than human manual monitoring and is expected to become the norm in banking and finance.
Consumers, particularly millennials, increasingly prefer digital servicing channels over going into a branch or calling in and have experienced AI in other areas of their lives – for example Siri on iPhones. From an economic standpoint, AI applied to customer servicing is also a big opportunity for retail banks to increase automation and reduce the cost of serving customers – which will be attractive as banks across the sector seek to reduce their cost bases.
Personalisation is a major talking point for banks and many are experimenting with innovative ways to match products and services to the consumer. For the customer the technology can simplify the money management process and offer suggestions and recommendations for upgrades and new services by matching algorithms. There are also great examples of companies embracing personal financial management (PFM) such as San Francisco start-up Wallet.AI, a new app which helps consumers make smarter purchase decisions, manage their finances and make cost savings while they are out and about spending money.
While AI can improve customer experiences, machines will not simply replace human customer service staff – many consumers will still want to speak with a person for more complex queries and so the key for banks will be delivering a service that gets the balance right between machine and human, ensuring human intervention at the necessary points.
Banking and financial services organisations need to pay attention to technological developments such as AI and plan ahead for what is coming and how they will address the changes. The way businesses discover and implement innovation is shifting, with the launch of venture teams and accelerator panels or internal ‘incubators’ to bring a start-up mentality to corporate organisations. The growth of automated services, AI and robotics has heightened the need for traditional banks, financial services and payment providers to work closely with proposition designers, coders, developers and marketers to ensure new concepts are identified, developed and commercialised professionally and effectively.
Pepper is already being used in customer services industries as a replacement to an informational booth or welcome desk. Now, a partnership with IBM means the Watson-powered version offers a service that developers can build into their apps or devices to make them smarter by doing things such as analysing data, making personal recommendations and even understanding human language and emotion. The Watson-powered Pepper will be able to tap into data such as social media, video, images and text with more types of jobs in development.
Mizuho Financial Group Inc bank in Japan introduced Pepper to its flagship branch in Tokyo in summer 2015 to deal with customer enquiries, while Mitsubishi UFJ Financial Group trialled “Nao”, a humanoid robot to interact with customers, also designed and developed by Aldebaran.
Robotics are already being used for back office tasks, but Pepper and Nao are pushing the boundaries of what an autonomous, artificially intelligent robot can do within a banking setting, and we envisage a time when robots will work side-by-side with humans.