With the advent of artificial intelligence driving change across many industries, how are Financial Services firms harnessing this technology? And on that basis, there's now a renewed focus on how new technologies can automate repeatable processes, enhance compliance audit trails without impacting customer experience, and even make interactions between staff internally and staff to customers smoother by providing responses to queries without direct human intervention. This means that the customer journey can have a renewed focus on it, as deploying AI at key points of highly regulated processes will mean that critical requirements for regulatory compliance can be fulfilled accurately and confidently by removing the [human] margin for error. Whilst industries such as Automotive have adopted robotics and AI into the manufacturing process, Financial Services is beginning to follow suit – robotic process automation, or RPA, is beginning to automate the multitude of repeatable processes across banking; for example, if a customer requests a change of address, the many legacy systems means it can often require updates to 6 or more records from the CRM system.
As banking organisations, 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 2017 and beyond. Santander announced it is to provide secure transactions using voice recognition via its banking app, while Royal Bank of Scotland has trialled'Luvo' AI customer service assistance to interact with staff and potentially serve customers in the future. 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', humanoid robot to interact with customers, also designed and developed by Aldebaran. 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.
At the Summit, I had the opportunity to share some thoughts on computer vision, and its impact on financial services. Activity using computer vision input has increased across the general technology landscape. As financial services players attempt to get ahead of the curve, what is the potential for computer vision? We think computer vision is most likely to transform insurance, commerce, capital markets, and banking.
This week Bank of America, MasterCard and several financial start-ups announced new tools -- known as chatbots -- that will allow customers to ask questions about their financial accounts, initiate transactions and get financial advice via text messages or services like Facebook Messenger and Amazon's Echo tower. The early versions of the financial chatbots generally do little more than answer basic queries about recent transactions and spending limits. But companies are aiming to build the chatbots into full-service automated financial assistants that can make payments and keep track of your budget for you. Facebook and Microsoft have introduced high-profile, occasionally problematic, chatbot features to capitalize on the popularity of messaging services, particularly among younger consumers.
In an engaging TED talk recorded recently, economist David Autor points out that in the 45 years since the introduction of Automated Teller Machines (ATMs), the number of human bank tellers doubled from a quarter of a million to half a million. No-one is arguing that previous rounds of automation caused lasting unemployment. Previous rounds of automation have involved machines substituting for human and animal muscle power. Calum Chace is an author and speaker about artificial intelligence's likely future impact on society.
This Deloitte Global report outlines a clear roadmap for you to deploy RPA within your organization. Our paper indicates that companies that are not already considering automation as a component of a broader worker ecosystem will miss significant opportunities for efficiency, quality enhancement, risk mitigation, innovation, and ultimately growth. This report, Automation is here to stay…but what about your workforce?, is the first in a series of upcoming reports looking at automation in Financial Services. Read this report to learn what automation has to offer, and how that might impact your business.
Opportunities to expand the use of machine learning in payments range from using Web-sourced data to more accurately predict borrower delinquency to using virtual assistants to improve customer service. Among the benefits are: lower servicing costs, enhanced agent performance, more efficient capacity management, improved digital customer experience, reduced risk, and elimination of waiting times. Cognitive agents like IPSoft's Amelia combine natural language and deep insight technologies to complete tasks typically handled by humans. Learn: Cognitive agents absorb data from the customer language they process, and can refer the customer to a live agent language they process, and can refer the customer to a live agent when uncertain about how to react.
While the progress being made in these projects is very impressive, the costs are significant and they pose some interesting challenges. The equivalents in the financial services are self-optimizing customer service, loan pricing, financial advice, and claims/complaints handling. Nonetheless, there are clearly areas where a degree of learning or'cognitive' technology offers a significant advantage, such as processing of paper documentation, understanding speech and detection of fraud. However, as a more general solution, it could also form part of a future wave of automation, when financial services organizations are more mature in the deployment of advanced analytics techniques and associated model risk management, and when the technologies are more mature and cost less.
While there is a lot of discussion in the popular press about general purpose AI (aka AGI - which is defined as a machine that can perform any intellectual task a person can), much less emphasis has been placed on near-term specific vertical markets or areas that AI and machine learning (ML) are likely to transform in the coming 5 years. One likely outgrowth of the inevitable rise of self driving cars, and other markets using lots of machine learning, will be the need for more efficient hardware optimized to run ML models. The rise of the robo-advisors like WealthFront and FutureAdvisor (acquired by BlackRock) shows that financial services companies are aware of machine driven portfolio management and trading. One approach to accelerating machine learning application in medicine would be to buy out an existing radiology center or clinic.
Also, another fairly new entrant is Self Lender, which gives people a way to improve their credit score, by taking out a small loan and paying it back to themselves. By comparison, the average net promoter score for banks was 32, according to an October 2016 release by Temkin Group. KeyBank offers financial wellness scores, USAA offers an app designed to help millennials save, Ally Bank offers an app designed to help people spend less, TD Bank in Canada white-labels technology from Moven, Bank of America and Citi include budgeting features in their apps, and Wells Fargo recently launched a stand-alone savings app and is creating an app for predictive budgeting. The goal is to to keep the platform and chatbot free, so the company makes money through the investment management services it offers to those who want human advice (the company has three investment advisers on staff that manage $120 million currently); it charges an assets under management fee for that.