All the new features offered by Google Assistant and other AI agents.
Our relationship with technology continues to evolve. Machines are slowly starting to learn and adapt to their environments. AI has long been considered the realm of science fiction, but as technology improves, AI is becoming a reality today. One of the most striking example came this month from Google during their annual developer conference. Google’s virtual assistant can now make phone calls on your behalf to schedule appointments, make reservations in restaurants and get holiday hours. The robotic assistant uses a very natural speech pattern that includes hesitations and affirmations such as “er” and “mmm-hmm” so that it is extremely difficult to distinguish from an actual human phone call. People will soon have a choice of choosing from six voices, including one of musician John Legend, to talk to “Google Assistant”.
On the other end, Amazon’s Alexa is already being integrated in number of services including by banks. the US Bank personal customers can now speak to Amazon Alexa to complete their basic banking tools. Available features include checking bank balance, paying bills and viewing transaction history. Similar featured are also now offered by American Express, US investment bank Fidelity, Capital One. In UK RBS has also been working on voice interface based on Alexa, whereas PayPal and Barclays have deployed solutions based on Apple’s Siri payments.
AI In Financial Services
In Financial Service there is already a wide adoption of chatbots and many banks are developing robo-advice propositions. While advanced learning machines may replace low-skill jobs, AIs will be able to work collaboratively with human professionals to solve intensely complex problems. AI stands to become one of the most disruptive forces across many industries. Last year Scandinavian Nordea bank has deployed chatbot with the mission of providing customers with a better experience, speaking up customer queries response timed allocating customer questions and problems to the right agent. The solution can analyse hundreds of messages per second, opening up a broad range of applications. The chatbot has been introduced initially to answer basic queries and refer customers on to human colleagues for more complex questions. Over time, however, the bot is becoming more intuitive as it learns on the job, building more knowledge about how to interpret and respond to customers’ queries.
JPMorgan Chase has invested in technology and recently introduced a Contract Intelligence (COiN) platform designed to “analyze legal documents and extract important data points and clauses.” Manual review of 12,000 annual commercial credit agreements normally requires approximately 360,000 hours. Results from an initial implementation of this machine learning technology showed that the same amount of agreements could be reviewed in seconds. ABN AMRO, a Dutch bank from Amsterdam, heavily invested in another use cases for machine learning – combating fraud and improving compliance. The technology is ideally suited to the problem as machine learning algorithms can comb through huge transactional data sets to spot unusual behaviour. AI can spot the anomalies or patterns in transactions which might indicate fraud and money-laundering.
Almost every big consultancy or think tank has published research on how AI will transform banking. KPMG went one step further with its vision of an ‘invisible bank’ where “enlightened virtual assistants” replace people at all points of customer interaction.
IBM and Royal Bank of Scotland delivering a state of art digital virtual agent.
At Royal Bank of Scotland has partnered with IBM to pilot a life-like avatar called Cora. The project aims to help customers with basic queries and giving its digital banking drive a more human face. Cora is currently able to answer basic questions such as “How do I login to online banking?”, “How do I apply for a mortgage?” and “What do I do if I lose my card?”. It could help cut down on waiting times because it would be able to deal with simple problems, adding that Cora’s AI skills would eventually expand to answering hundreds of different questions, even detecting human emotions and reacting verbally and physically with facial expressions. The technology comes from New Zealand-based firm Soul Machines, which uses biologically inspired models of the human brain and neural networks to create a “virtual nervous system” for its digital humans that can detect human emotion and react verbally as well as physically through facial expressions.
Banks are using virtual agents to interact with customers and solve problems before offering an added-value human support. Natural language processing and generation make it increasingly difficult for customers to tell whether they are talking to a human or an AI interface. The latest example from Google digital assistant is setting benchmark high and it is just a matter of time when high-end AI driven virtual agents will be supporting customers in all major banks, having first validated customer identity through voice or facial recognition (instead of login passwords to ensure security). It is a win-win service where bank manage to service low-value processes by AI (with low operating costs) and customer scan received an accurate and fast advice, guidance or end-to-end administrative service less prone to human errors. .
AI helps companies drive personalised communications and offering based on detailed profiling of each customer. AI engines can use the vast mass of unstructured data on each person to profile customers. Machine learning – computers which can learn from data – can then be used to analyse behaviour patterns. Algorithms could also automate increasing numbers of decisions and are already an engine in many robo-advice investment propositions.
There has been a growth in the number of companies developing specific AI solutions, including customer servicing and communications, data cleansing, customer profiling, data analytics and modelling. In next few years AI will be a technology that enables a ‘zero user interface’ future and lead is to Web 3.0 – an internet model where information is accessed, analysed and understood by both humans and AI learning machines. Companies will be able to analyse much larger number of data points to produce high quality insights beyond the capabilities of traditional business intelligence, providing quick, tailored and cost effective services across industries.