The rise of sectors like Artificial Intelligence (AI) has opened up new avenues for the banking industry and the confluence of AI and digital banking has led to the banks improving their offerings and services, notably in the field of mobile banking.
The rise of mobile banking in the last few years has coincided with the rise of Artificial Intelligence (AI) becoming more capable and pervasive in a variety of fields and applications. Although unrelated, the parallel growth in both the sectors has, of course, led to a confluence of the two, as more and more banking and finance institutions explore AI applications to improve their offerings and services. While enterprise-solutions are many – from more efficient data analytics to improved back-end workflows through automation – quite a few organisations have also explored consumer-facing applications of AI. One of the most widely adopted applications has been the consumer chat-assistant.
Simply put, a chat assistant (colloquially called a chatbot) is an AI or computer programme that conducts conversations meant to replicate human conversation via auditory or textual means. Chat assistants have come a long way from their humble origins, with advancements in machine learning and natural language processing allowing them to “learn” from their interactions and hold conversations in a human-like manner, thereby becoming more “human”. A stunning example is the Google Duplex technology displayed a couple of weeks ago at Google I/O that showcased a human interacting with Google Assistant, the AI-powered virtual assistant, without ever realising it was a bot.
As per a PwC FinTech Trends Report (India) 2017, in the last year, global investment in AI applications touched $5.1 billion, up from $4 billion in 2015. Mobile banking has grown by leaps and bounds around the world, as easy access to mobile devices has helped reshape interactions between banking institutions and their consumers. Integrating virtual chat assistants into their mobile banking offerings is the next step towards making these mobile-first interactions more seamless and natural.
While today’s mobile banking chat assistants lack the digital capabilities of Google Duplex, they still are the cutting edge of consumer-facing AI applications in banking. Here are some of the top virtual chat assistants from the global banking sector:
The Bank of America (BoA), one of the world’s largest banking institutions, has become the latest company to join the chat assistant bandwagon with the launch of Erica, an AI-driven virtual assistant to help its 25 million mobile clients. According to BoA, customers can use Erica to search for past transactions, such as checks written or shopping activity, across any one of their accounts; increase awareness about their credit scores and connect them to information that will help them learn about money management through Better Money Habits (BoA’s personal finance education tool); navigate the app and access key information, such as routing numbers or the closest ATM or financial centre; schedule face-to-face meetings with more than 25,000 specialists in the bank’s financial centres; view bills and schedule payments; lock and unlock debit cards; and transfer money between accounts or send money to friends and family with Zelle, BoA’s money transfer app-based solution.
Bank holding company Capital One launched Eno earlier this year. Eno is their text-based chat assistant, initially introduced as a pilot programme for 100,000 Capital One users, the company claims Eno can “learn” consumer behaviour over time, adapting itself to an individual user’s needs. Clients can get information from Eno about account balance, transaction history, and credit limit as an instant message and can use it to pay bills instantly. Eno can even understand emojis – not exactly cutting-edge AI, but nevertheless a big step for a chat assistant built solely to respond to text messages.
New York-based Ally Bank was one of the first global banks to introduce a virtual chat assistant, rolling out Ally Assist in 2015 as a part of the Ally Mobile Banking app. The assistant can respond to voice or text to make payments, transfers, P2P transactions, and deposits. A customer can also request an account summary or transaction history as well as monitor saving and spending patterns. Ally Bank claims that through machine learning, Ally Assist can predict customer needs by analyzing accounts and transactions to provide relevant help topics and messages while using natural language processing to understand and address common customer service queries.
A virtual chat assistant for Corporate Banking at HSBC Hong Kong, Amy can provide instant resolutions to consumer inquiries 24x7. Amy can understand English as well as Traditional and Simplified Chinese, with an embedded customer feedback mechanism helping the assistant evolve over time to answer broader and more general inquiries naturally. HSBC plans to integrate the assistant’s AI capabilities with human intervention for more complex queries while continuing to improve the platform’s AI functionality.
Hang Seng Bank introduced two chat assistants at the start of the year, HARO and DORI. HARO is aimed at answering general queries about Hang Seng’s products and services, especially the mortgage, personal loan, credit card, medical insurance, and travel insurance services, while DORI is embedded in Facebook Messenger and lets customers search for dining discounts and makes recommendations based on consumer preferences. Both assistants can communicate Chinese, English, Cantonese, and a mix of Chinese and English, and use machine learning and natural language processing to continuously improve their abilities to answer customer queries.
Aida has been working on SEB’s front-end customer service portal since early 2017, and currently handles about 13 percent of the bank’s IT support questions, as well as helping bank customers with card issues, account queries, and booking meetings. SEB has admitted that Aida has faced difficulties with natural language processing on account of the complexity of Nordic languages, but the bank hopes to improve on the situation in the future. A possible solution is to use stored consumer data to better understand and answer user requirements.
The Commonwealth Bank of Australia (CBA) debuted Ceba in January this year – a chat assistant capable of assisting customers with more than 200 tasks, including card activation, account balance inquiries, payments, cardless cash withdrawals, etc. Using machine learning, natural language processing, and conversational AI techniques, CBA aims to be able to use Ceba to answer over 500,000 common consumer enquiries about more than 500 banking activities, by the end of the year.
The State Bank of India, India’s largest public-sector banking institution, embraced AI in a big way with the introduction of the State Bank Intelligent Assistant (SIA) in September 2017. Developed by Silicon Valley and Bengaluru-based startup Payjo, SIA is aimed at helping the bank’s customers with everyday banking tasks as well as answering their queries. According to Payjo, SIA can handle up to 10,000 enquiries every second, or nearly 864 million queries in a day. Using machine learning and a large set of commonly asked questions, SIA is one of the world’s largest deployments of AI in consumer-facing banking.
Bengaluru-based Senseforth AI Research partnered with HDFC to launch Eva in March this year. According to a statement by the bank, since its launch, Eva (which stands for Electronic Virtual Assistant) has addressed over 2.7 million customer queries, held 1.2 million conversations, and interacted with over 530,000 unique users from 17 countries. HDFC says, “Eva...becomes smarter as it learns through its customer interactions. Going forward, Eva would be able to handle real banking transactions as well, which would enable HDFC Bank to offer the true power of conversational banking to its customers.”
ICICI Bank launched its AI-based virtual chat assistant iPal in February this year. The bank says that since its launch, iPal has interacted with 3.1 million customers, answering about 6 million queries with a 90 percent accuracy rate. iPal covers three broad applications – answering FAQs or common queries from consumers for bank executives; conducting financial transactions like P2P fund transfers, bill payments, etc.; and how-to tasks such as resetting ATM pins. ICICI has said that it is in the process of integrating iPal with existing voice assistants like Cortana, Siri, and Google Assistant, calling it a “natural progression”.
These chat assistants and their ilk have barely scratched the surface of the possibilities for consumer-institution interactions. Dhananjaya Tambe, Deputy Managing Director and Chief Information Officer, State Bank of India, says, “I like to think of chatbots in four generations. Gen-I is the earliest chatbots which were basically informational assistants that people could use to seek information in response to basic pre-programmed commands. Gen-II chatbots can conduct basic transactions that require low-security clearance, like transferring money to pre-added beneficiaries or topping-up your own mobile. Gen-III chatbots are able to combine with biometric capabilities to perform more secure transactions, like sending remittances or making payments to unregistered beneficiaries.”
When asked about the future, Dhananjaya adds, “Gen-IV is where we’re likely heading – chatbots powered by state-of-the-art voice recognition technology, so that the assistant can recognise me beyond doubt based on my speech mannerism and voice patterns, and conduct all types of transactions. Voice assistants like Siri, Google Assistant, and Alexa are already getting there, and once we find a way to address the extra security needed for financial and banking transactions, voice-based banking assistants that you could simply call and talk to, the way you talk to a teller at the bank counter, could soon follow.”
As the tech giants like Google, Microsoft, and Apple work towards making their AI assistant offerings more robust, the market for dedicated chat assistants to help consumers with their banking activities is only going to grow. A number of players have begun to explore the vast potential of this sector, but there still remains a lot to explore for startups and entrepreneurs interested in the space.
Have you interacted with a chat assistant for banking? What was the interaction like? Did we miss a virtual chat assistant you’ve used before? Let us know in the comments!