With digital banking taking new age dimensions and moving beyond the standard automation and user experience , there is a notable shift in the rise of investments in intelligent systems across the globe . 2017 is predicted to be the real game-changer where the bots are going to greet , guide and operate the bank.
Geared up for Botsification
With 11,000 bots live on Facebook messenger alone and 23,000 developers having signed up for building bots this year , get ready to chat with your service providers.As per Gartner 38% of consumers have already used a virtual assistant for services on their mobile phone in 2016 . The bots in fintech are typically either goal-based dialog agents or chatbots. This is the year of finbots
Over the last 2 years the Bot builders have reached a certain state of momentum if not maturity and therefore today ‘botsification’ of processes is no longer sounding far-fetched or experimental. Google , Facebook , Microsoft , Apple and every company that matters has invested heavily in bots and now have bot building frameworks ready for mainstream adoption.
Is Banking Ready for ‘Botsification’?
Banking as an industry is not a leader in the adoption of chatbots but there is a conscious decisioning if not adoption process underway in most banks with a careful and definitive digital strategy. Healthcare in the US , has seen the maximum advancement in mainstream operations , with bots playing a major role in the transcription process. Most of these conversational interactions with a bot are now HIPAA compliant with mainstream service providers stepping up on the compliance front. Banking is yet to enforce Regtech in to the ‘botsification’ arena and is toying with dialog agents and customer service chatbots as its step one into the world of adoption of AI . However , the use cases or area of engagement in most banks is clear and evolving.
Banks that have Invested Already
Santander UK introduced the Santander Smart Bank app where customers could speak to their mobile phone and ask queries related to their accounts . Future versions promised to have voice enabled payments and reporting of lost or stolen cards. Very ambitious! However, I have my reservations on bot enabled payments because artificial intelligence as a technology relies greatly on historic data and pattern recognition. Unless there is security to match up this may be a step to premature.
In April last year , Swedbank adopted Nuance’s Nina as its intelligent virtual assistant based on NLU technology. Nina offers a conversational experience to Swedbank’s mobile customers . Nina offers its chatbot primarily as a healthcare transcription engine . We are yet to measure the nature of success Nina can offer Swedbank .
Erica , Bank of America’s chatbot due for launch in 2017 is a shift away from the conversational bots where the conversation is initiated by the consumer . Instead , Erica can drive conversations . BoA promises ‘Questions , Answers, Insights’. Insights is what is intriguing the community. Erica is going to be able to guide a user on spending habits and also track credit score. Experts such as Chris Gledhill think Erica might be the ‘coolest’ entrant if industry rumors are to be believed.
Closer to home , HDFC launched it OnChat platform through Niki.ai’s chatbot integration last week. Niki is a great platform to start building your services but its NLU capabilities cannot yet be commented on.Measurable goals for such initiatives in terms of business and engagement are yet to be ascertained as experimental or mainstream.
The DBS Digibank’s Mykai powered by Kasisto is perhaps one of the most conversationally mature bots to have entered the arena this year. Not surprising as Kasisto is a spin-off from SRI International that powers Apple’s SIRI. Mykai is currently available on app stores in the US.
|Bank / FI||BOT||Tech Vendor||Phase|
|Santander UK||SmartBank||Nuance and built in assistane with Santander Universities||Live|
|Bank of America||Erica||Homegrown||To go live in 2017|
|DBS , Digibank||MyKai/Kai||Kasisito spun off SRI International(makers of Apple’s SIRI)||Live|
|RBS||Luvo||IBM watson||Was to launch in December with 10% of Scotland customers|
|Axis Bank||Not known||Active Intelligence PTE
|Announced its award of contract to the tech vendor|
|ABSA||Not known||Not known||Announced intent|
|Bank of Tokyo Mitsubishi||Nao. This is not a chatbot. It is a pint sized interactive robot||Aldebaran Robotic (Softbank Robotics)||Early 2015
|Mizuho||Pepper.||Aldebaran Robotic (Softbank Robotics)|
|Yes Bank||Payjo||To Launch in 2017|
|RBL||Payjo||To Launch in 2017|
The Bots will Grow Up Over Time
It is going to be interesting to see how the bots grow up over time. The investment in these bots is also with the intent that artificial intelligence is a continuously learning technology. The more you talk to your bot, the more intelligent it gets . Well , the historic embarrassment that TAY caused Microsoft merely 16 hours post its twitter launch will ensure banks invest in technology to monitor and control bot activity . While budgeting for the investment in bots , it is equally important to invest in companies who are working on technology that empowers and secures the bot.
Generative , Hybrid or Heuristic ?
While the tech providers today are using primarily the ‘Retrieval Based Model’ , it is foreseen the Chatbots build on the ‘Generative Models’ will survive the test of time.
A Generative Model takes as input the user message and the previous message to generate responses. It urges you to talk more to it and divulge more information , before it responds.
On the other hand , the Retrieval Based Model relies primarily on a database of responses – it assimiliate context , user message and retrieves the closes pre-defined response through syntactical algorithms .Context may include a position in the dialog tree. In case it does not use context , it will respond only basis the last message ensuring stateless retrieval.
Alternately , there is also the Pattern Based Heuristic Model which engineering responses . The chatbot traverses through multiple patterns prior to responding.
Finally , a hybrid model that uses a combination of generative for intent classification and retrieval based for entity categorization , pass them through a heuristic model (or rule engine ) for response
Usability drives Adoption
The ultimate test which is goes beyond the Turing Test is that of a user’s acceptance of the offered conversational experience. Will it be dismissed as an annoying addition to the mobile phone or a trusted personal assistant , only time and technology will tell.