Last year, a rather hilarious (/creepy) story about Facebook went viral across the Internet. The rumor was that company data scientists had frantically shut down a research program that went off-piste, when two A.I.’s involved created their own language and began conversing with each other in it, to everyone’s horror. Like the A.I. operatives in question, the story took on a life of its own yielding some sublimely sensationalist headlines, many going as far as professing the beginning of the end of our species… It turned out that the story had been blown completely out of proportion, as things tend to in our increasingly click-bait driven existence. That said, for many readers, that story marked their first exposure to a technology that is poised to make a huge impact on their lives, whether they realize it or not: Chatbots.
By way of definition, chatbots are computer programs that are capable of maintaining a conversation with a user in natural language, understanding their intent, and replying based on preset rules and data. Since the Facebook story, chatbots (particularly in the voice-activated category) have become more visible, with the rise of Amazon’s Alexaselling in the tens of millions, Google Home’s Assistant, Apple making a late (but characteristically stylish) entrance to the market with its HomePod, and of course the odd amusing anecdote.
As we continue our voyage through how key technologies are being implemented in the FinTech ecosystem, I decided to follow up on my last piece with a deep dive into chatbots, the most ubiquitous (and immediately promising) application of A.I. currently being used in the space today.
As the science of Artificial Intelligence itself, chatbots have been around a surprisingly long time. The first chatbot, named ELIZA, was developed by MIT professor Joseph Weizenbaum in 1966. It worked by passing words users entered into a computer which then paired them to a list of pre-coded, scripted responses. The voice assistants gaining popularity today are also descendants of a technological child of the swinging sixties – IBM’s Shoebox.
So why, you might ask are they only hitting mainstream traction 50-odd years later? The answer is twofold:
There are two basic buckets into which chatbots fall: Scripted bots and Natural Language Processing (NLP) bots. Scripted bots, the simplest form, follow predetermined paths with decision-tree answer options which the user can select from. Financial players looking to implement more complex use cases (like customer service or advanced analytics) into their business-models however, NLP bots are the key.
Taking a look under-the-hood, NLP chatbot technology is underpinned by three basic concepts:
The first two are used in structuring the bot, and the last facilitates training the application to improve over time. The details of the technology, while fascinating, are beyond the scope of this piece, but for a comprehensive and easy-to-read look at the magic of chatbots, Amir Shevat’s “Designing Bots” is a sensational choice.
Why are chatbots receiving such a warm reception in the Financial Services industry? Well, in a word: Millennials.
As we’ve discussed before, the demographic shift to a generation of consumers who’ve grown up on and had their expectations set by technology poses the biggest challenge to the Financial Services industry in a multitude of ways.
For the enterprising financial firm Chief Digital Officer, chatbots offer banks a huge range of benefits, including:
It’s these reasons (and more) that have led to their swell in popularity. Indeed, chatbots are one of the A.I. technologies that traditional banks have latched on to without much resistance.
Here are five examples that prove exactly that:
Of course, the big boys aren’t the only people heavily invested in this innovative technology. Venture-backed start-ups around the globe are in an arms race to create chatbots that solve myriad customer problems in the slickest way possible. Below are some of the front-running examples:
While the majority of the start-ups mentioned above were in the intelligent money management/personal assistant niche, the range of potential use cases doesn’t end there. It is very easy to imagine a whole host of additional segments of the customer experience where chatbots could be deployed. Off the top of my head:
The most effective bots in the future will be the ones that operate in at the intersect of A.I., analytics, and chatbots into a single platform. This will enable the “cognification” of the entire user experience. Bots this sophisticated would provide customers with personalized financial guidance, conversational self-service, and automated programs to help them successfully meet their financial goals.
The evolution of A.I. is now alive and kicking in the FinTech world, and chatbots are only a faint splash in a tidal wave of progress. That said, artificially intelligent chatbots are rapidly transforming the financial services industry, and their importance to our sector shouldn’t be underestimated…
Original Source: LinkedIn
HighRadius Autonomous Receivables Software Platform is the world’s only end-to-end accounts receivable software platform to lower DSO and bad-debt, automate cash posting, speed-up collections, and dispute resolution, and improve team productivity. It leverages RivanaTM Artificial Intelligence for Accounts Receivable to convert receivables faster and more effectively by using machine learning for accurate decision making across both credit and receivable processes and also enables suppliers to digitally connect with buyers via the radiusOneTM network, closing the loop from the supplier accounts receivable process to the buyer accounts payable process. Autonomous Receivables have been divided into 6 distinct applications: Credit Software, EIPP Software, Cash Application Software, Deductions Software, Collections Software, and ERP Payment Gateway – covering the entire gamut of credit-to-cash.