If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Your phone or car answering your questions doesn't sound insane anymore. AI is entering our everyday lives. We can ask a computer to order a pair of Converse sneakers, book a hotel, or schedule a romantic dinner with our spouse. Shouldn't we also be able to pay for stuff with our voices? Industries from eCommerce to banking harness voice technologies with every new piece of software released.
Worldwide spending on artificial intelligence (AI) systems is forecast to reach US$35.8 billion in 2019, an increase of 44.0% over the amount spent in 2018. With industries investing aggressively in projects that utilise AI software capabilities, the IDC Worldwide Semiannual Artificial Intelligence Systems Spending Guide expects spending on AI systems will more than double to $79.2 billion in 2022 with a compound annual growth rate (CAGR) of 38.0% over the 2018-2022 forecast period. Spending will be led by the retail industry where companies will invest $5.9 billion this year on solutions such as automated customer service agents and expert shopping advisors & product recommendations. Banking will be the second largest industry with $5.6 billion going toward AI-enabled solutions including automated threat intelligence and prevention systems and fraud analysis and investigation systems. Discrete manufacturing, healthcare providers, and process manufacturing will complete the top 5 industries for AI systems spending this year.
At a time when consumers are doing more and more on digital platforms, they also have an increasing desire for human-like conversations that can resolve issues, provide advice, help them navigate increasingly complex platforms, and make life simpler. No longer are simple chatbots the desired resolution. Consumers want contextual engagement that reflects their situation at a specific point in time and allows them to make the choice between machine or human engagement. Conversational banking is an extension of the chatbots that were originally used to respond to the most basic of inquiries. As data availability, analytic capability and digital technology improve, a more enhanced form of digital engagement will become both personalized and scalable.
It wasn't so long ago that all banking was done in a branch, by phone, via an envelope or at an ATM. Then came the personal computer, and online banking became popular because of its convenience and ability to track balances, which used to require a branch visit and updating a passbook. Today, most transactional banking is done on mobile devices. The smartphone's dominance in banking and of consumers' communication, social, entertainment, health, shopping, scheduling and research lives is the perfect example of digital integration done right. Today's smartphone replaces separate music players, cameras, game consoles, fitness trackers, personal digital assistants and a phone.
In 1997 when Deep Blue, a supercomputer, beat the then chess champion, Garry Kasparov, we all were taken aback. That was more than 20 years ago and most of us did not even know that computers like that even existed. In late 2017, AlphaZero taught itself how to play chess under just four hours and beat the world's then best chess-playing computer program. Remember, AlphaZero, the game-playing AI created by DeepMind, was not taught any domain knowledge but the rules of the game. Such is the power of machines to learn and improvise and industries across the world are tapping a machine's ability to learn and improve from its experience without being explicitly programmed.
Retail banks have long competed on distribution, realizing economies of scale through network effects and investments in brand and infrastructure. But even those scale economies had limits above a certain size. As a result, in most retail-banking markets, a few large institutions, operating at similar efficiency ratios, dominate market share. Changes to the retail-banking business model have mostly come in response to regulatory shifts, as opposed to a purposeful reimagining of what the winning bank of the future will look like. Retail banks have also not kept pace with the improvements in customer experience seen in other consumer industries.
When it comes to analytics tools, data scientists have a plethora of options available to them. Features that may appeal to one data scientist don't necessarily work for another. When it comes to offerings from H2O.ai, users expressed different reasons for their choices. Last week, Datanami was a guest at H2O.ai's annual user conference, called H2O World, and had a chance to talk with several customers, including Ruben Diaz, a data scientist with Vision Banco, and Bharath Sudharsan, director of data science and innovation at Armada Health. Vision Banco is one of Paraguay's largest banks, with consumer and micro-finance lines of business.
Never before has the importance of technology been greater in financial services. Competition from fintech firms and big tech giants, increased expectations from the consumer, and new innovations connecting data to digital delivery are requiring banks and credit unions to embrace new technologies in order to build winning strategies. Here are some of the most important technologies banks must focus on this year and in the foreseeable future. These are in no particular order, since each organization will be different as to the prioritization and investment allocation. Suffice it to say, however, than none should be ignored.
Whether through chatbots or voice assistants, bank customers are beginning to experience Banking AI and are responding favourably. Initially, the focus of these interfaces is similar to the task-oriented nature of channels that banks already provide. Though conversational interfaces have yet to attain the level of adoption expected in the banking space and has not yet matured as mobile has, there are already a variety of approaches for establishing a conversational banking capability. Going forward, I believe there will be at least four distinct banking models supported by apps and conversational interfaces. The majority of banks will begin their journey in the conversational banking space by implementing a conversational interface as an additional channel with a task-based focus.
Natural language processing, or NLP, is one AI-based technology that's finding its way into a variety of verticals. We covered the business applications of NLP and where it comes into play in finance broadly in our previous reports. We intend to cover the technology's applications in banking specifically in this report. NLP might allow a company to garner insights that can be used to assess a creditor's risk or gauge brand-related sentiment across the web.