Chatbots, computer programs that typically use text-based live chat as an interface to carry out tasks for customers on behalf of the business, are emerging as an inexpensive way to introduce artificial intelligence (AI) in banking.
New digitally savvy companies have found success attracting consumers with user-friendly offerings, while legacy banks are finding it difficult to invest in and adopt innovative products. To remain competitive, these large banks will have to adapt their traditional services by incorporating more robotics in banking that will attract more tech-savvy customers.
Chatbots in Banking
Chatbots in banking are a digital solution that is relatively inexpensive to develop and maintain. For starters, chatbots require less coding than standalone banking apps. And the current growth in popularity of messaging platforms saves banks the cost of developing their own channels, as well as saving on data storage thanks to chatbots’ cloud-based systems.
Companies such as Cleo, Stripe, and Wealthfront are giving traditional banks a run for their money. However, for these players it is more difficult to meet the demand of key bank products (such as loans) due to less restricted regulations that force their customers to spend heavily on compliance and maintain large capital cushions.
DBS uses Kasisto’s Kai, the underlying technology of MyKai, to allow customers to conduct transactions such as transfers and bill paying. Furthermore, they can ask about their personal finances using messaging applications such as Facebook Messenger and eventually WhatsApp and WeChat, all of which are the top messaging applications used across the world.
In 2016, Swedbank launched on its website and mobile application Nuance’s NINA, who helps answer customer inquiries more quickly by sourcing information relevant to their query using intuitive analysis.
Chatbots in Finance
The finance industry is built on processing information, which makes it an ideal industry for automation and reduction of salary expenditure, according to a new report from PwC. However, two-thirds of US financial services respondents said that they’re limited by operations, regulations, budgets, or resources to make the investment in such innovative development.
Fintech companies such as Plum, Digit, and Cleo use chatbots that drive microsaving by putting small amounts into savings each day for their users. These companies’ chatbot is their core product, unlike legacy banks that use it to supplement a core product.
These companies are improving various financial services that provide their customers more than just automated savings. Chatbots can provide wealth management for the masses, underwrite loans and insurance, provide data analyses and advanced analytics, and detect and notify of fraudulent behavior, all through an automated virtual assistant.
Bank of America uses ERICA to give customers key and real-time updates on their finances using a channel of their preference. Her predictive analytics and cognitive messaging helps customers make payments, pay down debts, and check their balances.
Chatbots Set to Grow
Although chatbots have been around for a long time, recently the underlying AI technology has made waves in the market.
BI Intelligence, Business Insider’s premium research service, has found that the technological advancements in AI has made leaps and bounds in recent years in financial services.
The growing popularity of messaging apps have made them reliable hosts for chatbots, and the increasing public acceptance of chatbots have created more trustworthy relationships with users, particularly for millennials, whom banks are trying to target.
“Gartner’s research chief couldn’t have opened the company’s flagship conference with a more astounding proclamation if he had claimed that next year’s event would be held on the International Space Station and Gartner was offering free rides.”
Actually, I agree with Peter – I wrote a whole book, Silicon Collar which looks at a century of automation and how humans go through panic attacks every couple of decades about automation and impact on jobs. Automation tends to target tasks, not complete jobs. In general, it transforms jobs, not destroy them. And societies have “circuit breakers” which slow down rapid mass adoption of automation technology as I wrote here.
What I would I have liked to hear from Peter was “we were too pessimistic just 3 years ago”, when he said from the same podium
“Gartner predicts one in three jobs will be converted to software, robots and smart machines by 2025…New digital businesses require less labor; machines will make sense of data faster than humans can. By 2018, digital business will require 50% fewer business process workers.”
And I would liked him to say “we really fxxked up” when we predicted that by next year (2018)
20 percent of business content will be authored by machines.
more than 3 million workers globally will be supervised by a “robo-boss.”
45 percent of the fastest-growing companies will have fewer employees than instances of smart machines.
In contrast, Oracle Co-CEO Mark Hurd shared with the OpenWorld audience a few hours later some of the “mean tweets” as he called it about some of the predictions he has been making about the cloud market.
Later in a Q&A, I joked with Mark that as an industry analyst he would have the luxury of hedging and assigning a probability to his predictions and then never publicly having to audit or redact his predictions.
In 1900, 30 million people in the United States were farmers. By 1990 that number had fallen to under 3 million even as the population more than tripled. So, in a matter of speaking, 90% of American agriculture workers lost their jobs, mostly due to automation. Yet somehow, the 20th century was still seen as an era of unprecedented prosperity.
In the decades to come, we are likely to see similar shifts. Today, just like then, many people’s jobs will be taken over by machines and many of the jobs of the future haven’t been invented yet. That inspires fear in some, excitement in others, but everybody will need to plan for a future that we can barely comprehend today.
This creates a dilemma for leaders. Clearly, any enterprise that doesn’t embrace automation won’t be able to survive any better than a farmer with a horse-drawn plow. At the same time, managers need to continue to motivate employees who fear their jobs being replaced by robots. In this new era of automation, leaders will need to identify new sources of value creation.
Identify Value At A Higher Level
It’s fun to make lists of things we thought machines could never do. It was said that that only humans could recognize faces, play chess, drive a car, and do many other things that are automated today. Yet while machines have taken over tasks, they haven’t actually replaced humans. Although the workforce has doubled since 1970, unemployment remains fairly low, especially among those that have more than a high school level of education. In fact, overall labor force participation for working age adults has risen from around 70% in 1970 to over 80% today.
Once a task becomes automated, it also becomes largely commoditized. Value is then created on a higher level than when people were busy doing more basic things. The value of bank branches, for example, is no longer to manually process deposits, but to solve more complex customer problems like providing mortgages. In much the same way, nobody calls a travel agency to book a simple flight anymore. They expect something more, like designing a dream vacation. Administrative assistants aren’t valuable because they take dictation and type it up on a typewriter, but because they serve as gatekeepers who prioritize tasks in an era of information overload.
So the first challenge for business leaders facing a new age of automation is not try to simply to cut costs, but to identify the next big area of value creation. How can we use technology to extend the skills of humans in ways that aren’t immediately clear, but will seem obvious a decade from now? Whoever identifies those areas of value first will have a leg up on the competition.
Innovate Business Models
Amazon may be the most successfully automated company in the world. Everything from its supply chain to its customer relationship management are optimized through its use of big data and artificial intelligence. Its dominance online has become so complete that during the most recent Christmas season it achieved a whopping 36.9% market share in online sales.
So a lot of people were surprised when it launched a brick and mortar book store, but as Apple has shown with its highly successful retail operation, there’s a big advantage to having stores staffed with well trained people. They can answer questions, give advice, and interact with customers in ways that a machine never could.
Notice as well that the Apple and Amazon stores are not your typical mom-and-pop shops, but are largely automated themselves, with industrial age conventions like cash registers and shopping aisles disappearing altogether. That allows the sales associates to focus on serving customers rather than wasting time and energy managing transactions.
When Xerox executives first got a glimpse of the Alto, the early personal computer that inspired Steve Jobs to create the Macintosh, they weren’t impressed. To them, it looked more like a machine that automated secretarial work than something that would be valuable to executives. Today, of course, few professionals could function without word processing or spreadsheets.
We’re already seeing a similar process of redesign with artificially intelligent technologies. Scott Eckert, CEO of Rethink Robotics, which makes the popular Baxter and Sawyer robots told me, “We have seen in many cases that not only does throughput improve significantly, but jobs are redesigned in a way that makes them more interesting and rewarding for the employee.” Factory jobs are shifting from manual tasks to designing the work of robots.
Lynda Chin, who co-developed the Oncology Expert Advisor at MD Andersonpowered by IBM’s Watson, believes that automating cognitive tasks in medicine can help physicians focus more on patients. “Instead of spending 12 minutes searching for information and three with the patient, imagine the doctor getting prepared in three minutes and spending 12 with the patient,” she says.
“This will change how doctors will interact with patients.” she continues. “When doctors have the world’s medical knowledge at their fingertips, they can devote more of their mental energy to understanding the patient as a person, not just a medical diagnosis. This will help them take lifestyle, family situation and other factors into account when prescribing care.”
Humanity Is Becoming The Scarce Resource
Before the industrial revolution, most people earned their living through physical labor. Much like today, many tradesman saw mechanization as a threat — and indeed it was. There’s not much work for blacksmiths or loom weavers these days. What wasn’t clear at the time was that industrialization would create a knowledge economy and demand for higher paid cognitive work.
Today we’re seeing a similar shift from cognitive skills to social skills. When we all carry supercomputers in our pocket that can access the collective knowledge of the world in an instant, skills like being able to retain information or manipulate numbers are in less demand, while the ability to collaborate, with humans and machines, are rising to the fore.
There are, quite clearly, some things machines will never do. They will never strike out in Little League, get their heart broken, or worry about how their kids are doing in school. These limitations mean that they will never be able to share human experiences or show genuine empathy. We will always need humans to collaborate with other humans.
As the futurist Dr. James Canton put it to me, “It is largely a matter of coevolution. With automation driving down value in some activities and increasing the value of others, we redesign our work processes so that people are focused on the areas where they can deliver the most value by partnering with machines to become more productive.”
So the key to winning in the era of automation, where robots do jobs formerly performed by humans, is not simply more efficiency, but to explore and identify how greater efficiency creates demand for new jobs to be done.
When it comes to beginning the robotic process automation (RPA) journey, many organisations are preconditioned to believe that the first step is to purchase and install the software to ensure it works. Time and time again, the same outcome is achieved: the software installation appears to be successful.
However, the process of proving the software works only delays the organisation from using the technology to achieve key business goals, streamline processes and alleviate employees of mundane, rules-based work.
Rather than waste time with a “proof of concept,” organisations should begin by performing a “proof of value.” This prompts the business to identify where automation will drive the most value and how it can be configured to solve critical problems and set the business up for greater success (and competitive advantage).
Here are five steps every organization must follow to implement RPA and prove its value based on unique, individual business needs:
1. Look for people, not processes
RPA is not built to address the same business problems across varying organisations; what may be used as a payroll tool for one business can also be used to streamline HR on-boarding processes for another.
In many scenarios, organisations will first identify various processes that they’d like to automate based on the success cases of other businesses. Not only does this complicate RPA implementation, but it also masks the true value of automation, forcing it to be applied to areas that aren’t necessarily organisational pain points.
Perhaps ironically, a business should first think about its human employees when considering a new software deployment. Where are people being forced to perform non-value adding work (i.e., structured data entry or invoice processing)?
What needs to change so employees can pursue the judgement-based roles they’re more suited for – like creative problem solving, strategising and critical thinking? These are the processes that will benefit the most from automation and will help solve the challenges the organisation is facing.
2. Ask the experts
Once businesses identify the processes to be automated, the next step is to sit down with experts who can determine the areas where configuration will be complex. The experts will work with the employees who perform the tasks that will be automated to discuss how the process works and where the tool will be deployed. The experts will also train employees to manage the tool and ensure it remains consistent in performing the task at hand, making future deployments more efficient.
3. Map the impact
Using the experts’ advice and insight, organisations should design a model that depicts the business structure and processes that will be most affected by RPA. It’s important to identify the ripple effects – starting from the internal resources, applications and systems, and ending with the organisation’s external stakeholders.
This process accounts for the time required to implement RPA and the benefits of doing so – specifically in allowing employees to pursue more value-adding roles. Having the impact mapped will also identify areas within the organisation that can use the additional resources that will be available thanks to automation.
4. Calculate the cost (and savings!)
RPA implementation doesn’t happen overnight; it’s important that businesses set expectations on the time required to both get the tool up and running and start seeing benefits and change.
However, it is possible to calculate and forecast the financial model attributed to RPA, including the cost to maintain and update the solution, as well as the savings that the transformation will bring.
5. Present the proposal
With a clear strategy, expert assistance and financial business case in hand, the final step for organisations is to present the benefits of RPA implementation so it will be prioritised over other projects.
With the monetary savings, increased employee satisfaction and scoped transformation, the benefits of automation in helping the business solve organisational challenges and drive value will be clear. Once the project is approved, organisations can move forward with implementing the solution and be prepared to see the ROI.
While it’s a simple task to prove that RPA works, identifying how it can bring transformational benefits to an organisation is the key to successful deployment. It doesn’t have to take long – the assessment of suitable processes, design and forecasting the implementation plan and production of a business case can be completed in less than six weeks.
Using these five steps will provide organisations with the strategy, sponsorship and access to resources to prove RPA’s value and ensure it gets successfully implemented.
“Cobots”, or collaborative robots, are making inroads into work previously considered too difficult to automate. But as cobots get better at performing tasks such as material handling or packaging, their designers are having to consider the effects on their colleagues of the machines’ improved ability to interact with humans.
In its early stages, this new technology has been safe if underwhelming, says David Mindell, a professor at Massachusetts Institute of Technology. Of the cobots, he says: “They don’t do much collaboration, but at least they won’t cut your head off.”
Small, light and slow moving, cobots are generally harmless — the sensors and machine-learning software that enable them to “understand” their environment have a simple override: if a human gets too close, they are programmed to shut down.
The first job has been to design the software models to allow robots to operate in the human world, says Manuela Veloso, head of machine learning at Carnegie Mellon’s School of Computer Science. “It’s very important to be able to envision a mobile creature moving around in our space,” she says. For instance, getting machines to work alongside people will require an understanding of “safety zones” of the body: “We’re trying to model a person. You don’t want to hit an eye — an elbow is less important.”
As the software becomes more sophisticated, it promises more flexible machines that can be released from their cages. “We’ve got people doing jobs today because the regular robots can’t do it,” says Jim Lawton, head of product and marketing at Rethink Robotics, a Boston-based maker of cobots. These often involve repetitive actions that strain human limbs, are mind-numbingly dull and consign workers to jobs with no chance of career advancement, he says.
Mindell, author of Our Robots, Ourselves, a 2015 book about human-robot interaction, agrees there is much to be gained in the way of worker wellbeing: “If your work is truly about to be augmented, or made less dangerous or less straining, these are good things.” But he says that limits in both the technology and imagination on how to apply it have made this more promise than reality.
Designing complex interactions between robots and people will take a change in mindset, he says, adding that the history of automation has largely been about treating humans like robots, to fit into automated processes. “The computer science world still has a long way to go before it has a clue about how to deal with people,” he says.
At a simple level, makers of cobots are working to reduce the sense of weirdness for people working alongside machines whose level of intelligence they find hard to judge. Rethink, for instance, experimented with putting smiling mouths on its robots to make them seem more “human”. The result was the opposite, says Lawton: people thought the machines were smirking at them, and found them “arrogant and condescending”. Moving into the “uncanny valley” where robots start to copy humans too closely “spooked people”, he says.
Veloso says there are hurdles that will have to be overcome to improve the human experience of working with the machines. One is that the machines have to be more understandable. “The more humans infer what a robot will do next, the safer it will be,” she says.
Rethink’s answer has been to give its robots “eyes” (an image on a tablet computer) that indicate the direction the machine is about to move in — a simple way to prepare people around them that they are about to do something, says Lawton.
The computer science world still has a long way to go on how to deal with people
Another key is to design a form of robot-human symbiosis in which each helps the other achieve its goal, says Veloso. That will mean teaching people to respond to requests from the robots, or to anticipate their needs, as much as the other way around. As interactions like this become more subtle and machines take over more work alongside people, the long-term impact on the wellbeing of human workers is hard to predict. Against the obvious benefits of taking dangerous or tedious work away from people, there may be unexpected side-effects. “When people invented keyboards, they weren’t imagining carpal tunnel syndrome,” Veloso points out.
As more automation creeps in, there may be subtle but far-reaching effects on the way work is designed. There is a fear that the iterative process improvements that are a product of lean manufacturing — constantly learning and implementing better ways of working — may be threatened, says Lawton. If existing work processes are automated, the result could be an ossific9ation that prevents this steady improvement.
Like much technology whose benefits are clear in the short term, even if their long-term effects on human wellbeing are hard to judge, the advance of the cobots is unlikely to be slowed. People are likely to take to their new robot colleagues as enthusiastically as they took to their smartphones, says Mindell. “People have their fears — in some ways, they are legitimate fears,” he says. “At the same time, they are addicted to their technology.”
‘Algorithms took our jobs’
Tom Gordon was 45 when his lucrative career as an oil trader suddenly faced a new threat. Electronic trading, which originally had been introduced to expand trading capacity overnight, was now operating head-to-head with Gordon and his colleagues on the floor of the exchange during the day.
Gordon says he used to handle between 500 and 750 trades a day. In his nearly 25 years as a trader he recalls recording only two months of losses. But even the high volumes that a successful trader like Gordon could handle were quickly overshadowed by the volumes electronic systems were capable of processing.
For Gordon, working alongside the electronic market was like being hit by a truck. “I saw the transition was coming and knew [traders] were going to get run over,” he says. He eventually left and retrained as a social worker.
He was wise to do so, because a few years later, in 2016, CME Group, which owns the New York Mercantile Exchange (Nymex), closed the last of its remaining commodity-trading pits.
Gordon says some of his former colleagues have struggled to cope in their new lives. “Some have done quite well, but for many of the people it really broke their lives and their spirit.”
Losing a job to a machine or algorithm carries a unique psychological burden, says Marty Nemko, a psychologist and career counsellor.
No training exists that can help a human match the speed and efficiency of artificial intelligence. “There is an inevitability of [one’s] inferior ability that accrues,” Nemko says.
Tim Leberecht, a consultant on business leadership, agrees: “If we lose our jobs due to automation and can’t get back into the workforce, then there is this huge void of purpose and meaning.”
“The big issue with this fourth industrial revolution is that we don’t have the social institutions that are facilitating and enabling the transition,” says Ravin Jesuthasan, managing director at Willis Towers Watson, and leader of the consulting group’s research area, “Future of Work”.
Research on the threat of automation paints a complicated picture. A 2016 OECD report found an average of 9 per cent of all jobs across the 21 countries the research covered could be automated, given current technology. A report by consultants McKinsey puts the global figure at less than 5 per cent.
Many researchers suggest the more nuanced effect of this transition will be on the handful of tasks across all sectors that are routine and repetitive.
According to another McKinsey report, more than 70 per cent of tasks performed by workers in the food service and hospitality sector could be carried out by machines. In manufacturing, nearly 60 per cent of tasks in jobs such as welding and maintaining equipment are at risk.
Higher-paying jobs are not immune from the disruption. McKinsey found that up to 50 per cent of tasks in the financial services industry could be automated, as could about a third of jobs in healthcare.
Jesuthasan says this refocusing of tasks can give people the space to do more meaningful work. “Leaving behind all of those routine things [creates] a huge emphasis on creativity and empathy and care,” he says.
After witnessing his original job as a trader vanish, it is perhaps no surprise that Gordon has found himself engrossed in work requiring these human characteristics. “I want to do my part,” he says. “Will I make a difference? I don’t know, but I’m going to give it a shot.”