As traditional banks grapple with the challenges posed by fintechs, legacy constraints and traditional operational models, artificial intelligence (AI) is emerging as the savior. Organisations involved in the business of wealth are sparing no efforts to start to tap the potential of AI by experimenting and prototyping: intelligent digital assistants to amplify service, data-models to automate smart lending decisions, fraud detection through pattern recognition and speech/face recognition.
Throughout the financial services world, whether it is machine learning, deep learning or a series of algorithms that can crunch away into tons of big data, AI is giving the financial services sector distinct strategic advantages – ranging from breakthrough efficiencies to service differentiation advantages – to engage and connect more effectively with the one segment of humanity whose role will never be replaced by machines: customers.
According to recent research conducted by Infosys, ‘Amplifying Human Potential: Towards Purposeful Artificial Intelligence’, which assessed the impact of AI and current levels of AI maturity in enterprises, adoption is rising smartly. This has created the expectation that worldwide by 2020, companies will see AI contributing a 39% average increase in revenue and a 37% average cut in operating costs.
Fifty-six percent of financial services respondents said they had used AI at some point in the past one to three years. The study found that financial organisations invested much more in AI than other businesses (US$14.6m versus an average US$6.7m for all respondents). Somewhat contrarily, financial services ranked third from the bottom in terms of AI maturity.
While the industry’s AI outlook was positive, the study showed adoption is being held back by some reluctance to share customers’ personal data and the cost of technology. While service providers’ cost-managed transformation programmes for banks – often including phased mainframe modernisation – provide some relief from the cost perspective, the pressing issues of privacy protection and cyber security demand that policy makers and solution providers come together to frame purposeful and relevant solutions.
Opportunity vs redundancy
Job loss in the industry is also of definite concern, with one in four respondents (the highest among all verticals) saying roles automated by AI would become redundant. These concerns are understandable. Technology can become an amplifier of our human potential only if people are able to exploit and benefit from the incredible opportunities.
We can solve these issues by focusing on lifelong learning and satisfying people’s continual hunger to develop, rather than focusing only on formal education systems. History bears testimony to the fact that every technology revolution has actually augmented and amplified the work done by human beings, and driven them to adapt to the new paradigm by acquiring fresh skills.
The AI wave, in banking, will do the same by giving an opportunity for organisations to focus more on retraining, right-skilling and redeploying displaced workers to engage in more purposeful pursuits that entail problem finding, creative solutioning and innovation. These would be essential for roles that require such innately human skills for building out new products and services, determining new models to establish credit worthiness, differentiating experiences, managing risks and inclusive growth.
For traditional banking institutions under severe pressure from faster, smarter, better rivals, AI offers a lifeline to improve customer experience. Some examples – facial recognition technology is 10 to 15 times more accurate than human beings at identifying people. AI software is proving to be faster and better than service agents at responding to customer emails in contact centres. Far from depriving customer interactions of the ‘human touch’, AI is improving it with agility, accuracy and insightful intimacy.
Examples that stand out include Australia’s Westpac, which is leading its market in the use of visual recognition by enabling customers to activate new cards through their smartphone cameras. In February this year, Santander became the first bank in the United Kingdom to offer voice-activated payments. First Direct and Barclays have been using voice recognition to authenticate telephone banking customers for some time now.
When it comes to using robots in customer service, two Japanese banks – Mizuho Bank (with Pepper) and Mitsubishi UFJ (with Nao) are early adopters. While Pepper entertains customers with games and multimedia, also providing product information on request, Mitsubishi’s Nao greets customers at branches and enquires about services they might need. This is a great way to counter the country’s shrinking workforce and create greater bandwidth for the banks’ taskforce to focus on more than just the routine of customer service. For customers, the novelty of non-human agents at their service has certainly added a zing-factor to the mundane chore of banking.
No going back
Beyond the front office, AI has plenty to offer the middle and back offices as well. In credit risk management, banks are leveraging smarter algorithms produced by machine learning and prescriptive analytics to understand repayment patterns, identify tardy debtors, and predict default. In the Hong Kong-based hedge fund Aidyia, AI makes all the trades, without any human intervention or support.
A robot, costing just US$10,000-15,000 can process five to 10 times the number of insurance claims that an agent can. At PayPal, an AI engine built on open-source can not only spot suspicious transactions, it can also tell a false positive from a real case of fraud. The biggest banks in the US, JPMorgan Chase & Co, is using a machine learning programme called COIN (Contract Intelligence) to review commercial loan agreements in seconds, which a team of lawyers and loan officers would take 360,000 hours each year to do.
So, not only is there no going back on AI, there’s a very clear imperative to go fast-forward. In less than a decade, a whole new Generation Z will join the Millennials as the most important customers of banks. These customers, beyond tech-savvy, will be tech-innate, juggling five screens at a time, communicating with images, and shunning text and touch interfaces in favor of the instantaneity of voice-based commands.
Understanding and serving their needs will demand more than the average human ability. It will require man and machine to work together more symbiotically so people can then prepare for roles and jobs that don’t yet exist – like product predictors, customer-trend readers, maybe even managers of digital currency portfolios. The possibilities are only just beginning to emerge.
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