We are on the cusp of Artificial Intelligence (AI) becoming the norm, thanks to the falling cost of computing power, the availability of huge volumes of data, and, of course, improved algorithms. AI is transitioning from something that makes our lives more convenient (from Alexa to Siri, Uber, and Netflix) to a force that will overhaul our work processes in irreversible ways. Over the next few years, the commercial world will be transformed by the new machines down to the smallest detail, and the banking industry is no exception. Our recent research confirms that 58% of industry executives surveyed believe that AI will have a game-changing impact on their work by 2020. In short, the future of work in the banking industry is the mirror image of the future of AI.
In preparation for this massive shake-up, we are already seeing banks experimenting with chatbots and robotic advisory services in the areas of fraud detection and trading, among others. For example, India’s ICICI Bank is using natural language processing for sentiment mapping; SEB, one of Sweden’s largest banks, has introduced Amelia, a digital employee that can handle internal IT support; and digibank by DBS has integrated an AI platform into its mobile app to streamline the efficiency of customer conversations. Meanwhile, Active.ai, a Singapore-based fintech startup, is in talks with 20+ banks in the Asia Pacific region to deploy its Chatbot platform and expects at least 10 of these to roll it out live on their platform this year.
However, AI’s potential goes way beyond answering simple customer queries like “why was I charged a fee?” The time has come to move beyond experiments and take a long hard look at AI and what it means to your business. Process by process, throughout your entire value chain, you should pinpoint how AI can be applied to revolutionize how you perform your work. In short, AI is set to radically refashion banks’ operating models, making a deep-seated impact on business. There are three areas on which banks can focus to unlock AI’s true potential:
AI-led automation will enable massive savings. At present, banking executives are mainly focused on the front office, where customer interactions take place or products and services are offered. Such a narrow view, however, leads them to running the risk of missing something significant: the hidden treasure of their back office. Our research shows that banks are yet to unlock the true value of what lies in those spaces. AI has as much to do with securing tremendous savings in back-office operations and improving processes as it does with defining the face of the company.
AI-led automation will accelerate the pace of modernization in both middle- and back-office operations, thereby truly digitizing the fundamental operational blocks. With banks looking to reduce human error and get away from the complexity of legacy technology and operations, AI is the ideal candidate. For instance, Blue Prism is applying bots to risk, fraud, claims processing, and loan management in banking, saving millions. In another interesting example, ANZ Bank is leveraging AI for back-office automation to reduce time-to-market for the approval of unsecured and personal loans. According to their CTO, 1000 hours of back office activity have been eliminated due to the increased automation. Institutions’ resources can now be directed to tasks that add greater value to the business. In fact, automation will dictate firms’ subsequent AI initiatives. Our latest book, What to Do When Machines Do Everything, offers guidance on how leaders should pick their automation targets across their organization.
Big data is essential for viable AI systems. Looking after an AI machine is like keeping a hungry monster fed; incredible volumes of data are needed to provide the necessary capacity for learning algorithms so the machine can make strategic decisions (where to open a branch office or whether to approve a loan). Don’t jump the AI gun until you have addressed your data volumes and any quality issues. Banks will have to start tapping into their data for machine learning to improve business operations and build consumer-facing applications. Many banks are stuck in a paradox of an overload of data into which they have little insight; this is where big data steps up as an enabler for AI. This blend of the two should be hailed as a revolutionary paradigm for businesses.
AI is set to transform cyber security. The recent ransomware attack, WannaCry, demonstrated that cyber security threats have already become more than organizations have the capacity to handle. When it comes to security, too many organizations seem to adopt a reactive, rather than proactive, approach. It is estimated that cyber-attacks will cost businesses as much as US$400 billion per annum by 2021. To put things in perspective, that’s more than the GDP of roughly 160 of the 196 countries in the world.
Compounding the problem is the fact that our current computational infrastructure is woefully inadequate. What we need is a software infrastructure that can be mathematically proven to ensure a superlative level of security and has the ability to identify suspicious patterns before they can do any damage (however, we need to remember that there is no such thing as 100% security). AI and big data will complement each other and become the new face of consumer trust for organizations. In the same way that smartphones have become an extension of our persona, intelligent machines will grow from this new ethos of cyber security.
The early bird catches the worm, so make sure you move fast to reap all the rewards of this exciting AI age.
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