Furthermore, depending on their market position, size, and aspirations, banks need not build all capabilities themselves. It includes various capabilities, such as machine learning, facial recognition, computer vision, smart robotics, virtual agents, and autonomous vehicles. This includes: The immense competition in the banking sector; Push for process-driven services; Introduce self-service at banks; Demand from customers to provide more customised solutions; Creating operational efficiencies; Increasing employee productivity Arguably, however, it is the significant advancement being achieved in the world of artificial intelligence (AI) that is having … The core-technology-and-data layer has six key elements (Exhibit 7): The AI-first bank of the future will need a new operating model for the organization, so it can achieve the requisite agility and speed and unleash value across the other layers. Currently, applications are more about automating repetitive tasks and reducing business process outsourcing. This machinery is critical for translating decisions and insights generated in the decision-making layer into a set of coordinated interventions delivered through the bank’s engagement layer. “The executive’s AI playbook,” McKinsey.com. In this article I examine the global artificial intelligence industry and in this context consider the aspects of politics, data, … For an interactive view, visit: www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/the-executives-ai-playbook?page=industries/banking/ Core systems are also difficult to change, and their maintenance requires significant resources. Artificial Intelligence is the future of banking as it brings the power of advanced data analytics to combat fraudulent transactions and improve compliance. Apart from RPA which is used to increase efficiency and cut costs through process automation, AI and machine learning are used for improving the relationship with the clients, increasing customization and even fraud detection. The banking sector is becoming one of the first adopters of Artificial Intelligence. The penetration of artificial intelligence in the banking sector had been unnoticed and sluggish until the advent of the era of internet banking. Banking operations have been frozen in processes that have not been changed in years, but that is about to change drastically. There’s a lot of money being spent on artificial intelligence. Once this alignment is in place, bank leaders should conduct a comprehensive diagnostic of the bank’s starting position across the four layers, to identify areas that need key shifts, additional investments and new talent. For the bank to be ubiquitous in customers’ lives, solving latent and emerging needs while delivering intuitive omnichannel experiences, banks will need to reimagine how they engage with customers and undertake several key shifts. Most transformations fail. In 2016, AlphaGo, a machine, defeated 18-time world champion Lee Sedol at the game of Go, a complex board game requiring intuition, imagination, and strategic thinking—abilities long considered distinctly human. Flip the odds. While many financial managers view the technology with caution, the opportunities it offers for efficiency augmentation, cost reduction and customer satisfaction are irresistible; the big question is how to practically implement AI in day-to-day operations. The increasing degree of smart cities and the boost of IoT is expected to help clients conduct safer transactions based on geolocation, voice and face recognition. Subscribed to {PRACTICE_NAME} email alerts. This risk is further accentuated by four current trends: To meet customers’ rising expectations and beat competitive threats in the AI-powered digital era, the AI-first bank will offer propositions and experiences that are intelligent (that is, recommending actions, anticipating and automating key decisions or tasks), personalized (that is, relevant and timely, and based on a detailed understanding of customers’ past behavior and context), and truly omnichannel (seamlessly spanning the physical and online contexts across multiple devices, and delivering a consistent experience) and that blend banking capabilities with relevant products and services beyond banking. Challenges in introducing automation and AI in the banks AI systems are only as good as the data used to train them and the data fed into them for calibration purposes. Clayton M. Christensen, Taddy Hall, Karen Dillon and David S. Duncan, “Know your customers ‘jobs to be done,”, “ICICI Bank crosses 1 million users on WhatsApp platform,”, Jennifer Kilian, Hugo Sarrazin, and Hyo Yeon, “, Renny Thomas, Vinayak HV, Raphael Bick, and Shwaitang Singh, “. Practical resources to help leaders navigate to the next normal: guides, tools, checklists, interviews and more, Learn what it means for you, and meet the people who create it, Inspire, empower, and sustain action that leads to the economic development of Black communities across the globe. AI-powered … 3. Renny Thomas, Vinayak HV, Raphael Bick, and Shwaitang Singh, “Ten lessons for building a winning retail and small-business digital lending franchise,” November 2019, McKinsey.com. It has changed the landscape impressively and made banking activities a lot easier to perform. Among the obstacles hampering banks’ efforts, the most common is the lack of a clear strategy for AI. How Will AI, Automation, And Robots Impact The Banking Sector? Artificial intelligence has been around for a while, but recently it is taking on a life of its own, invading various segments of business, including finance. The 2000s saw broad adoption of 24/7 online banking, followed by the spread of mobile-based “banking on the go” in the 2010s. The platform operating model envisions cross-functional business-and-technology teams organized as a series of platforms within the bank. This involves allowing customers to move across multiple modes (e.g., web, mobile app, branch, call center, smart devices) seamlessly within a single journey and retaining and continuously updating the latest context of interaction. In return, the team delivers a family of products or services either to end customers of the bank or to other platforms within the bank. According to Mcafee, cybercrime targets primarily banks and roughly costs the global economy $600 billion. The AI-first bank of the future will also enjoy the speed and agility that today characterize digital-native companies. It provides complete customer support in a variety of procedures. See “Global AI Survey: AI proves its worth, but few scale impact,” November 2019, McKinsey.com. What obstacles prevent banks from deploying AI capabilities at scale? A veritable smorgasbord of new, interrelated technologies are brewing up a perfect storm of disruption in the industry, including blockchain, data science, cloud computing and biometrics. Reimagining the engagement layer of the AI bank will require a clear strategy on how to engage customers through channels owned by non-bank partners. Many of these leading-edge capabilities have the potential to bring a paradigm shift in customer experience and/or operational efficiency. While each solution is currently in-market by at least one large bank this is a far cry from broadly deployed. 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