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artificial intelligence prospects and challenges in banking sector

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Deutsche Bank AG Deutsche Bank Research Frankfurt am Main Germany E-mail: marketing.dbr@db.com Fax: +49 69 910-31877 www.dbresearch.com DB Research Management Stefan Schneider June 4, 2019 Artificial intelligence in banking A lever for profitability with limited implementation to date Digital solution providers state that one robot can work 24/7 and replace up to eight employees, without asking for days off or a raise. The application scope of the Artificial Intelligence (AI) in Fintech Industry market comprises Bank,Insurance,Securities and Funds,Third-party Financial Company andOthers. Reasons include the lack of a clear strategy for AI, an inflexible and investment-starved technology core, fragmented data assets, and outmoded operating models that hamper collaboration between business and technology teams. This requires embedding personalization decisions (what to offer, when to offer, which channel to offer) in the core customer journeys and designing value propositions that go beyond the core banking product and include intelligence that automates decisions and activities on behalf of the customer. Most traditional banks are organized around distinct business lines, with centralized technology and analytics teams structured as cost centers. The second necessary shift is to embed customer journeys seamlessly in partner ecosystems and platforms, so that banks engage customers at the point of end use and in the process take advantage of partners’ data and channel platform to increase higher engagement and usage. They can then translate these insights into a transformation roadmap that spans business, technology, and analytics teams. 8. It is simply supporting in understand the challenges, providing deep insights that drive to effective decision making. Banks introduced ATMs in the 1960s and electronic, card-based payments in the ’70s. Please click "Accept" to help us improve its usefulness with additional cookies. They might elect to keep differentiating core capabilities in-house and acquire non-differentiating capabilities from technology vendors and partners, including AI specialists. In the target state, the bank could end up with three archetypes of platform teams. This is a core feature when introducing new products or processes that need to be adopted by all branches in a short time. Since most people are creatures of habit, whenever there is a transaction that is not like the rest, either by amount, geolocation or even the language used by the browser accessing the bank, the machine triggers an alert, requesting additional verification steps from the owner. While most banks are transitioning their technology platforms and assets to become more modular and flexible, working teams within the bank continue to operate in functional silos under suboptimal collaboration models and often lack alignment of goals and priorities. Data Science: Where Does It Fit in the Org Chart? Another tool that can prove useful in fighting crime and increasing transaction security is the blockchain approach, a framework currently popular for cryptocurrencies, but which can help traditional financial institution and state authorities to combat money laundering. The development of artificial intelligence in the financial sector 1.1. Some of the applications of robotics and AI that got the widest media coverage are listed below. See “, John Euart, Nuno Ferreira, Jonathan Gordon, Ajay Gupta, Atakan Hilal, Olivia White, “. While many banks may lack both the talent and the requisite investment appetite to develop these technologies themselves, they need at minimum to be able to procure and integrate these emerging capabilities from specialist providers at rapid speed through an architecture enabled by an application programming interface (API), promote continuous experimentation with these technologies in sandbox environments to test and refine applications and evaluate potential risks, and subsequently decide which technologies to deploy at scale. Currently, banks have vast amounts of data regarding their clients, operations, payment terms, credit risks and more. However, banks must resolve several weaknesses inherent to legacy systems before they can deploy AI technologies at scale (Exhibit 5). The banking sector is becoming one of the first adopters of Artificial Intelligence. Data-ingestion pipelines that capture a range of data from multiple sources both within the bank (e.g., clickstream data from apps) and beyond (e.g., third-party partnerships with telco providers), Data platforms that aggregate, develop, and maintain a 360-degree view of customers and enable AA/ML models to run and execute in near real time, Campaign platforms that track past actions and coordinate forward-looking interventions across the range of channels in the engagement layer. 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. Many of these leading-edge capabilities have the potential to bring a paradigm shift in customer experience and/or operational efficiency. On the other, they must continue managing the scale, security standards, and regulatory requirements of a traditional financial-services enterprise. How can banks transform to become AI-first? Banking & Insurance. To become AI-first, banks must invest in transforming capabilities across all four layers of the integrated capability stack (Exhibit 6): the engagement layer, the AI-powered decisioning layer, the core technology and data layer, and the operating model. Some banks are pushing ahead in the design of omnichannel journeys, but most will need to catch up. These will serve them well in the years ahead. Banking is catching up with the technology revolution, and in the next few years, the tendency is to invest more in automatization and AI applications instead of human employees. What obstacles prevent banks from deploying AI capabilities at scale? It will collaborate extensively with partners to deliver new value propositions integrated seamlessly across journeys, technology platforms, and data sets. Two additional challenges for many banks are, first, a weak core technology and data backbone and, second, an outmoded operating model and talent strategy. AI, cloud computing, mobile-first and digital dashboards are already the norm, and new technologies are being adopted. Never miss an insight. Retrieving insights from these types of documents is impossible without AI which can understand patterns and create responses. AI algorithm accomplishes anti-money laundering activities in few seconds, which otherwise take hours and days. “ICICI Bank crosses 1 million users on WhatsApp platform,” Live Mint, July 7, 2020, livemint.com. Digital upends old models. See “Global AI Survey: AI proves its worth, but few scale impact,” November 2019, McKinsey.com. Artificial Intelligence (AI) has been touted as the next major disruptor of the financial services sector. Artificial Intelligence (AI) is transforming banking industry in improving their routine operations to boost efficiency level. Often underestimated, this effort requires rewiring the business processes in which these AA/AI models will be embedded; making AI decisioning “explainable” to end-users; and a change-management plan that addresses employee mindset shifts and skills gaps. Please try again later. Delivering personalized messages and decisions to millions of users and thousands of employees, in (near) real time across the full spectrum of engagement channels, will require the bank to develop an at-scale AI-powered decision-making layer. Please email us at: McKinsey Insights - Get our latest thinking on your iPhone, iPad, or Android device. Artificial Intelligence. Other applications are related to back-end operations or fraud prevention. Unfortunately, each of these pieces of information is stored in a different silo that is not interconnected with others and almost always tributary to legacy systems. What might the AI-bank of the future look like? Challenges in introducing automation and AI in the banks. Role of Artificial Intelligence. 10 Blurred background, film effect. This effort is motivated not only by cost reductions but also by clients’ preferences. The authors would like to thank Milan Mitra, Anushi Shah, Arihant Kothari, and Yihong Wu for their contributions to this article. Innovation Enterprise Ltd is a division of Argyle Executive Forum. However, AI has contributed magnificently to the rapidly developing banking industry. As an illustration, in the domain of unsecured consumer lending alone, more than 20 decisions across the life cycle can be automated. Suparna Biswas is a partner, Shwaitang Singh is an associate partner, and Renny Thomas is a senior partner, all in McKinsey’s Mumbai office. A practical way to get started is to evaluate how the bank’s strategic goals (e.g., growth, profitability, customer engagement, innovation) can be materially enabled by the range of AI technologies—and dovetailing AI goals with the strategic goals of the bank. Banks that fail to make AI central to their core strategy and operations—what we refer to as becoming “AI-first”—will risk being overtaken by competition and deserted by their customers. By Upasana Padhi Swedish philosopher Nick Bostrom, in the book Superintelligence said, “Machine learning is the last invention that humanity will ever need to make.”From electronic trading platforms to medical diagnosis, robot control, entertainment, education, health, and commerce, Artificial Intelligence (AI) and digital disruption have touched every field in the 21st century. As banks grapple with the many challenges posed by the COVID-19 crisis it becomes clear that, whatever the eventual outcome, they will learn many valuable lessons about their customers, their own capabilities, and the market as a whole. What are the main opportunities for artificial intelligence in the financial sector? 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. AI-powered … Artificial Intelligence in Banking Artificial intelligence has transformed every aspect of the banking process. Lastly, for various analytics and advanced-AI models to scale, organizations need a robust set of tools and standardized processes to build, test, deploy, and monitor models, in a repeatable and “industrial” way. With proactive efforts, we will soon be able to realize the full value of this technological innovation and how it can make digital banking … In this article, we propose answers to four questions that can help leaders articulate a clear vision and develop a road map for becoming an AI-first bank: Over several decades, banks have continually adapted the latest technology innovations to redefine how customers interact with them. Automated systems can ensure compliance with internal regulation every time and collect data that will be further used to calibrate the system even more. Clayton M. Christensen, Taddy Hall, Karen Dillon and David S. Duncan, “Know your customers ‘jobs to be done,” Harvard Business Review, September 2016, hbr.org. More broadly, disruptive AI technologies can dramatically improve banks’ ability to achieve four key outcomes: higher profits, at-scale personalization, distinctive omnichannel experiences, and rapid innovation cycles. Also, 75% of the current banking operations can undergo robotic process automation (RPA). Each platform team controls their own assets (e.g., technology solutions, data, infrastructure), budgets, key performance indicators, and talent. The AI-first bank of the future will also enjoy the speed and agility that today characterize digital-native companies. Artificial intelligence is also expected to massively disrupt banks and traditional financial services. 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. It will innovate rapidly, launching new features in days or weeks instead of months. Exhibit 4 shows an example of the banking experience of a small-business owner or the treasurer of a medium-size enterprise. In this article I examine the global artificial intelligence industry and in this context consider the aspects of politics, data, … AI-bank of the future: Can banks meet the AI challenge? AI in banking is represented by chatbots or online assistants that help customers with their issues by providing necessary information or executing different transactions. The fintech’s customers can solve several pain points—including decisions about which card to pay first (tailored to the forecast of their monthly income and expenses), when to pay, and how much to pay (minimum balance versus retiring principal)—a complex set of tasks that are often not done well by customers themselves. For an interactive view, visit: www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/the-executives-ai-playbook?page=industries/banking/ 11 6. It’s an exciting time for financial services. The journey to becoming an AI-first bank entails transforming capabilities across all four layers of the capability stack. While each solution is currently in-market by at least one large bank this is a far cry from broadly deployed. Techno-pessimists are alarmed, while optimists just envision ways of smoothing out the effects of what is called the fourth industrial revolution. 6 Business owners define goals unilaterally, and alignment with the enterprise’s technology and analytics strategy (where it exists) is often weak or inadequate. Equally important is the design of an execution approach that is tailored to the organization. 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. 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. In the span of a just few years, artificial intelligence (AI) has gone from a niche, relatively abstract concept, to entwining itself in multiple aspects of our daily lives. Siloed working teams and “waterfall” implementation processes invariably lead to delays, cost overruns, and suboptimal performance. It has changed the landscape impressively and made banking activities a lot easier to perform. Artificial Intelligence in Banking Sector. Third, banks will need to redesign overall customer experiences and specific journeys for omnichannel interaction. Core systems are also difficult to change, and their maintenance requires significant resources. Benefits of using automation, robots and AI. With that in mind, artificial intelligence is being used to refine the ways of confirming one’s identity to heighten the protection and security of one’s financials and privacy. Registered in England and Wales, Company Registered Number 6982151, 57-61 Charterhouse St, London EC1M 6HA, Why Businesses Should Have a Data Whizz on Their Team, Why You Need MFT for Healthcare Cybersecurity, How to Hire a Productive, Diverse Team of Data Scientists, Keeping Machine Learning Algorithms Humble and Honest, Selecting and Preparing Data for Machine Learning Projects, Health and Fitness E-Gear Come With Security Risks, How Recruiters are Using Big Data to Find the Best Hires, The Big Sleep: Big Data Helps Scientists Tackle Lack of Quality Shut Eye, U.S. Is More Relaxed About AI Than Europe Is, How To Use Data To Improve E-commerce Conversions, Personalization & Measurement. What started about four decades ago in gas stations with self-service pumps will become the norm in more conservative areas, including banking, law enforcement, and even government. Since then, artificial intelligence (AI) technologies have advanced even further, In addition to strong collaboration between business teams and analytics talent, this requires robust tools for model development, efficient processes (e.g., for re-using code across projects), and diffusion of knowledge (e.g., repositories) across teams. By John Manning, International Banker. The banking and financial sectors are slowly moving from the first digital age to the second. What is more, several trends in digital engagement have accelerated during the COVID-19 pandemic, and big-tech companies are looking to enter financial services as the next adjacency. AI-powered machines are tailoring recommendations of digital content to individual tastes and preferences, designing clothing lines for fashion retailers, and even beginning to surpass experienced doctors in detecting signs of cancer. 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. In the future, when AI becomes more autonomous it could focus on core issues such as the development of new products based on customer needs, decreasing credit risks and even advising HR regarding staffing levels. Internally, the AI-first institution will be optimized for operational efficiency through extreme automation of manual tasks (a “zero-ops” mindset) and the replacement or augmentation of human decisions by advanced diagnostic engines in diverse areas of bank operations. AI can be defined as the ability of a machine to perform cognitive functions associated with human minds (e.g., perceiving, reasoning, learning, and problem solving). 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. Leading consumer internet companies with offline-to-online business models have reshaped customer expectations on this dimension. Millennials and the upcoming generations prefer to interact with technology at a time that is convenient for them. There’s a lot of money being spent on artificial intelligence. It includes various capabilities, such as machine learning, facial recognition, computer vision, smart robotics, virtual agents, and autonomous vehicles. In Europe, similar challenges exist, and overcapacity, fragmentation, and the lack of a banking union, could further confound recovery prospects. Enterprise platforms deliver specialized capabilities and/or shared services to establish standardization throughout the organization in areas such as collections, payment utilities, human resources, and finance. To bolster revenues, many banks try to leverage fee income as the primary driver of growth, but such prospects may be limited, given the somber macroeconomic climate and surge in industry competition. AI technologies can help boost revenues through increased personalization of services to customers (and employees); lower costs through efficiencies generated by higher automation, reduced errors rates, and better resource utilization; and uncover new and previously unrealized opportunities based on an improved ability to process and generate insights from vast troves of data. 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. 2 Banking operations have been frozen in processes that have not been changed in years, but that is about to change drastically. And find out what the key steps are to developing the banking workforce of the future. The future of banking after COVID-19. The following paragraphs explore some of the changes banks will need to undertake in each layer of this capability stack. Highly Expensive. In this article we set out to study the AI applications of top b… Furthermore, depending on their market position, size, and aspirations, banks need not build all capabilities themselves. Yet, the 24/7 operating schedule, low maintenance cost and, in the case of AI, the possibility of self-improvement can easily motivate the investment. Artificial Intelligence is the future of banking as it brings the power of advanced data analytics to combat fraudulent transactions and improve compliance. 1 Beyond the at-scale development of decision models across domains, the road map should also include plans to embed AI in business-as-usual process. Despite billions of dollars spent on change-the-bank technology initiatives each year, few banks have succeeded in diffusing and scaling AI technologies throughout the organization. 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. Business platforms are customer- or partner-facing teams dedicated to achieving business outcomes in areas such as consumer lending, corporate lending, and transaction banking. Bank of America is currently the US leader in the use of mobile banking and artificial intelligence implementation with its chatbot erica, a platform that sends personalized financial recommendations to customers from within the Bank of America mobile app, after analyzing the customer’s data using predictive analytics and cognitive learning. The penetration of artificial intelligence in the banking sector had been unnoticed and sluggish until the advent of the era of internet banking. All of this aims to provide a granular understanding of journeys and enable continuous improvement. If data constitute the bank’s fundamental raw material, the data must be governed and made available securely in a manner that enables analysis of data from internal and external sources at scale for millions of customers, in (near) real time, at the “point of decision” across the organization. This gives clients peace of mind and saves the bank from important financial and image losses. Using augmented passwords and biometric identification such as voice and facial recognition and … AI in banking was an unheard term in the past decade. collaboration with select social media and trusted analytics partners By integrating business and technology in jointly owned platforms run by cross-functional teams, banks can break up organizational silos, increasing agility and speed and improving the alignment of goals and priorities across the enterprise. 11. 3 An illustration of the “jobs-to-be-done” approach can be seen in the way fintech Tally helps customers grapple with the challenge of managing multiple credit cards. Practical resources to help leaders navigate to the next normal: guides, tools, checklists, interviews and more. Across more than 25 use cases, A proper AI implementation requires the centralization of data and a cleaning stage. One of the main benefits of letting technology deal with bank processes is scalability. 7 Additionally, organizations lack a test-and-learn mindset and robust feedback loops that promote rapid experimentation and iterative improvement. Accuracy, predictability and removing any trace of human error are primary goals of introducing robots into the banking industry. 8 Banks are exploring and implementing technology in various ways. For global banking, McKinsey estimates that AI technologies could potentially deliver up to $1 trillion of additional value each year. To enable at-scale development of decision models, banks need to make the development process repeatable and thus capable of delivering solutions effectively and on-time. Michael Chui, Sankalp Malhotra, “AI adoption advances, but foundational barriers remain,” November 2018, McKinsey.com. As we will explain, when these interdependent layers work in unison, they enable a bank to provide customers with distinctive omnichannel experiences, support at-scale personalization, and drive the rapid innovation cycles critical to remaining competitive in today’s world. cookies, Global AI Survey: AI proves its worth, but few scale impact, McKinsey_Website_Accessibility@mckinsey.com, www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/the-executives-ai-playbook?page=industries/banking/, A global view of financial life during COVID-19—an update, AI adoption advances, but foundational barriers remain, Ten lessons for building a winning retail and small-business digital lending franchise, Unlocking business acceleration in a hybrid cloud world. 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 “The executive’s AI playbook,” McKinsey.com. The banking industry is becoming increasingly invested in the implementation of AI-powered systems across several areas, including customer services and … To establish a robust AI-powered decision layer, banks will need to shift from attempting to develop specific use cases and point solutions to an enterprise-wide road map for deploying advanced-analytics (AA)/machine-learning (ML) models across entire business domains. AI systems are only as good as the data used to train them and the data fed into them for calibration purposes. It includes various capabilities, such as machine learning, facial recognition, computer vision, smart robotics, virtual agents, and autonomous vehicles. The 2000s saw broad adoption of 24/7 online banking, followed by the spread of mobile-based “banking on the go” in the 2010s. Additionally, banks will need to augment homegrown AI models, with fast-evolving capabilities (e.g., natural-language processing, computer-vision techniques, AI agents and bots, augmented or virtual reality) in their core business processes. Insights for the annual growth rate and market share of each application segment during … To overcome the challenges that limit organization-wide deployment of AI technologies, banks must take a holistic approach. Flip the odds. Of late, the banking sector is becoming an active adapter of artificial intelligence—exploring and implementing this technology in new ways. Financial services clients expect meaningful and personalized experiences through intuitive and straightforward interfaces on any device, anywhere, and at any time. AI has impacted every banking “office" — front, middle and back. Currently, applications are more about automating repetitive tasks and reducing business process outsourcing. The digital future of work can’t be reversed and will expand to every activity sector. We use cookies essential for this site to function well. Discussions in the media around the emergence of AI in the banking industry range from the topic of automation and its potential to cut countless jobs to startup acquisitions. Our mission is to help leaders in multiple sectors develop a deeper understanding of the global economy. Learn more about what senior banking executives and employees are thinking and doing with regard to artificial intelligence. Since then, artificial intelligence (AI) technologies have advanced even further, 1 and their transformative impact is increasingly evident across industries. Envisioning and building the bank’s capabilities holistically across the four layers will be critical to success. “Closed loop” refers to the fact that the models’ intelligence is applied to incoming data in near real time, which in turn refines the content presented to the user in near real time. July 4, 2018. What’s next for remote work: An analysis of 2,000 tasks, 800 jobs, and nine countries, Overcoming pandemic fatigue: How to reenergize organizations for the long run, AI can be defined as the ability of a machine to perform cognitive functions associated with human minds (e.g., perceiving, reasoning, learning, and problem solving). Client loyalty is a product born through sturdy relationships that start by comprehending the client and their expectations. While customer experience can be tricky to quantify, client turnover is substantial, and client loyalty is rapidly becoming an endangered idea. To compete successfully and thrive, incumbent banks must become “AI-first” institutions, adopting AI technologies as the foundation for new value propositions and distinctive customer experiences. 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. hereLearn more about cookies, Opens in new 2. Take Customer Care to the Next Level with New Ways ... Why This Is the Perfect Time to Launch a Tech Startup. 1. People create and sustain change. How Will AI, Automation, And Robots Impact The Banking Sector? These technologies can lead to higher automation and, when deployed after controlling for risks, can often improve upon human decision making in terms of both speed and accuracy. 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