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Only 6% of retail banks have built an enterprise roadmap to drive AI-driven transformation at scale
Just 4% of retail banks are ready to take full advantage of generative AI-led intelligent automation
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Customer onboarding teams currently spend 91% of their time on operational and compliance activities
Paris, March 5, 2024 – The 20th anniversary edition of the Capgemini Research Institute’s World Retail Banking Report, published today, reveals 80% of retail bank executives believe that generative AI represents a significant leap in advancing AI technology. However, only 6% of retail banks are ready with a roadmap for enterprise-wide AI-driven transformation at scale.
As a result of macroeconomic uncertainty, many retail banks are being forced to make strategic decisions to navigate challenges to their existing business models. Productivity and efficiency dominated the priority list of the bank leaders surveyed. When it comes to technology, 70% of bank CXOs plan to increase investment in digital transformation by up to 10% in 2024. Yet, the report finds that banks are not ready to embrace and scale intelligent transformation, which involves the strategic application of advanced technologies like AI, machine learning and gen AI to drive innovation and efficiencies.
Banks must act quickly to avoid “generative AI silent failure”
For this report, Capgemini evaluated 250 retail banks across diverse business and technology parameters1 to understand their infrastructure data maturity and commitment to artificial intelligence. It found most banks are ill-prepared to thrive in an intelligent banking2 future. Globally, only 4% of retail banks achieved a high score on business commitment and technology capabilities, while 41% scored average, indicating a widespread lack of readiness to embrace and effectively implement intelligent transformation.3 Regional disparities further underscore this issue. In North America, 27% of banks displayed low readiness, followed by Europe with 31%, and Asia-Pacific (APAC) exhibiting a significant lag, with 48% of banks scoring low.
Focusing on intelligent solutions, that are embedded with AI-driven capabilities, will allow banks to navigate ongoing structural challenges, ultimately ensuring sustainable growth. However, success must be measurable: among those surveyed, just 6% of banks have established key performance indicators (KPIs) to measure AI impact and continuous monitoring. More than 60% of banks are still identifying and developing KPIs, while 26% of banks that have already setup some KPIs are not measuring them.
According to the report banks risk succumbing to “generative AI silent failure” due to the delayed realization of suboptimal results and outcomes from their experiments with the technology. For instance, just 2% of executives indicate they are regularly tracking the business impact KPIs of their generative AI performance. In addition, 39% of executives express dissatisfaction with the outcomes of their AI use cases further reinforcing this disconnect. To combat this, the study suggests banks set up an AI observatory to track, monitor, and report AI and generative AI real impact, when implemented at scale.
“One year after generative AI cemented itself as a core boardroom conversation, we’re seeing how banks risk becoming technological laggards if they aren’t rapidly adopting solutions and preparing to take advantage of its capabilities,” said Nilesh Vaidya, Global Industry Head of Retail Banking and Wealth Management at Capgemini. “Generative AI can have a lighthouse effect when used responsibly and wisely across operations. There is also a need for increased efforts on making gen AI explainable and appropriately transparent. The time to act is now to establish practices that build much-needed trust and customer intimacy. Success will come down to developing a roadmap that balances hype with a pragmatic, traceable and measurable approach.”
Bank employees welcome generative AI copilots
Generative AI holds massive potential to elevate efficiency and customer experience across the retail banking value chain. Over two-in-three (70%) bank employees are focused on operational activities, rising to 91% for those employees on customer onboarding teams, leaving little time for customer interactions. Over 80% of bank employees give a “moderate” rating to the effectiveness of automation across their functions (onboarding, lending, marketing, contact center), identifying a significant gap between the bank’s aspirations and reality.
Bank employees reported to be most enthusiastic about generative AI copilots’ potential to automate fraud detection, data visualization and analytics, as well as drafting and sending personalized content to customers. The report determines that banks could optimize up to 66% of the time spent on operations, documentation, compliance, and other onboarding-related activities through AI-powered intelligent transformation and generative AI copilots.
Conversational AI could alleviate customer call abandonment
The pandemic shifted customer service offers across to digital channels as self-service tools like chatbots became the norm. Despite this change, customers express dissatisfaction. Nearly two-in-three (61%) bank customers contacted agents because they were unhappy with chatbot resolutions, while 17% simply distrusted chatbots and preferred human agents.
Traditional rule-based chatbots lack the flexibility and adaptability of advanced AI-driven systems due to their inability to handle complex or unanticipated queries. More than 60% of customers rated their experience with chatbots as only average. These conditions mean that call abandonment is on the rise, reaching 12% for Tier I banks and nearly 18% for Tier II banks globally4. According to the report, banks should create intelligent contact centers that leverage chatbots with conversational AI capabilities and intelligent copilots to assist agents in their day-to-day tasks.
Report Methodology
The World Retail Banking Report 2024 cites regional statistics in Capgemini's proprietary market-sizing model, as well as interviews with Capgemini’s partners including Microsoft, Salesforce and Temenos. For this report, the Capgemini Research Institute surveyed more than 250 retail banking executives, 1,500 banking employees and 4,500 banking customers. The report focused on 14 markets – the United States, Canada, the UK, France, Germany, Spain, the Netherlands, the United Arab Emirates, Singapore, Hong Kong, Japan, China, India, and Australia.
About Capgemini
Capgemini is a global business and technology transformation partner, helping organizations to accelerate their dual transition to a digital and sustainable world, while creating tangible impact for enterprises and society. It is a responsible and diverse group of 340,000 team members in more than 50 countries. With its strong over 55-year heritage, Capgemini is trusted by its clients to unlock the value of technology to address the entire breadth of their business needs. It delivers end-to-end services and solutions leveraging strengths from strategy and design to engineering, all fueled by its market leading capabilities in AI, cloud and data, combined with its deep industry expertise and partner ecosystem. The Group reported 2023 global revenues of €22.5 billion.
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About the Capgemini Research Institute
The Capgemini Research Institute is Capgemini’s in-house think-tank on all things digital and their impact across industries. It is the publisher of Capgemini’s flagship World Report Series for over 25 years with dedicated focus for Financial Services and publishes thought leadership on digitalization, innovation, technology and business trends that affect banks, wealth management firms, and insurers across the globe. Independent agency rated a recent World Retail Banking Report, published by the Institute, as one of the top 10 publications among consultancy and technology firms globally.
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1 Business support and commitment are measured by scoring AI vision, AI adoption roadmap, budget, talent, use cases in the pipeline, level of KPI monitoring, and AI governance. Tech and data readiness is measured by data sourcing systems, ability to manage real-time data, systems to generate synthetic data, centralized data lakes, capability to transform data, MLOps setup (machine learning operations), data management approach to modernize data estate, and data governance framework.
2 Intelligent banking is an outcome of intelligent transformation where banks embrace a high degree of process automation at enterprise scale to deliver mass personalization.
3 Banks that score more than 44 on technology parameters and greater than 32 on business parameters are classified as high scorers. Banks that score between 33 and 44 on technology parameters and have scored between 24 and 32 on business parameters are classified as medium scorers. Banks with scores of less than 33 on technology parameters and less than 24 on business parameters are classified as low scorers.
4 Tier I bank have assets of US$100 billion and more; Tier II bank have assets between US$10 billion to US$100 billion.
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