Building the AI Bank of the Future
The essential capability stack for financial institutions to thrive in the AI-powered digital age
Executive Overview
Banking is at a pivotal moment as artificial intelligence (AI) technologies offer the potential to increase revenue at lower cost by engaging and serving customers in radically new ways. To compete and thrive in this challenging environment, banks must develop new value propositions founded upon leading-edge AI-and-analytics capabilities to deliver intelligent, highly personalized solutions at scale.
Higher Profits
Banks implementing AI technologies could potentially unlock up to $1 trillion in additional value annually through increased personalization, lower costs, and reduced errors.
At-scale Personalization
AI-first banks can deliver personalized messages and decisions to millions of users in real-time across the full spectrum of engagement channels.
Omnichannel Experiences
Customers expect seamless interactions across physical and digital channels, with consistent experiences that span the bank's proprietary platforms and partner ecosystems.
Rapid Innovation Cycles
AI-first banks innovate rapidly, launching new features in days or weeks instead of months, collaborating extensively with partners to deliver new value propositions.
The AI Bank Capability Stack
The AI bank capability stack consists of four interdependent layers that must work in unison to deliver value. Each layer has unique requirements, and underinvestment in any layer will ripple through the entire stack.
1. Reimagined Engagement Layer
Enables the AI bank to provide customers with intelligent offers, personalized solutions, and smart servicing within omnichannel journeys across bank-owned platforms and partner ecosystems.
2. AI-powered Decision Making
Machine-learning models significantly enhance customer experiences and bank productivity by generating real-time analytical insights and translating them into messages addressing precise customer needs.
3. Core Technology & Data Infrastructure
Provides the backbone with automated cloud provisioning and an API and streaming architecture to enable continuous, secure data exchange between centralized data infrastructure and other layers.
4. Platform Operating Model
Brings together agile teams with the right talent mix, culture, and ways of working to maximize value in close alignment with enterprise strategy.
Executive Talent Implications
To build an AI bank, financial institutions need to transform their leadership approach and talent strategy:
Tech-forward Leadership
Banks need leaders who can formulate strategic goals for the AI-enabled digital age and evaluate how AI technologies support these goals, taking a holistic perspective across business and technology.
Interdisciplinary Talent
Success requires blending business, IT, and digital expertise, with talent that understands both customer needs and technology capabilities to develop AI solutions that create competitive advantage.
Modern Talent Strategy
Banks must develop strategies for attracting, retaining, and upskilling digital talent, creating an environment that prioritizes learning and rewards deep expertise while offering flexible, collaborative ways of working.
Agile Culture
Implementing platform operating models requires a shift to autonomous cross-functional teams organized around value delivery, with leaders who can steer organizations to focus on end users, collaborate across silos, and foster experimentation.
Platform Operating Model
The platform operating model is crucial for AI banks, consisting of three main categories of platforms, each functioning as a nimble fintech group:
Business Platforms
Directly aligned to business units to deliver business and technology outcomes.
- Consumer lending
- Cards
- Wealth management
Enterprise Platforms
Aligned to multiple business units to deliver outcomes across units.
- Core banking
- Payments
- Analytics and data
- Finance, Risk, HR
Enabling Platforms
Provide scale benefits and define guardrails for the organization.
- Enterprise architecture
- IT infrastructure
- Cybersecurity
Key Leadership Roles for the AI Bank
The transformation to an AI bank requires new leadership roles and capabilities:
Platform Leaders
Co-owners of business and technology outcomes who facilitate interaction between business and technology teams to balance "run-the-bank" and "change-the-bank" initiatives.
Data Scientists
Responsible for identifying analytics techniques to meet business goals and programming advanced analytics algorithms within lab and factory environments.
Data Engineers
Build data architecture and pipelines, identifying major data sources to be consolidated for analytics and setting up data architecture for storage and layering.
Technology Translators
Interface between business and technical stakeholders to ensure consistent communication and smooth collaboration across disciplines.
DevOps Engineers
Develop continuous integration and continuous deployment pipelines for deploying software securely and efficiently.
Machine Learning Engineers
Optimize ML models for performance and scalability, preparing models for deployment at scale across enterprise platforms.
Edge AI Capabilities
Leading AI banks are deploying advanced "edge capabilities" to differentiate their offerings and create superior customer experiences. These technologies provide a competitive advantage and enable new business models:
Natural Language Processing
Enables banks to analyze unstructured data from customer interactions, automate document processing, and power conversational interfaces that understand customer intent and context.
Voice Script Analysis
Analyzes voice interactions to detect customer sentiment, match customers with suitable agents, and provide real-time guidance to service representatives during calls.
Virtual Agents & Bots
Handle routine customer inquiries, guide users through complex processes, and provide 24/7 service while continuously learning from interactions to improve performance.
Computer Vision
Automates document processing through optical character recognition (OCR), verifies identity through document scanning, and enables frictionless in-branch experiences.
Facial Recognition
Provides secure biometric authentication, streamlines customer onboarding, and enables smile-to-pay transactions and personalized branch experiences when customers enter.
Blockchain
Creates smart contracts, secures trade documents, automates release of funds upon delivery of goods, and establishes shared utilities for KYC and anti-money laundering compliance.
Robotics
Automates repetitive operational tasks, enhances branch experiences with humanoid assistants, and improves back-office processing efficiency.
Behavioral Analytics
Analyzes patterns in customer interactions to predict needs, detect fraudulent activities, and optimize communications timing and channel selection.
Implementation Roadmap
To successfully build an AI bank, leaders should take a holistic approach starting with these key actions:
- Establish a strategic vision for the AI-enabled digital age and evaluate how AI technologies can support these goals.
- Chart a detailed roadmap for modernizing enterprise technology and streamlining the end-to-end stack.
- Assess the potential of emerging technologies to meet precise customer needs and prioritize technology initiatives with the greatest impact.
- Leverage partnerships for non-differentiating capabilities while devoting capital resources to in-house development of differentiating capabilities.
- Implement a platform operating model with cross-functional teams organized around value delivery.
