Research Notes: Academia, Big Four Consulting, and Key Books for Understanding AI in Banking
By Rob Tyrie
The Future of AI-Enabled Banking: A Strategic Architecture and Implementation Framework
Transforming Banks with AI
As banks navigate increasing customer expectations, regulatory pressure, and competition, artificial intelligence (AI) emerges as the transformative force to redefine the banking landscape. However, AI adoption requires more than fragmented solutions; it demands a coherent architecture paired with a systematic framework for implementation.
This document presents:
1. The AI-Enabled Banking Architecture: A layered, system-oriented approach for designing AI-powered banks.
2. The ABCDE Implementation Model: A structured method for assessing, building, and deploying AI solutions.
But first here’s some locations that are working now:
look at these resources about banking and finance and AI.
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1. Academic Research Articles
1.1. "Artificial Intelligence in Finance: A Comprehensive Review Through Bibliometric and Content Analysis"
Authors: Salman Bahoo, Marco Cucculelli, Xhoana Goga, Jasmine Mondolo
Published in: SN Business & Economics, 2024
Focus: This study provides a comprehensive overview of AI applications in finance, analyzing research trends and identifying future directions.
URL: Artificial Intelligence in Finance - Springer
1.2. "Is Artificial Intelligence and Machine Learning Changing the Ways of Banking: A Systematic Literature Review and Meta-Analysis"
Authors: Sushil Kalyani, Neha Gupta
Published in: Discover Artificial Intelligence, 2023
Focus: This paper analyzes how AI and machine learning are transforming banking operations, enhancing efficiency, and building consumer trust.
URL: AI and Machine Learning in Banking - Springer
1.3. "Utilization of Artificial Intelligence in the Banking Sector: A Systematic Review"
Authors: Fares, Irfan Butt, Seung Hwan Mark Lee
Published in: Journal of Banking and Financial Technology, 2022
Focus: This study provides a holistic review of AI utilization in banking, identifying research themes and proposing an AI banking service framework.
URL: AI in Banking Sector - Springer
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2. Consulting Reports
2.1. "Extracting Value from AI in Banking: Rewiring the Enterprise"
Source: McKinsey & Company, 2024
Focus: This report discusses how banks can become AI-first institutions by adopting AI technologies enterprise-wide to boost value.
URL: Extracting Value from AI in Banking - McKinsey
2.2. "Artificial Intelligence: Transforming the Future of Banking"
Source: Deloitte
Focus: This report explores how AI and machine learning are transformative technologies reshaping the banking industry.
URL: AI Transforming Banking - Deloitte
2.3. "AI in Banking: AI Will Be an Incremental Game Changer"
Source: S&P Global
Focus: This report analyzes the impact of AI on the banking sector, highlighting its potential to enhance efficiency and profitability.
URL: AI in Banking - S&P Global
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3. Books
3.1. "AI 2041: Ten Visions for Our Future"
Authors: Kai-Fu Lee and Chen Qiufan
Focus: This book presents imaginative narratives and analysis on how AI will transform various industries, including finance, by 2041.
URL: AI 2041 on Amazon
3.2. "Deep Reinforcement Learning Hands-On"
Author: Maxim Lapan
Focus: A practical guide to implementing reinforcement learning algorithms, relevant for developing AI models in financial applications.
URL: Deep Reinforcement Learning Hands-On on Amazon
3.3. "The Alignment Problem: Machine Learning and Human Values"
Author: Brian Christian
Focus: This book explores the challenges of aligning AI systems with human values, crucial for ethical AI deployment in finance.
URL: The Alignment Problem on Amazon
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These resources provide a comprehensive understanding of how AI is reshaping the banking and finance sectors, offering insights from academic research, industry analyses, and practical implementations.
Mastercard’s AI Fraud Detection Revolution
Mastercard’s Decision Intelligence Pro, a groundbreaking generative AI model, is transforming fraud detection capabilities. The system processes 125 billion annual transactions on the Mastercard network, focusing on merchant relationships to identify fraudulent activities. The technology has demonstrated remarkable results, improving fraud detection rates by an average of 20%, with some cases showing up to 300% improvement - all within 50 milliseconds[1].
PwC Reports Game-Changing Impact on Banking Security
Generative AI is revolutionizing banking security and operational efficiency according to PwC's latest analysis. The technology is streamlining loan processing while enhancing cybersecurity measures through advanced AI models that enable faster, more precise decision-making in loan approvals and risk assessment[3].
Bank of America’s Erica Shows AI Success in Customer Service
Bank of America’s AI chatbot Erica exemplifies successful AI implementation in banking, serving over 10 million users. The system handles everything from transaction queries to bill payments and personalized financial advice, continuously improving its service through machine learning algorithms that adapt to user interactions[5].
Qatar Banks Pioneer AI Collaboration Framework
Qatar's banking sector is leading the way in AI innovation through a progressive regulatory framework that enables banks to collaborate with FinTech companies. This initiative has already yielded success, with banks developing new mobile trading applications that allow customers to execute buy and sell orders seamlessly[7].
Real-Time AI Risk Management Breakthrough
Financial institutions are implementing sophisticated AI systems for real-time analysis across multiple channels including ATMs and internet banking. These systems are successfully integrating anti-financial crime measures by analyzing complex data points and predicting patterns in financial transactions, creating a more secure banking environment[1].
Citations:
[1] How AI could reshape finance in 2024 - Codebase Technologies https://www.codebtech.com/how-ai-could-reshape-finance-in-2024/
[2] AI uses in the financial sector - CPABC https://www.bccpa.ca/news-events/cpabc-newsroom/2024/july/ai-uses-in-the-financial-sector/
[3] AI Transforms Finance as Banks, Governance and Trading Evolve https://www.pymnts.com/news/artificial-intelligence/2024/ai-surge-transforms-finance-banks-governance-trading-evolve/
[4] The Financial Stability Implications of Artificial Intelligence https://www.fsb.org/2024/11/the-financial-stability-implications-of-artificial-intelligence/
[5] Artificial Intelligence in Banking: A Comprehensive Outlook for 2024 https://latinia.com/en/resources/artificial-intelligence-banking-comprehensive-outlook-2024
[6] 2024 and Beyond: How AI is Transforming Banking & Finance https://www.linkedin.com/pulse/2024-beyond-how-ai-transforming-banking-finance-corey-rockafeler-vcawe
[7] Making AI Pay Off in Global Banking https://gfmag.com/executive-interviews/making-ai-pay-off-in-global-banking/
[8] Generative AI In Banking: 8 Use Cases And Challenges In 2024 https://www.ideas2it.com/blogs/generative-ai-in-banking
[9] 2024's Banking Landscape: Digital Evolution and AI Integration https://www.onesafe.io/blog/future-banking-trends-2024
[10] How AI Is Transforming The Finance Industry - Forbes https://www.forbes.com/sites/kathleenwalch/2024/09/14/how-ai-is-transforming-the-finance-industry/
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The Big Picture: Banking as a Layered AI System
Banks operate as complex systems composed of infrastructure, models, workflows, and customer-facing interactions. AI amplifies their ability to create value through automation, personalization, and optimization. The Layered AI Bank Framework simplifies this complexity by visualizing the bank as a five-layer architecture, where each layer builds upon the previous to deliver tangible business outcomes.
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The Layered AI Bank Architecture
The architecture moves bottom-up to show dependencies: from technology foundations to AI-powered business impact.
1. Foundation Layer: Infrastructure and Core Technology
What it is: The backbone of the AI system, handling compute, storage, and secure data pipelines.
Components:
Cloud AI infrastructure
Data ingestion, preprocessing, and pipelines
API gateways and cybersecurity governance
2. AI Models and Capabilities Layer
What it is: The AI engines that process data and make decisions.
Components:
LLMs (Large Language Models) for natural language tasks
AI agents for risk scoring, fraud detection, and credit analysis
Predictive models for decision support
3. Application Layer: Workflows and Use Cases
What it is: Where AI creates operational and business value in specific banking domains.
Domains:
Retail Banking: AI for personalized products, transaction monitoring
Corporate Banking: Automated underwriting, risk-based pricing
Cross-Functional: AI-driven developer tools, HR staffing, and compliance
4. Engagement and Interaction Layer
What it is: Customer and employee-facing AI tools.
Components:
Conversational AI (voice assistants, chatbots)
Omnichannel customer experiences
Digital twins for behavior simulation
5. Impact Layer: Business and Value Outcomes
What it is: The measurable impact of AI deployment.
Outcomes:
Improved customer satisfaction (CLV growth)
Cost reductions via automation
Enhanced risk and compliance management
New revenue streams from AI products
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The Diagram: Layered AI Bank Architecture
Here is a summarized depiction of the layers:
+--------------------------------------------------+
| 5. Impact Layer: Customer Value & Business Gains|
+--------------------------------------------------+
| 4. Engagement Layer: AI-Driven Interactions |
+--------------------------------------------------+
| 3. Application Layer: Workflows & Use Cases |
+--------------------------------------------------+
| 2. AI Models & Capabilities |
+--------------------------------------------------+
| 1. Foundation: Infrastructure & Core Technology |
+--------------------------------------------------+
Each layer builds up to generate AI-driven value, aligning technology with tangible outcomes.
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The ABCDE Model: A Method to Apply AI in Banking
Implementing AI successfully requires a structured approach. The ABCDE Model ensures systematic execution:
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A: Assess Needs and Opportunities
Conduct an audit to identify gaps in workflows, operations, and customer engagement.
Prioritize use cases where AI creates the most value, e.g., risk mitigation, cost savings, and customer satisfaction.
Example: Identify high-friction processes like loan approvals or fraud detection for automation.
Key Question: Where can AI reduce friction, optimize processes, or enhance customer satisfaction?
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B: Build a Modular AI Stack
Develop a modular AI technology stack aligned with the Layered AI Bank Architecture.
Ensure data readiness by building robust ingestion pipelines and preprocessing tools.
Example Components:
Cloud-based compute infrastructure
LLM-powered credit risk tools
API gateways for integrations
Key Goal: Create a scalable foundation to test and expand AI capabilities.
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C: Configure Use Cases and Models
Match AI tools to specific domains and workflows.
Fine-tune models for banking applications, ensuring alignment with real data.
Example Use Cases:
Retail Banking: Fraud detection using AI-driven anomaly detection.
Corporate Banking: AI-driven loan underwriting models.
Key Deliverable: Practical AI solutions tied to business outcomes.
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D: Deploy and Test in Controlled Environments
Pilot AI solutions with clear success metrics (KPIs: cost reduction, risk accuracy, customer NPS).
Use controlled environments and A/B testing to evaluate impact.
Example: Deploy an AI-powered chatbot in a subset of branches and measure customer engagement.
Key Principle: Start small, measure thoroughly, and iterate.
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E: Expand and Optimize
Scale successful AI pilots across workflows, domains, and geographies.
Build feedback loops to continuously retrain and improve models.
Example: Expand a tested fraud detection model from retail to corporate banking.
Key Outcome: A continuously evolving, AI-powered banking system.
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The Roadmap to AI-Powered Banking
By implementing the Layered AI Bank Architecture and the ABCDE Model, banks can seamlessly integrate AI into their operations. This approach balances technological capability with business impact, ensuring:
1. Efficient AI deployment via modular systems.
2. Focused use cases that align with strategic goals.
3. Measurable outcomes in cost, risk, and customer value.
The future of banking is not just AI-enabled—it’s AI-optimized, delivering smarter, faster, and customer-centric solutions.
To truly grasp the AI Layered Architecture and the ABCDE Implementation Model, here are curated notes from academic research, Big Four insights, and essential books. These focus on Generative AI (GenAI), computer vision, and evolving technologies like world models for simulation and testing.
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Here is a curated list of resources, each focusing on the intersection of Artificial Intelligence (AI), banking, and finance. These resources encompass academic research, consulting reports, and insightful books relevant to understanding AI’s role in the financial sector.
By combining insights from these sources, banks can successfully implement the Layered AI Bank Architecture and the ABCDE Model, ensuring that AI systems are efficient, trustworthy, and capable of transforming the industry.
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1. Academic Research Articles
1.1. "Artificial Intelligence in Finance: A Comprehensive Review Through Bibliometric and Content Analysis"
Authors: Salman Bahoo, Marco Cucculelli, Xhoana Goga, Jasmine Mondolo
Published in: SN Business & Economics, 2024
Focus: This study provides a comprehensive overview of AI applications in finance, analyzing research trends and identifying future directions.
URL: Artificial Intelligence in Finance - Springer
1.2. "Is Artificial Intelligence and Machine Learning Changing the Ways of Banking: A Systematic Literature Review and Meta-Analysis"
Authors: Sushil Kalyani, Neha Gupta
Published in: Discover Artificial Intelligence, 2023
Focus: This paper analyzes how AI and machine learning are transforming banking operations, enhancing efficiency, and building consumer trust.
URL: AI and Machine Learning in Banking - Springer
1.3. "Utilization of Artificial Intelligence in the Banking Sector: A Systematic Review"
Authors: Fares, Irfan Butt, Seung Hwan Mark Lee
Published in: Journal of Banking and Financial Technology, 2022
Focus: This study provides a holistic review of AI utilization in banking, identifying research themes and proposing an AI banking service framework.
URL: AI in Banking Sector - Springer
---
2. Consulting Reports
2.1. "Extracting Value from AI in Banking: Rewiring the Enterprise"
Source: McKinsey & Company, 2024
Focus: This report discusses how banks can become AI-first institutions by adopting AI technologies enterprise-wide to boost value.
URL: Extracting Value from AI in Banking - McKinsey
2.2. "Artificial Intelligence: Transforming the Future of Banking"
Source: Deloitte
Focus: This report explores how AI and machine learning are transformative technologies reshaping the banking industry.
URL: AI Transforming Banking - Deloitte
2.3. "AI in Banking: AI Will Be an Incremental Game Changer"
Source: S&P Global
Focus: This report analyzes the impact of AI on the banking sector, highlighting its potential to enhance efficiency and profitability.
URL: AI in Banking - S&P Global
---
3. Books
3.1. "AI 2041: Ten Visions for Our Future"
Authors: Kai-Fu Lee and Chen Qiufan
Focus: This book presents imaginative narratives and analysis on how AI will transform various industries, including finance, by 2041.
URL: AI 2041 on Amazon
3.2. "Deep Reinforcement Learning Hands-On"
Author: Maxim Lapan
Focus: A practical guide to implementing reinforcement learning algorithms, relevant for developing AI models in financial applications.
URL: Deep Reinforcement Learning Hands-On on Amazon
3.3. "The Alignment Problem: Machine Learning and Human Values"
Author: Brian Christian
Focus: This book explores the challenges of aligning AI systems with human values, crucial for ethical AI deployment in finance.
URL: The Alignment Problem on Amazon
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These resources provide a comprehensive understanding of how AI is reshaping the banking and finance sectors, offering insights from academic research, industry analyses, and practical implementations.
