Artificial Intelligence in Banking and Finance: Use Cases, Benefits, and a Reference Service Model

alvarobarrera0
Mar 24, 2025
2 min read

1. Introduction

Artificial Intelligence (AI) is rapidly transforming the financial sector by streamlining operations, reducing risks, enhancing customer experience, and enabling regulatory compliance. This document outlines the main AI use cases in finance, presents a reference model for AI-enabled financial services, and introduces the ISO/IEC 42001 standard—a globally recognized framework for managing AI systems responsibly and effectively.

2. Key AI Use Cases in the Financial Sector

2.1. Fraud Detection and Prevention

Real-time anomaly detection in financial transactions.
Behavioural models to identify deviations from normal customer activity.
Cross-channel fraud detection (e.g., social media, biometrics, geolocation).

2.2. Risk Assessment and Management

AI-based alternative credit scoring.
Predictive modelling for credit, market, and operational risks.
Enhanced stress testing and scenario analysis.

2.3. Customer Experience and Virtual Assistants

AI-powered chatbots offering 24/7 support.
Tailored product recommendations through behavioural analytics.
Virtual financial advisors for budgeting and planning.

2.4. Process Automation (Intelligent RPA)

Automated loan processing, customer onboarding, and KYC verification.
Contract review and document analysis using generative AI.
Intelligent process orchestration by integrating AI and RPA.

2.5. Regulatory Compliance (RegTech)

Automated compliance monitoring and reporting.
AI for anti-money laundering (AML) and Know Your Customer (KYC).
Real-time transaction analysis aligned with financial regulations.

2.6. Investment Optimization (WealthTech)

Algorithmic trading and portfolio management.
Sentiment analysis using news and social media data.
Human-AI hybrid investment decision frameworks.

3. AI Services Reference Model for Financial Institutions

The following five-level framework can be used by financial institutions to guide the strategic and operational adoption of AI.

Level 1: Foundation and Governance

AI governance aligned with ISO/IEC 42001.
Definition of ethical principles (transparency, fairness, accountability).
Privacy, data protection, and cybersecurity policies.

Level 2: Data and Infrastructure Management

Data lifecycle management for AI training and inference.
Centralized data platforms (data lakes, warehouses).
Scalable infrastructure for model development and deployment.

Level 3: Use Case Development

Prioritised AI use cases based on business impact and feasibility.
Risk evaluation per use case (bias, explainability, model drift).
Thorough model testing before deployment.

Level 4: Operation and MLOps

Adoption of MLOps for model versioning, deployment, and monitoring.
Seamless integration with core banking systems and digital channels.
Continuous model performance and regulatory monitoring.

Level 5: Measurement, Improvement, and Audit

AI KPIs (accuracy, ROI, fraud detection rate, customer satisfaction).
Impact assessments (ethical, societal, operational).
Internal and external auditing of AI systems in line with ISO/IEC 42001.

4. ISO/IEC 42001: AI Management Systems in Financial Services

ISO/IEC 42001:2023 is the first international standard for AI Management Systems (AIMS). It provides a structured approach to managing risks, compliance, and ethical considerations throughout the AI lifecycle.

Core Elements:

AI lifecycle planning (design, development, deployment, operation, decommissioning).
Risk management (bias mitigation, reliability, human oversight).
Transparency (clear documentation of purpose and logic).
Auditability and continuous improvement mechanisms.

Relevance for Financial Institutions:

Builds trust among customers, stakeholders, and regulators.
Mitigates compliance, reputational, and operational risks.
Supports governance alignment with other standards such as ISO/IEC 27001 (information security), ISO 37301 (compliance), and PCI DSS (payment security).

Artificial intelligence is becoming essential for financial institutions to remain competitive and compliant. However, successful AI adoption requires structured implementation, strong governance, and alignment with international standards such as ISO/IEC 42001. This ensures responsible, secure, and explainable AI that meets both business goals and regulatory expectations.