Responsible AI in Banking: Ethics, Transparency, and Trust - Digital Banking

This one-day workshop equips banking professionals with foundational knowledge of responsible AI principles, emphasising ethical considerations, transparency in AI decision-making, and trust-building with stakeholders.

Participants will explore challenges such as AI bias, regulatory requirements, and ethical dilemmas, ultimately developing actionable strategies to integrate trustworthy AI solutions in banking operations.

Programme Outline

Learning Objectives

By the end of the programme, participants will be able to:

Explain the ethical implications of AI in banking and financial services.
Apply frameworks and best practices to enhance AI transparency and mitigate risks.
Identify key regulatory requirements impacting AI applications in the banking sector.
Analyse AI models for potential biases and recommend strategies to mitigate them.
Develop an implementation roadmap for trustworthy AI solutions aligned with ethical standards.

Programme Outline

Module 1: Introduction to Responsible AI in Banking
Overview of AI in banking, key applications (e.g., fraud detection, credit scoring, customer service automation)
Understanding responsible AI, concepts of fairness, accountability, and transparency
Importance of ethics in banking AI, building trust with customers and regulators
Case study, analysis of AI in credit scoring (example: controversies and biases in algorithm-based credit decisions)
Module 2: Ethical Principles and Challenges in AI
AI ethics frameworks, overview of principles (e.g., fairness, explainability, privacy)
Ethical dilemmas in AI, addressing data privacy, security, and accountability
Examples of ethical failures, notable cases (e.g., biased loan approvals, customer profiling in fintech)
Interactive exercise, identify ethical risks in a hypothetical AI-powered loan approval system
Module 3: Ensuring AI Transparency and Explainability
Building transparency in AI models, approaches to make AI decisions understandable
Explainability in banking applications, how transparency impacts customer trust and compliance
Case study, examining transparent AI in fraud detection (example: Citi’s AI-driven fraud monitoring)
Discussion, balancing technical complexity with the need for customer-facing transparency
Module 4: Regulatory Landscape for AI in Banking
Key regulations impacting AI in financial services, PDPA, AI Act, and emerging standards
Compliance challenges, addressing regional and global regulatory requirements
Case study, impact of PDPA on AI-driven customer profiling in banks
Group activity, develop a checklist for regulatory compliance in an AI application
Module 5: Identifying and Mitigating Bias in AI Models
Types of bias in AI, data, algorithmic, and human biases and their impact
Approaches to mitigating bias, best practices in data selection, model training, and evaluation
Use case, bias in credit decisioning (example: U.S. bank facing scrutiny over algorithmic lending bias)
Interactive exercise, evaluate an AI model’s input data for potential biases
Module 6: Building Trust with Stakeholders
Communicating AI decisions to customers, simplifying complex processes and ensuring clarity
Internal trust-building, training employees on responsible AI use and ethical standards
Case study, trust-building initiatives by financial institutions using AI (example: HSBC’s customer transparency policy)
Discussion, strategies for aligning AI practices with the bank’s mission and values
Module 7: Responsible AI in Action
Identify real-world banking challenges and opportunities where responsible AI can enhance customer trust and operational transparency
Activity, small groups brainstorm to outline responsible AI solutions for key areas (e.g., credit assessment, fraud monitoring, personalised customer service)
Outcome, present and discuss potential AI solutions that prioritise ethics and transparency, developing a roadmap for implementation

METHODOLOGY

This workshop uses lectures, real-world case studies, interactive discussions, and practical brainstorming exercises to engage participants. By examining relevant examples and regulations, attendees gain a holistic understanding of responsible AI in banking and develop actionable ideas to integrate ethical AI practices into their work.

Participant profile

Banking professionals, compliance officers, and decision-makers involved in AI implementation and strategy

Trainer

Peter Kua

Co-Founder of Growth.Pro Data Science Training Academy | Data & AI Strategist Trainer

Ng Keng Fai

Lead Trainer and Data Science Lead at Growth.Pro Data Science Consulting & Training

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Peter Kua

Co-Founder of Growth.Pro Data Science Training Academy | Data & AI Strategist Trainer

Peter Kua is the cofounder and Chief Data Officer of Growth.Pro Data Science Consulting & Training. His responsibilities include finding ways data can be used as a competitive advantage as well as identifying new business opportunities with data. He also headed the Data Science team in REV Media Group (formerly known as Media Prima Digital) and was instrumental in driving the National Big Data Analytics Initiative under MDEC in the areas of thought leadership and industry development. He played a key role in developing the first National BDA Framework that delivered strategic recommendations and action plans to achieve the National BDA vision.

Peter has conducted face-to-face training which includes public masterclasses in Big Data Strategy for NTT Data, PAS Selangor, CIIF, PosAviation, Hitachi, FGV, Perodua, Maxis, SIRIM, and Principal Asset Management CIMB. In addition, he also trained several in-house Big Data Strategy workshops for organizations such as OCBC, Keysight, TNB, TM One, and Johnson & Johnson. During the MCO period, Peter conducted several 2-day Big Data Strategy public virtual classes for Citibank, Alliance Bank, Bank Islam, Intel, Osram, Dell, Sarawak Energy, Optics Balzers, Penang Port, and Maxis Broadband. Most of his audience who had attended his in-house and public classes are senior managers and above. Peter’s core professional strengths include data science & big data strategies and thought leadership. His industry experience includes the media, internet, manufacturing, FMCG, e- learning and agriculture.

Ng Keng Fai

Lead Trainer and Data Science Lead at Growth.Pro Data Science Consulting & Training

Keng Fai is the Lead Trainer and Data Science Lead at Growth.Pro Data Science Consulting & Training, where he designs interactive corporate training programs in AI, data science, and machine learning. He also develops frameworks and prototypes for AI-driven solutions, helping businesses integrate cutting-edge technology into their operations.

With a diverse background spanning academia, entrepreneurship, and industry, Keng Fai has previously lectured at UniRazak and served as a vocational trainer at New Era Institute of Vocational and Continuous Education (Kajang). He also founded and led software development teams in fintech and edtech startups, combining his expertise in marketing, software engineering, and AI to deliver impactful solutions.

Keng Fai also brings corporate training experience across manufacturing, fintech, and financial institutions, equipping professionals with future-ready AI and data competencies. He has led specialized workshops and upskilling programs for manufacturing giants, trained fintech teams on AI-powered automation and fraud detection and conducted analytics and AI strategy sessions for banks and financial service providers.

His cross-industry experience includes robotics/mechatronics engineering, with roles in oil & gas and manufacturing at Flextronics. Additionally, he has consulted for and coached clients such as Beyond Insights, Plus Minus Zero, and Watson-Marlow, helping them leverage AI and data-driven strategies.

During the Malaysian Movement Control Order (MCO), Keng Fai conducted e-training sessions for entrepreneurs across industries, covering marketing, web development, and data analytics for clients like TOC Automotive College and La Gourmet. His expertise also earned him an invitation to Astro AEC’s《企业棒帮忙》SME Great Helper, where he advised on digital transformation for the music industry.