Excellence in Digital Banking International Summit

Explore the tipping point where standardization enhances control, transparency, and efficiency and where it becomes a barrier to agility, innovation, and local business needs.

Unpack real‑world challenges of implementing group-wide standards across a large bank with diverse,non‑licensed subsidiaries operating under different regulatory expectations and risk profiles.

Discuss pragmatic pathways forward: how to harmonize what must be uniform while preserving the flexibility needed for specialized business models and sustainable, risk-aware growth

• The shift toward AI-powered credit scoring for more accurate borrower assessment
• Rising importance of alternative datasets, including behavioral and non-traditional indicators
• Adaptive pricing methodologies that respond to real-time market changes
• Challenges of modelling credit risk during periods of heightened economic uncertainty

• Implementing AI chatbots that provide instant customer support and basic financial guidance across all digital channels
• Developing natural language processing capabilities for complex financial queries and transactions
• Creating seamless handoffs between AI assistants and human agents for high-touch interactions
• Measuring and optimizing conversation success rates and customer satisfaction metrics

• Beyond Automation: When Banks Start Thinking
• The Intelligent Banking Blueprint: From Automation to Autonomy
• Smart Collaboration Between Fintechs and Banks in Autonomous Banking Models
• The Self-Learning Bank: Transformation Through Data, Talent and Trust

• The Payments Landscape in 2026: Driving Fraud Prevention and Innovation

• Expanding partnerships between banks, fintechs, and tech providers to create seamless, interconnected financial experiences
• Empowering customers with greater control over their financial data and access tosmarter, integrated services
• Monetizing open data through innovative business models and personalized financialproducts
• Ensuring trust and transparency as the foundation for a thriving open financeenvironment

• Understanding how emerging risks are reshaping credit risk dynamics and stress testing frameworks.
• How geopolitical fragmentation, cyber threats and AI adoption are creating new sources of credit risk
• Transmission channels. How operational disruption, supply chain shocks and technology dependencies translate into credit outcomes
• Emerging concentration risks linked to digital infrastructure and financial market interconnections
• Implications for credit risk management. Scenario analysis, portfolio monitoring and governance frameworks

• Competing with Neobanks through Innovation
• Building Customer-First Products in Legacy Systems
• Supporting SMEs' Liquidity in Wartime Economy

• Creating an adaptive environment where innovation thrives through system-level governance, human-AI
• cooperation, and continuous literacy.
• Evolving strong oversight practices to meet the unique challenges of a world with agentic AI systems.
• Sharing emerging patterns for oversight to enable organizations to embrace the power of autonomous AI
• safely and responsibly

• The foundational pillars of Data Quality - to prevent Garbage In Garbage out scenario.
• Risk Data Aggregation as Strategic Imperative to enhance decision-making and crisismanagement
• Practical framework for implementation, that integrates data governance, tech andpeople

• Model Development & Landscape – Overview of the banking modelling ecosystem, defining what
• constitutes a model (business & regulatory), and comparing machine‑learning versus traditional
• approaches with respect to explainability, governance, and suitability for alternative data.
• Model Risk Management (MoRM) – How models are governed across their lifecycle, why MoRM is
• critical for regulatory compliance and responsible model use, and which validation dimensionsapply (data, methodology, performance, stability).
• Alternative & Traditional Data for Credit Risk – Role of complementary (orthogonal) data sources,
• overview of traditional and alternative data (notably transactional and behavioural), and identification of gaps alternative data can address in existing models.
• Use Cases & Business Impact – Practical application of transactional data in credit risk and portfolio
• management, methods to assess incremental model value, and translation of improved insights into better risk outcomes and business decisions.

Integration of climate risk into credit risk models, including IRB, IFRS 9, stress testing, and Pillar 2 frameworks

Modeling approaches, regulatory expectations, key challenges, and data-related considerations in this emerging area

Early initiatives by banks within the GCD consortium to collect climate risk data and develop methodologies

The role of academic research in supporting methodological development and laying the groundwork for future regulatory capital requirements

AI for Credit Risk Management: case uses

As AI allows banks to understand customers like never before, where should the line be drawn

between helpful insights and intrusive data use?