• Integration challenges from emerging tech impacting data flow, emphasising aligned security and governance strategies for data integrity and compliance.
• Design strategies and scalability considerations for future readiness.
• Evaluation of current systems, creating integration plans, and setting governance protocols for seamless tool integration.
• Importance of collaboration, continuous monitoring of tool performance, and training to relevant teams to ensure alignment with business goals.

• Designing coherent data architecture in order to streamline data management processes
• Implementing systems for improved data quality in order to have clean, accurate and consistent data, and how this is essential for accurate for AI Models
• Implementing lifecycle management strategies to optimize storage costs
• Enhancing real time data processing to enable greater insights and decision making
• Reducing and rationalising 3rd party data and software licensing costs
• Gaining buy in from the wider business

• Exploring and understanding the challenges, strategies, and triumphs encountered, to discover actionable takeaways to elevate data initiatives.
• Highlighting the importance of strategic alignment, effective communication, and a clear vision
• Fostering organizational transformation by promoting data-enabled cultures

In today’s environment, it can be difficult for businesses to keep up with customer expectations. Organisations need to innovate to deliver the best products, while also modernising legacy applications and systems. Every journey to a more modern data architecture is unique and driven by specific business initiatives, but without the proper planning and execution, it can become an expensive, time-consuming project. In this presentation, we’ll discuss one customer’s challenges and solution for delivering resilient, real-time replication from traditional on-premise systems to the latest cloud analytics platform.

• Exploring how to begin your data journey, shifting the mindset to empower business units to own and leverage data effectively.
• Establishing a unified vision for the data transformation process, ensuring it resonates across all functions and teams within the organisation
• Analysing the current state of the data lifecycle, emphasising its alignment with modern data practices.
• Outlining the future vision for data management and adapting to new technological landscape.
• How to align data architecture with business strategy – streaming

• Integrating big data technologies and event-driven architectures, bridging gaps between traditional and modern systems within highly regulated spheres.
• Encouraging continuous improvement and evolving practices to advance maturity level and better meet the dynamic needs of the business environment.
• Highlighting the role of data architecture in the future roadmap, particularly in its integration with AI and emerging technologies.

• In today's rapidly changing business landscape, the ability to access and act on relevant, real-time insights is more critical than ever. However, the journey to achieving this can often be complex and filled with challenges. From the integration of disparate data systems to the balancing of stakeholder expectations, businesses face a multitude of obstacles when seeking to streamline their data processes. Join this session to explore these common challenges and offer insights and solutions to help businesses navigate this exciting journey and unlock the full potential of real-time business insights.

• Recognising the transformative power of treating data as a strategic asset, reshaping mindsets and sparking innovation.
• Embracing Agile Development principles for iterative refinement in crafting resilient and adaptable data architectures.
• Revolutionising data management with cutting-edge AI and ML technologies, unlocking unprecedented value through intelligent architectural design.
• Safeguarding data integrity and privacy through AI-driven governance strategies, ensuring robust security measures are integrated from the ground up.

• Discovering a proven Cloud modernisation strategy with a logical data platform to migrate enterprise applications, data platforms, and information resources to the Cloud.
• Exploring logical data platform for a strategically agile, highly reduced risk, and cost-effective technology foundation.
• Discussing the challenges and pitfalls of cloud modernisation and how Logical Data Platform can ensure the success of your Cloud modernisation initiative.

This presentation explores the journey of implementing a new cloud data platform and enterprise architecture to propel digital transformation. Highlighting the platform's foundational role in shaping AI and digital strategies with a customer-centric focus, it emphasises embedding curated data services to instigate a mindset shift towards data-driven decision-making. Practical insights include BI self-service tools, automated data governance, and leveraging cloud data markets, empowering organisations to advance along the analytics maturity curve and thrive in the digital age.

• How to spend less time on data management and more on value-creation activities
• Best-practice architectures for mobilising your data efficiently and cost -effectively
• Ways to accelerate data maturity for your organisation

• Leveraging a data governance perspective to unify data architecture, risk, and compliance teams.
• Exploring essential considerations for crafting a robust data architecture that aligns with governance objectives.
• Discovering strategies and challenges encountered while bridging the gap between data architecture and governance for seamless collaboration.

• Delving into the process of fine-tuning data architectures for maximum efficiency, focusing on design considerations and implementation strategies.
• Aligning data architecture with specific business use cases, illustrating how tailored structures directly contribute to achieving organisational objectives.
• Exploring the tangible economic benefits derived from a well-crafted data architecture, emphasising how strategic decisions in this realm can directly influence commercial values and outcomes.
• Discovering practical insights and actionable takeaways, providing a roadmap for integrating business-centric data structures to enhance overall value and success.

Discover the essentials of building a centralised modern data platform. Learn how to integrate diverse data sources, leverage advanced technologies, and implement best practices for improved data quality, agility, and scalability. Gain insights into real-world examples and walk away empowered to unlock the full potential of your data assets.

• Achieving clear and persuasive communication to effectively convey complex architectural visions to diverse stakeholders.
• Aligning architectural visions with overarching business goals, emphasising the transformative impact of data architecture.
• Engaging and influencing stakeholders across all levels, gaining support and buy-in for architectural initiatives.
• Developing leadership abilities to foresee risks, adapt strategies, and propose scalable solutions within architectural visions, ensuring adaptability and resilience.

• Discussion of the advantages of using low-code and no-code platforms, including faster development times and increased efficiency 
• Overview of the challenges that may arise when implementing these technologies 
• How low-code and no-code platforms can improve collaboration and communication within a financial organization 
• Examining the security and compliance considerations that must be taken into account when using low-code and no-code platforms 

• Learn how advanced AI techniques can be used to create personalized consumer profiles for risk assessment and targeted marketing 
• Using AI to create consumer profiles 
• How consumer profiles can improve consumer loyalty 
• Understand the benefits of using machine learning and deep learning methods for personalized risk assessment 
• Discover how to use AI-based techniques for targeted marketing strategies to improve customer engagement and increase revenue 

• What are the new technologies to be aware of? 
• Tools & techniques to help plan for a stable future & gain a competitive advantage 
• What changes are happening in the financial sector around AI? 
• What challenges have arisen recently in fintech? 

• Learn about the latest developments in generative AI and its applications in the banking sector 
• Understand how generative AI can be used to improve financial forecasting, risk assessment and decision-making in banking 
• Discover how generative AI can be used to create new financial products and services such as synthetic data, virtual assistants and personalization 
• Explore the use of generative AI in various banking applications, such as fraud detection, customer service and compliance management 
• What is NLG and its uses in the financial sector 

• Are we heading to a more automated future? 
• Overview of the latest advancements and trends in AI and data and their applications in the financial services industry. 
• Discussion of the benefits of using AI and data in financial services, such as increased efficiency, improved decision-making, and cost savings. 
• Examination of the challenges and limitations of using AI and data in financial services, such as data privacy and regulatory compliance 

• How AI can be used to automate various aspects of the payment process, such as verification, validation, and reconciliation. 
• What are the benefits of using AI in the payment process?  
• Learn about the specific techniques and technologies used in AI-enabled payment systems, such as machine learning, natural language processing, and robotics process automation. 
• How AI can be integrated with other technologies, such as blockchain, to further enhance the payment process's security, speed and scalability. 

• Introduction to Identity Verification and its importance in financial transactions 
• Current methods of identity verification and their limitations 
• Introduction to Artificial Intelligence and its potential in identity verification 
• How AI can improve the accuracy and efficiency of identity verification 
• Use cases of AI in identity verification in the financial industry 
• Biometric authentication methods using AI (facial recognition, fingerprint, voice recognition, etc.) 
• The impact of AI on fraud detection and prevention in financial transactions 

This presentation explores how organizations of all sizes can leverage AI to meet AML Compliance. While traditional rule-based systems are easily explainable, quick, and easy to implement, they result in low true positive rates, operational overhead, and failure to adapt to changing environments. The adoption of an AI-based approach allows companies and government institutions to achieve six-fold improvement in suspicious transaction activity detection while achieving scalable operational efficiency. This presentation also explores how AML operations will be transformed into analytics-driven MLOps that will continuously provide insights and improvements to the AI system, uncovering even the most hidden patterns. With recent advances and more widespread access to machine learning tools, AI-based AML systems will become a dominant force in the future of Financial Compliance. 

In this presentation you will:
•    Learn how to translate money laundering topologies into machine learning data.
•    Discover how to transition from a rule-based to an AI-based AML transaction monitoring system.
•    Explore the pros and cons of unsupervised learning to spot suspicious activity.
•    Establish a continuous AI improvement loop with the help of AML specialists.
•    Gain insights on how to implement an AI system while meeting AML regulatory requirements.

As a Principal Product Manager in SumUp, Yev is driving the vision and strategy for the Machine Learning Platform in SumUp for financial use cases in close collaboration with data science, engineering, and product teams to deliver ML-powered products that customers love.

• Overview of the current landscape of card and payments fraud, including common types of fraud and their impact on businesses and consumers 
• Discussion of the potential of machine learning and other AI-based techniques for detecting and preventing fraud 
• Examination of the challenges and limitations of using machine learning for fraud detection and ways to overcome them 
• Explore Use-Cases of how banks and financial services are combatting card and payment fraud