• Productionising existing machine learning models to yield maximum profit
• What’s on the horizon for generative AI in the financial services?
• Preparing for the increase in streaming data and analytics infrastructures to excel in AI operationalisation
• Ensuring value with necessary operational rigour to keep all processes synchronous and working seamlessly

• Situational know-how is hard to impart in a traditional training class, what’s the solution?
• Hear about proactive, personalized guidance using AI technology, delivered from modern knowledge hubs
• Learn about a use-case for such guidance, where the capability augments the conversation of a banker with a consumer, as they assess a customer’s situation and recommend a personalised solution

Speakers

Stephen Kennedy

Director of Solutions, eGain

• Rethinking how to participate in digital ecosystems by using AI to harness the full power of data available from new sources
• Implementing clear AI strategy to move from experimentation to scaling AI across your organisation
• Practical ways in which firms can overcome the challenge of deploying AI capabilities at scale
• Adopting AI at scale as the foundation for new value propositions and distinctive customer experiences
• Scaling up the size of data sets to accelerate growth and digital transformation

Speakers

Michael Natusch

Chief Science Officer,Prudential

• The challenges surrounding bias AI results
• Exploring explicable AI (EXAI) to ensure customers receive sensible answers to denied services
• The possibilities of guaranteeing fair outcomes far from discriminatory results
• Dealing with unpredictive models resulting in macro implications

Speakers

Jason Maude

Chief Technology Advocate, Starling Bank

• Overcoming real data usage restrictions and avoiding data breeches with synthetic data
• How to make the most out of synthetic data
• Getting on board with an emerging concept
• Exploring synthetic data’s usability as a substitute for data that contains sensitive information

Speakers

Deepak Paramanand

Director of AI Research & Product Management, JP Morgan

• Ensuring trust is at the centre of how businesses develop and use AI ethically and responsibly
• Automating KYC compliance: revamping risk profiling to improve accuracy, reliability and save time and money
• Hear how Europe's new AI Act will impact the progress of ethical AI
• Setting standards for ethical model development, deployment, and monitoring across the industry

• Make your business a one-stop hub to modernise your client interaction workflows
• Streamline your client business processes in banking across account onboarding, account servicing, and exception handling
• Rethinking today’s costly, inefficient, and fragmented client engagement model, which requires significant manual intervention
• Orchestrate each step of your workflows to fast-track client business, gain efficiency, and grow your business.

• Taking ESG credibility further with NLP and spatial AI as they become leading technologies in the space
• Factoring current social conscious into AI and ESG strategy
• Identifying material risks and growth opportunities by applying ESG criteria to business analysis processes
• Guaranteeing ESG transparency with AI in emerging markets

• Uncovering the powerful potential of Chat GPT: ally or enemy to innovation?
• Are we doing right by clients by adopting it?
• What are the true business drivers and decision points of Chat GPT in the financial services?
• Preparing for the next wave of AI: ensuring a successful pipeline to use this technology effectively

Speakers

Nathan Huntley

Data Science Manager, Atom Bank

Ines Rigaud

Innovation Analyst, HSBC

• Driving better customer experiences with data-driven approaches
• Optimising customer journeys powered by smarter personalisation
• Unearth new ways to foster AI to create people-centric experiences at scale
• Pioneering the chatbot and Chat GPT collaboration: leveraging large language models to fine tune chatbot technology

• Achieving the right level of data granularity to ensure positive impact on the accuracy and usefulness of AI results
• Where does the latest data governance sit in the data quality ecosystem?
• Can we ever simplify maintaining data quality when dealing with so many data changes?
• Making intelligent decisions on how best to serve your customers with high quality data
• Finding the balance for traditional banks between dependency on legacy systems and embracing AI models to avoid falling behind

AI in Financial Services: Transforming customer experience in the era of digital transformation
• How can AI be effectively integrated into financial services to enhance customer experience?
• How can institutions ensure the ethical use of AI in financial services?
• What are the key challenges and opportunities of implementing AI in financial services?
• Discussion of current trends, best practices, and case studies of AI implementation in financial services What’s the future of AI in the industry and its potential impact on customers?

Speakers

Stephen Kennedy

Director of Solutions, eGain

• How can you evidence explainability, robustness, privacy, and fairness within existing model risk management frameworks?
• Adapting and integrating model risk framework into business whilst dealing with elements of subjectivity
• Should all AI be a model?
• Hear about associated AI safety standards that are being applied across the financial services sector

• Incorporating pre-emptive risk management to get ahead of the criminals
• How to connect the abstract potential of AI in financial crime risk management to regulatory expectations
• Hear practical and actionable processes for deploying AI in the war against fraud in the current unstable economic market
• Optimising machine learning for data-driven fraud detection

• Hear about how key developments of the EU AI Act will impact the sector and aim to harmonise rules on AI systems
• Looking to regulation to improve the speed at which AI is implemented
• Drafting documentation for new UK Consumer Duty: enable and support customers to pursue their financial objectives with AI
• How can the industry prepare for future regulatory restrictions?

• AI/ML tools hold significant potential for managing FS data
• Amidst the hype, how can we best manage the novel risks and new challenges that AI poses?
• Frameworks to ensure models are explainable, maintain privacy, are robust, causal, and fair, can help to mitigate these new risks
• We’ll look at how these frameworks can help to ensure safe and responsible AI becomes embedded within organisations, and aligns with regulatory guidance

Speakers

Jamie Farnes

Lead Data Scientist, Nationwide Building Society