Category: Stream 2

Drinks & Networking

Livestream Particpants: Thank you for joining us, you will receive follow-up communications with all of today’s content in the coming days.

Attending In-Person: From causal to casual, join us for networking and drinks.

Coffee Break

Livestream participants: stretch your legs, get your afternoon refreshments, and join us again in 30 minutes

Attending in-person: feel free to mingle, stretch your legs, or find some refreshments in the Marble Hall and City of London Room

Lunch Break

Livestream Participants: You have exclusive access to Nubank’s Matheus Facure discussing “Continuous Treatments: Challenges from the Banking Industry”

Attending In-Person: Lunch is served in the Marble Hall and City of London Room. Enjoy!

Coffee Break

Livestream participants: stretch your legs, get your mid-morning refreshments, and join us again in 30 minutes

Attending in-person: feel free to mingle, stretch your legs, or find some refreshments in the Marble Hall and City of London Room

Breakout to Sessions

Livestream participants: choose your livestream option.

Attending in-person: stay in the Kohn Centre or head to the Conference Room upstairs.

SLB: Semantic causality and causal-AI, enabling discourse for trust in real-time root-cause analysis

Counterfactuals enable exploration of solutions in causal-AI. In addition to counterfactual exploration of a causal-AI network, we introduce search-and-summary to provide a rich discourse between the engineer and the AI system. Working in the area of operational risk of non-productive time (NPT), we begin from semantic cause-effect chains from root-cause-analysis. These are interwoven in a primarily knowledge-driven causal-AI structure. Population of a large subset of the initial conditional probability tables is through physics models.That partially trained structure is then refined on scenarios from historical operations and expert-driven hypothetical situations. In deployment a behaviour tree architecture enables a neurosymbolic combination of human and IoT establish the right risk model for the right context. The results are analyzed to identify the key paths through the causal-AI, recovering specific semantic cause-effect chains corresponding to root causes. These are used in two ways: firstly they can be passed for summarization by LLMs, where we use roundtrip verification and access to underlying human-verified text; secondly, the causal-AI result is used in a skyline multicriterion search across the historical and hypothetical cases. We present the details of the complete system and highlight the role that causal-AI plus semantic web can play in enabling discourse on operational risk.

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  Michael Williams Principal AI Research Scientist, SLB

BBVA: Causality in Retail Banking at BBVA AI Factory

In retail banking, understanding the causal relationships between variables is crucial for making informed decisions that enhance customer experience and optimize financial outcomes. At BBVA AI Factory, causal inference techniques are improving our approach to complex challenges where traditional A/B testing is infeasible or removing bias becomes a hard problem. Also, it has significantly enhanced financial health metrics and risk management processes. By utilizing causal inference, we can find out the true impact of various interventions on customer behavior and financial stability, leading to more accurate risk assessments and tailored financial advice. This approach addresses the limitations of conventional experimental methods, which can be impractical or ethically challenging, by providing robust alternative solutions. This presentation will showcase three detailed case studies and quantifiable outcomes, highlighting the practical benefits and advancements that causality offers in retail banking analytics. We will deepen into how we’ve enhanced our recommendation systems and embedded causal inference in our analytics framework at BBVA AI Factory. Attendees will leave the room with a deep understanding of causal AI applications in the financial sector.

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  Álvaro Ibraín Rodríguez Expert Data Scientist, BBVA AI Factory

BBVA: Causality in a real use case in banking

In retail banking, understanding our clients is critical to offering them the best solutions and products. This talk presents an innovative approach based on causality to calculate optimal financial discounts aiming to benefit both the institution and its clientele. Traditional machine learning techniques fall short in this scenario due to the impossibility of making randomized controlled trials and the presence of confounding bias in observational data. We demonstrate with preliminary results on a real use case how causal inference methodologies can overcome these limitations, enabling us to estimate the effect of financial discounts. Our approach allows for interventions and counterfactual analysis, which is crucial for determining personalized optimal discounts. This presentation will detail our end-to-end causal inference pipeline customized for our problem but adaptable to other use cases. It will cover phases like covariate selection, causal discovery, and effect estimation, among others. We will describe our comprehensive approach, including the application and comparison of multiple causal discovery and estimation algorithms. Furthermore, we will contrast the outcomes of our causal inference pipeline with traditional ML-based methodologies, illustrating how causal inference effectively corrects biases that ML cannot address.

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  Clara Higuera Cabañes Lead Data Scientist, BBVA
• 
  Javier Moral Hernández Senior Data Scientist, BBVA

PANEL: Exploring Causal AI’s Potential in Responsible AI and Risk Management

Join an interesting discussion exploring how causal approaches could reshape responsible AI practices, influence policy decisions, and drive innovation in the financial sector. Drawing from their extensive experience in AI ethics, strategy, and data-driven decision-making, our panelists will offer unique insights into the challenges and opportunities presented by causal AI. This forward-looking conversation will focus on the strategic importance of causal AI and its implications for businesses navigating the complex landscape of AI adoption and implementation – providing valuable insights for professionals across industries.

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  Maria Axente Head of Public Policy & Ethics, PWC
• 
  Ari Cohen Chief Data Officer EMEA, Macquarie Group

BCA: Causality in spatio-temporal graphs

Graph theory and causal analysis are under-used tools in Data Science in general, but we rarely talk about their data discovery potential. By using these techniques, we were able to discover a cost-saving opportunity in logistics that traditional analysis and ML was unable to identify. In addition, the visually approachable nature of graph theory made the opportunity explainable to business stakeholders. This combination of data discovery and interpretability by a non-technical audience makes causal analysis a uniquely powerful tool for quickly solving business problems that require the cooperation of inte

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  Robert Nicholls Head of Data Science, BCA

Dunnhumby: Solving causal problems in practice: applications and learnings from retail

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  Dimitra Liotsiou Senior Research Data Scientist, dunnhumby

Bergfreunde: “Decoding eCommerce” – Facing and solving technical challenges implementing Causal Modeling at Bergfreunde.de

“Decoding eCommerce” – Facing and solving technical challenges implementing Causal Modeling at Bergfreunde.de” – Find out about the technical challenges (and solutions) Europe’s largest Outdoor eCommerce Retailer Bergfreunde.de (known in the UK as www.alpinetrek.co.uk/) had to face implementing a Causal Model to inform business steering. The focus of this talk will be on exposing the statistical and technical challenges faced, and some of the learnings and solutions implemented.

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  Alexander Dzionara Data Scientist, Bergfreunde

TIM: TIM AI Strategy: Leveraging Predictive and Causal AI

The presentation outlines TIM’s strategic approach to harnessing the power of Artificial Intelligence. Starting with predictive AI models that drive customer insights, churn modelling, and operational efficiency, TIM is now advancing towards the next frontier: Causal AI. By integrating causal inference, TIM aims to enhance decision-making, moving beyond prediction to understanding the “why” behind customer behaviour, optimizing interventions, and shaping a data-driven future.

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  Clara Fabiola Oliva VP, Data Analytics, Artificial Intelligence & Customer Insight, TIM
• 
  Nicola Vizioli Strategy and Data Analyst, TIM

Closing the day

A summary of the day and a chance to get any final questions in.

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  Darko Matovski Founding CEO, causaLens