Abi Giles-Haigh
Capgemini, Director of Analytics and Innovation
Newcastle upon Tyne, United Kingdom
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Dr Abi Giles-Haigh has over fifteen years of experience working with data from database management, report writing, advanced analytics, and AI. Abi is a technical evangelist in predictive/prescriptive analytics and data-informed decision-making. She holds a PhD in computational modelling and a Bachelor of Science in computing science from Newcastle University. Abi is also a coach and analyst for Newcastle United Women’s.
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Detecting Fraud in the Galactic Empire: Leveraging Machine Learning and Graph Technology
In a galaxy far, far away, financial fraud poses a growing threat to the stability of interplanetary systems. This session explores the application of machine learning and graph technology to uncover fraud cases within the Galactic Empire. The resistance is fighting back and employing a new state-of-the-art 23ai database from Oracle.
An ML model that identifies suspicions by constructing a multidimensional graph with nodes representing key figures such as Jedi, Sith, smugglers, and bounty hunters and edges denoting transactional relationships.
The study incorporates advanced algorithms like decision trees, random forests, and neural networks (NNs) to detect anomalies that could indicate fraudulent activity. The results showcase how these technologies can efficiently pinpoint deceptive actions hidden within complex interstellar dealings, offering a powerful tool for ensuring financial transparency across the galaxy.
Additional Notes:
The findings are illustrated with fictional case studies involving notorious Star Wars characters, demonstrating how ML and graph-based analytics can uphold justice in the face of galactic fraud and allow the resistance to beat the dark side. The demo will enable people to walk away with new skills to apply to there business.
Technology: Oracle 23ai, Graph, Data Science
Soccer: How to win the league
Getting started with Machine Learning can take time and effort. Still, in this session, we will explore the world of Oracle Machine Learning (OML) to analyse women's soccer data, uncovering performance trends, player insights, and team strategies. We will explore anomaly detection, AutoML, and clustering - and, more importantly, show you how you could get going with OML.
We apply advanced algorithms to reveal critical patterns that drive success, from tactical formations to individual contributions.
Our approach enhances coaches' and analysts' decision-making, pushing the boundaries of data-informed performance analysis in women's soccer.
Additional Notes:
Gartner’s hype cycle in 2024 indicated becoming AI-ready was the key theme. Part of ‘getting ready’ is understanding ML processes, how to use Auto ML, and how to interpret and use the results within the business. This talk will take the audience through this preparation and make people feel more confident when talking about ML.
Technology: Oracle 23ai, Data Science
Does anyone know what the Policy is?
In this session, we will go through the world of knowledge Retrieval-Augmented Generation (knowledge RAG) to simplify and extract insights from complex policy documents.
RAG enhances comprehension by combining natural language processing with intelligent data retrieval, distilling lengthy, dense texts into clear, actionable information.
But let’s go one step further. Let’s chat to those documents and ask questions. No more control F; ask a question about the policy. We will discuss the pros and cons of this approach, enabling the audience to decide where they might take it next.
Policy documents such as HR, Health and Safety, etc. can be long and difficult to understand. With subsections and interconnected sections, they can quickly become a maze. So why not use Vector Search with a chatbot front end to talk to the human needing the answer? Well, here, we will demonstrate exactly that.
Technology: Oracle 23ai, ChatBot, Data Science
How I utilised OAC to predict Euro 2024
As Euro 2024 approached, I decided to use OAC to predict the outcome of goals within games during the tournament. Along the way, I did data discovery, visualisations to tell a data story, and Machine Learning to make predictions, all within OAC.
I shared all of these with the world on social media channels, and a few of us were excited when England v Spain was the final while I was at Kscope24. In this session, I will reveal the final part of the puzzle and how well OAC predicted the competition, with insights on how I did the analysis in data flows, Data Visualisations, and ML algorithms.
Additional Notes:
OAC can be used in many areas, from data exploration, merging, and preparing data to data storytelling. This session showcases the best of OAC in these areas, with a real story about how it changed my viewing of Euro 2024 and a little surprise at the end.
Technology: Oracle 23ai, OAC
Abi Giles-Haigh
Capgemini, Director of Analytics and Innovation
Newcastle upon Tyne, United Kingdom
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