AI in Finance Summit AI in Finance Summit schedule

Evolution of AI in Finance: The Journey From POC to Deployment

Abstract: Financial industry as compared to other industries is very young when it comes to applying sophisticated machine learning techniques to their data problems. There are a huge number of challenges faced in global organisations that come in the form of technical debt, legacy code, outdated infrastructure, bad data practices, regulatory restrictions and so on. In such an environment, running a data driven machine learning algorithm that often comes with black box methods can prove to be really challenging. This talk will address some of the various challenges faced in the industry including explainability and how some of that can be mitigated more effectively. It will also address the distinction between roles for data scientists, machine learning engineers as well as analysts and how a more collaborative effort along the various stages of the pipeline will help drive projects from poc levels to deployment when running large scale projects in a global team.

Roshini has a background in AI and electronics from Edinburgh university. She has more than eight years of experience in applying machine learning techniques to design scalable robust solutions in the fields of e-commerce, travel and finance. She has worked with modelling user behaviour, preditive models, recommendation systems, generative adversarial models with deep learning frameworks and is currently working on models in finance to assess risk. She is very interested in understanding how AI techniques can be applied in various industries to make them more efficient and accurate. She is also very passionate about encouraging more women to enter and lead in this field and runs the London chapter for women in machine learning and data science. In her free time she dabbles with creating artwork with neural style transfer and travel photography to show how easily AI can be integrated with day to day activities and enhance our creativity.

Recent Developments in Deep Learning in Finance

Harsh currently works with Morgan Stanley in Quant Analytics Group. He started his career as a programmer focussed on developing data driven algos in the areas of speech recognition, image processing and bioinformatics. He then moved to financial risk management and over the last 12 years has worked in various roles through the life cycle of models. In these roles, he has been continuously enthusiastic to applying machine learning in problems related to behavioural assumptions, data quality, recommender systems, model benchmarking and text analytics. His current role requires him reviewing all Machine Learning models used by the firm and providing direction to shaping AIML governance framework and strategy. He is also a visiting lecturer with universities and training institutions.

Accelerate your AI Financial Modelling with IPU In the finance sector, the potential for innovation with advanced machine intelligence is significant. But often, new and complex models are not being fully leveraged due to latency issues and compute restraints. Enter the IPU – a completely new processing architecture designed for machine intelligence, capable of running advanced financial models up to 26x faster. Helen Byrne from Graphcore explains how the IPU’s unique architecture can power such incredible breakthroughs – and what this means for the future of finance and trading.

Helen joined Graphcore in July 2018 as an AI Research Engineer focussed on distributed learning in large-scale machines, before moving to her current role working on customer applications on Graphcore IPUs. She has a BSc in Mathematics and a Master’s degree in Artificial Intelligence. Before joining Graphcore, she was a Maths teacher and worked at an Investment Banking FinTech.

AI in Banking on the Cloud

Angela will talk about the drive for Financial Services organisations to move to the cloud and how this may advantage technologies such as AI, Machine Learning, Big Data and Analytics. Angela will talk about how to set up a vision and strategy and drive through implementation.

Prior to working at Lloyds, Angela worked At Deutsche Bank in the Cloud, Anti-Financial Crime Analytics, Data Quality and Finance Risk and Regulatory Reporting change and transformation teams. Before joining Deutsche Bank, Angela worked as a Senior Manager at EY across Asia Pacific and EMEIA within the Financial Services Organisation focusing on Corporate Banking and Capital Markets transformation and advisory programmes. Angela started her career at IBM in Australia working as a developer, tester, designer and then business analyst and project manager. Angela has an honours degree in Mechanical Engineering (Mechatronics / Robotics) and Computer Science from the University of Melbourne, Australia.

Using Deep Learning with Word Embeddings to improve Customer Satisfaction

Understanding customer satisfaction in retail banking requires exploring and comprehending multiple sources of feedback, such as emails, social networks reviews, web feedback, bot interactions, as well as speech-to-text transcripts collected from call centers. Since such a vast amount of textual data can be difficult to leverage with traditional text mining techniques, deep learning and word embeddings can be used to automatically classify and label feedback, and then deeply analyze and understand their content. In this communication we explain how we leverage all those AI techniques to get an in-depth understanding of the opinions and needs of National Bank’s retail customers. We also show how we improve the performance levels of those AI tools using in-house algorithms and data resources to improve the overall capacity of natural language understanding.

Eric Charton hold a Master in machine learning applied to voice recognition, and a Ph.D. in machine learning applied to Information extraction and natural language generation. He worked as scientist and research project coordinator in academic context in Europe (University of Avignon) and North America (CRIM, École Polytechnique de Montréal) before becoming head of search engine research and development at Yellow Pages Canada. Since March 2018, he is Senior AI Director at National Bank of Canada.

How Space-Based Technology can Hedge Financial Strategies

Artificial Intelligence is on the verge of transforming man-controlled spacecraft into cognitive machines that will soon drastically change the way we used to do space exploration. However, state-of-the-art AI technology is not only used by the European Space Agency (ESA) to improve planetary landing, exoplanet hunting or to increase autonomy in spacecrafts and robots. ESA’s Business Applications and Space Solutions supports FinTech companies to boost their technological capabilities by making use of space assets and technologies, therefore helping to develop breaking-through financial strategies and services. Examples or these include prediction and detection of stock market anomalies using ESA’s most advanced AI-based algorithm for spacecraft anomaly detection or support hedge funds and investment firms mapping energy and metal commodities activity or additionally evaluating credit risk through AI-enhanced geoinformation based on ESA’s Earth Observation Satellites. In this talk we will go through some of the key AI technologies being used by ESA in communications, operations and space exploration and explain how Fintech industry can engage with ESA to make use of them. We will also give an overview of the work performed by some of the brightest Fintech companies already using space technologies, sponsored by ESA’s Business Applications and Space Solutions.

Tomas Navarro is a Space Segment Engineer at ESA (European Space Agency) working on the Advanced Research in Telecommunications Systems (ARTES) programme. Thomas coordinates Artificial Intelligence activities for Space applications. Before joining ESA, Tomas worked at INMARSAT in satellite operations and as a R&D engineer for satellite & payload optimisation systems, focusing on genetic and machine learning algorithms used in operational geostationary communications satellites. Tomas is also the co-inventor of an Artificial Intelligence-powered Space architecture able to improve response times in systems based on a centralised topology. His invention is planned to be used in Space systems to distribute intelligence across a multi-node network, aiming at optimising resources while reducing spacecraft control time. Tomas works also with ESA Business Applications to understand how to apply this architecture to financial systems in order to reduce financial transaction times and optimise flow of information across different nodes.

Putting the Customer at the Heart of AI in Payments

Payment transactions provide one the largest, richest data sources on the planet. As the world’s leading payments provider, Visa is driving advances in data science and AI for the benefit of customers, the businesses that serve them, and society more broadly. As well as discussing some of the key use cases, this session will also look at what businesses need to do to empower consumers and engender public trust in these new technologies and use cases of data.

Jessica is a Senior Director of Data and AI Initiatives in Visa’s Global Strategy team. Her work covers data privacy, AI ethics and related policy, regulation and business development. Her previous roles at Visa include Director of External Affairs for Visa’s Data Science Lab, and Head of UK Regulation and Public Affairs. She has over ten years’ corporate affairs experience including lobbying, communications and reputation management. She has worked for political parties, businesses (start-up to FTSE 100), consultancies, trade bodies, think tanks and NGOs. Her particular expertise is highly regulated, technology-driven sectors including digital, data and AI, telecoms, energy and fintech. She was previously a capital markets solicitor at Linklaters, having completed an undergraduate degree in Law from Oxford University and a Masters degree in Political Theory from the LSE.

Fraud Detection in 2020 - Bad Guys Perspective

AI is evolving rapidly these days, and together with it are our fraud detection systems. I want to show you what is the current state of the art approach to fraud detection, how are such systems implemented, and what are key differentiators to look at when choosing a solution for your business (AML, credit card frauds and insurance). Next we will focus on credit card frauds, but not from a payment provider or a bank perspective but a criminal. Learn more on what are the newest technology trends for card frauds, how bad guys build their infrastructure and how they cheat and manipulate your million dollars black boxes that are supposed to keep you safe.

As Chief Data Scientist at PGS Software, Maciej is the technical lead of the data team and implements ML-based solutions for clients around the globe. In his 10 years of IT-experience, he’s worked for major players like Nokia and HPE, developing complex optimisation algorithms even before the term Data Science was coined.

AI-Enabled Data Extraction Using Horizontal AI

Data points from complex contracts including ISDAs, GMRA, etc. can be vital in guiding real-time decisions in an ever changing regulatory environment. Innodata’s Chief Product Officer, Rahul Singhal, will demonstrate how to extract the most critical data points across documents and how it will save time and improve productivity for legal, operations, and front-office.

Rahul Singhal leads product, technology and business development initiatives for Innodata, a data and content transformation company. Prior to joining Innodata, Rahul was Chief Product Officer at Equals 3, an AI marketing platform which won several accolades including Gartner Cool Vendor. Before that, Rahul spent 12 years at IBM, the last three of which he spent leading the product portfolio for the Watson Platform which included a collection of APIs for vision, speech, data and language.

Towards Algorithmic Assurance of Governing Machine Learning Systems at Scale

Over the past years a vast amount of research and guidelines have been published with the aim to pave the way towards 'governance frameworks' of machine learning systems affecting consumers, particularly around adversarial robustness, model transparency, privacy conservation, algorithmic fairness and ethical principles. This presentation focuses on a set of techniques that have shown potential and presumably practical relevance in financial services. Furthermore, the talk attempts to also shed light on opportunities and challenges of embedding third-party APIs that have been developed/trained by global communities.

Manuel is currently Head of Predictive Analytics in Banking Products at UBS. Previously, he's been a senior advisor and machine learning cloud platform lead at Ernst & Young, developed numerous AI-driven business solutions for global organizations, and held managing roles in cross-border audit & advisory engagements and leading international research collaborations with contributions to AI research, Cognitive Control Systems and Particle Physics.