Looking for the best Financial Services events and sessions at Data + AI Summit Europe 2020 (Nov 17-19)? Below are some highlights. You can also find all Financial-related sessions, including customer case studies and extensive how-tos, within the event homepage by selecting “Financial Services” from the “Industry” dropdown menu. You can still register for this free, virtual event here.
For Business Leaders
The Future of Financial Services with Data + AI
In today’s economy, financial services firms are forced to contend with heightened regulatory environments and a variety of market, economic and regulatory uncertainties. Coupled with increasing demand from customers for more personalized experiences and a focus on sustainability/ESG, incumbent Banks, Insurers and Asset Managers are reaching the limits of where their current technology can take them with their Digital Transformation initiatives. It’s more critical than ever for institutions to turn towards big data and AI to meet these demands, and make smarter, faster decisions that reduce risk and protect against fraud. Business and analytics leaders and teams from the Financial Services sector are invited to join this industry briefing to learn new ideas and strategies for driving growth and reducing risk with data analytics and AI.
- Junta Nakai, FSI GTM Lead, Databricks
- Jacques Oelofse, VP Data Engineering and ML, HSBC
- Mark Avallone, VP, Architecture, S&P Global
- Douglas Hamilton, Chief Data Scientist, Nasdaq
(Ernst & Young) Stories from the Financial Service AI Trenches: Lessons learned from building AI models at EY
EY helps clients establish their data- and AI-driven transformation strategies, operationalise their AI governance frameworks, as well as build and monitor AI solutions. In this presentation we discuss how we have approached the nuances of building AI solutions in financial services, and how a highly-regulated industry meets innovation with experiment-driven emerging technologies.
The adoption of AI as a critical component to the future of financial services has been widely recognised. AI does enable the creation of innovative financial products and personalised services. It also derives value from improving processes and services through intelligent automation. AI has made great strides, particularly in machine learning. However, these advanced methods require vast amounts of good quality data for models to learn from. This is a great challenge in the financial sector due to a multitude of factors. We discuss these challenges and how we solved some of them.
The talk covers our experience in building models where data is scarce or highly restricted, our learnings from deploying models in multiple geographies and jurisdictions, and how we monitor models where data can drift because of changes in customer behaviour, degrading data quality, or new legislation. Good quality data is a big problem in many sectors, but it becomes more prevalent in the financial sector due to incomplete data sources, biases and imbalances, among others. With pressure from regulators, privacy concerns and restrictions, this often leads to very small samples of usable data. We tackle the above challenges with various approaches, such as synthetic data generation, data anonymization, missing data prediction, and transfer learning, among others.
We also believe that domain expertise remains an integral part in maintaining a healthy and successful AI ecosystem, we will also discuss how we have embedded automated and human-in-the-loop guardrails to capture domain knowledge and ensure trust in the AI solutions we build for our clients.
(Ceska Sporitelna, DataSentics) Struggles along the way for the holy grail of personalization: Customer 360
Ceska Sporitelna is one of the largest banks in Central Europe and one of its main goals is to improve the customer experience by weaving together the digital and traditional banking approach. This talk will focus on the real world (both technical and enterprise) challenges for implementing the vision, from powerpoint slides into production:
- Implementing Spark and Databricks-centric analytics platform in the Azure cloud combined with a on-prem data lake in the EU-regulated financial environment
- Forming a new team focused on solving use cases on top of C360 in the 10,000+ employee enterprise
- Demonstrating this effort on real use cases such as client risk scoring using both offline and online data
- Spark and its MLlib as an enabler for employing hundreds of millions of client interactions through personalized omni-channel CRM campaigns.
ESG, Followed by AMA
The future of finance goes hand in hand with social responsibility, environmental stewardship and corporate ethics. In order to stay competitive, businesses are increasingly disclosing more information about their environmental, social and governance (ESG) performance.
In this free demo, we’ll demonstrate ways to use machine learning to extract the key ESG initiatives as communicated in yearly PDF reports and compare these with the actual media coverage from news analytics data
Afterwards, FinServe Technical Director Antoine Amend will be available to answer questions about this solution or any other financial services analytics use case questions you may have.
(First Digital Bank) SHAP & Game Theory For Recommendation Systems
We introduce a game-theoretic approach to the study of recommendation systems with strategic content providers. Such systems should be fair and stable. Showing that traditional approaches fail to satisfy these requirements, we propose the Shapley mediator. We show that the Shapley mediator fulfills the fairness and stability requirements, runs in linear time, and is the only economically efficient mechanism satisfying these properties
Looking forward to seeing you at the Data + AI Summit 2020.
Try Databricks for free. Get started today.
The post Financial Services Agenda for Data + AI Summit Europe 2020 appeared first on Databricks.