Whilst banking and insurance are, in many ways, very traditional industries, with recent advances in AI they are now embracing the huge potential that this technology holds and are leveraging AI and machine learning to revolutionise all areas of their processes. Lloyds syndicate has just announced the launch of its AI-enabled product for supply chain finance, the FinTech Times has stated that AI is the future we should all be prepared for, and Zheshang fund is aiming to launch China’s first AI-managed mutual fund. These announcements are only a speck in the ocean of companies applying AI in finance and insurance.
Have you been working in finance or insurance for several years, but aren’t sure how AI will transform your work? Or maybe you’re an AI expert keen to delve into the field. At the RE•WORK AI in Finance Summit and AI in Insurance Summit taking place in New York this September 5 - 6, experts will explore some of the most cutting edge research progressions and business applications in the space with over 60 speakers and 400 attendees over the two days. We’re currently offering a 25% discount on the summits when you register using the code SUMMER25 - one pass provides access to presentations and Deep Dive sessions from both summits.
In advance of the summit, we’re taking a look at some of the most popular job titles attending and highlighting some of the key sessions where you might want to spend your time:
If You’re a Business Leader
You’ve worked in Insurance or Finance for a number of years, but this is your first endeavour into AI. Find out how to develop, adopt and deploy AI, and learn how to solve problems in both back of house enterprise and customer-focused processes. Sessions will share how leaders in the field have overcome legacy systems with AI & moved into the fourth industrial revolution with confidence. Recommended sessions include:
Summit: AI in Insurance Summit
Time: Day 1, 10:05
Speaker: Michael Natusch, Global Head of AI at Prudential - Horses for Courses: Deep Learning Beyond Niche Applications
Machine learning in general and deep learning in particular are driving major advances for a wide range of specific finance use cases. This talk will outline how enterprise-wide learning loops will extend these point success to a coherent AI strategy and also show what other elements are required for success, using real-world examples at Prudential plc.
Summit: AI in Finance Summit
Time: Day 1, 10:00
Speaker: Marsal Gavalda - Head of Machine Learning - Square - Developing ML-driven Customer-Facing Product Features
This talk will explain the methodology followed at Square when developing ML-driven customer-facing product features, which is based on paying close attention to four key and interdependent aspects: Design, Modeling, Engineering, and Analytics. Design is concerned about the usefulness and remarkability of the feature, and thus cares about the overall functionality, ease of use, and aesthetics of the experience. Modeling is concerned about the accuracy of the ML model, and thus cares about the training data, the features and performance of the model, and —crucially for a customer-facing product— how the application behaves in the face of the mistakes the model will inevitably make (false positives, false negatives, lack of predictions above a certain confidence).
Summit: AI in Insurance Summit
Time: Day 2, 11:45
Speaker: Courtney McCormac, Director, Enterprise Data & Integration Analysis, New York Life - AI & Machine Learning Model Production & Operation at Scale
Historically, employment of data science techniques in FinTech has been limited to a small, elite group of actuaries and “quants” who, quite frankly, seemed a mystery to many of us. Today, Artificial Intelligence is all the rage. Recent years have seen a push to educate the workforce on how to incorporate AI when solving business problems. And now, the demand is here!
If you're a Data Engineer
Summit: Deep Dive Track
Time: Day 2, 13:20
Speaker: Jameson Tucker Allen, Data Engineer, Chubb - Near Real-Time Data: The Beauty of Pub/Sub Systems and Getting Started with Kafka
Every machine learning project is different, but all of them rely on the same thing: data. Without it, an AI project is just an idea. The success of some projects might rely on "Near real-time data", a term that has joined the elite ranks of "cloud" and "big data" in recent buzzword canon. But what is it that makes near real-time data pipelines so different from traditional methods? We'll begin with a brief explanation of the Publish/Subscribe (Pub/Sub) method of data transmission and how it enables us to ingest, process, transform and emit thousands of messages, or more, per second. We'll walk through a basic, single node, Kafka implementation together, and I'll share some of the stumbling blocks we experienced at Chubb during our own Kafka rollout.
If you’re a Data Scientist or AI Expert
You’ve got extensive experience in AI and data science, but are relatively new to Finance and Insurance. The summit will allow you to hear diverse developments in the space and will delve into the problems being solved through AI as well as the different approaches in design and deployment. Techniques such as DL, LSTM, Random Forest and ML will be covered as well as applications including CV, Fraud Detection and Pricing Models. Recommended sessions include:
Summit: AI in Insurance Summit
Time: Day 1, 11:50
Speaker: Priya Sundararaman, Principal Data Scientist, State Farm - Safety First: AI To Detect Distracted Driving
Distracted driving is one of the leading causes of auto accidents, according to the National Highway Traffic Safety Administration (NHTSA). This talk will demonstrate the use of Artificial Intelligence to analyze driver images and identify distracted driving behavior autonomously. Two deep learning models were created using videos of drivers from a 3D image sensor and a 2D web camera. An ensemble of the models was used to classify the action of the driver. I will discuss the methodology, results and suggested areas of future work to improve driver safety.
Summit: AI in Finance Summit
Time: Day 1, 11:30
Speaker: Leman Akoglu, Associate Professor, Carnegie Mellon University - Anomaly Mining: Detection, Explanation, Interaction
Anomaly mining is a key unsupervised learning task, with numerous applications in finance, security, surveillance, etc. Despite its importance and extensive work on the topic, anomaly mining remains a challenging subject in part due to the tremendous variety of both the forms that anomalies can take and the settings in which they are to be identified. One of the main thrusts of my research has been in tackling these challenges in anomaly mining by building models that are suitable for different practically-relevant settings. In this talk, I will highlight some vignettes from my recent work on streaming, contextual, and relational anomaly detection with concrete applications to intrusion, ad fraud, tax and credit card fraud detection. I will also discuss how to improve detection quality by bringing human-in-the-loop, with a focus on auditing systems. Finally I will move beyond detection and introduce new approaches for explaining anomalies toward verification and sense-making. Key Takeaways: 1 - Context and relational data matters for various fraud detection settings. 2- We can improve detection quality using interactive/human-in-the-loop techniques. 3- Explaining the anomalies is as important as detection.
Summit: AI in Finance Summit
Time: Day 2, 14:00
Speaker: Ali Raza, Machine Learning Engineer, Bank of America, Using Machine Learning to Detect Fraud
Modern technologies such as EMV (the chip card) have greatly reduced fraudulent card transactions at brick and mortar stores. Unfortunately, online credit card fraud remains prevalent and is projected to cost consumer over $32 billion by 2020. In this session we will discuss a how data science is used at Bank of America to reduce the risk of fraudulent transactions. In particular, we will explore a machine learning model that uses customer behavior history to detect suspicious logins in real time for over 20 million logins a day.
If you’re a Startup, FinTech or InsurTech Business
As one some the oldest and most traditional industries, finance and insurance have proved fairly resistant to change until recent years. A growing numbers of tech startups have emerged and are gaining momentum, as well as funding, and in 2018 InsurTech a attracted $1.7 billion in investment. If you're a startup or a VC looking to invest in the most up and coming companies, we recommend the following sessions:
Summit: Deep Dive Track
Time: Day 2, 10:15
Speakers: Deborah Barta, Mastercard, Noorjit Sidhu, Plug and Play Ventures, Devin Devrai, American Family Ventures
Session: “Investing in FinTech & InsurTech AI Startups”- this session is a VC panel discussion followed by dedicated time to network with leading investors.
Summit: AI in Insurance Summit
Time: Day 1, 13:30
Speaker: Baju Devani, SVP, Chief Data Officer, AVIVA, Key Factors Evolving the Fourth Industrial Revolution in P&C Insurance
Baiju has led data sciences and engineering teams for over a decade to bring algorithmic and data-driven business growth in FinTech and InsureTech space. As SVP and Chief Data Officer at Aviva Canada, Baiju is responsible for embedding the use of data and algorithms driving business growth. He leads a cross-functional team of actuaries, data scientists and data engineers to deliver innovative data-driven solutions including use of applied machine-learning for product pricing and underwriting and other advanced algorithms for decision making.