Predictive Analytics World for Financial 2020
May 31-June 4, 2020
Click here to view the full 7-track agenda for the five co-located conferences at Machine Learning Week (PAW Business, PAW Financial, PAW Healthcare, PAW Industry 4.0, and Deep Learning World).
Blue circle sessions are for All Levels
Red triangle sessions are Expert/Practitioner Level
Pre-Conference Workshops - Sunday, May 31st, 2020
Full-day: 8:00am – 3:00pm
This one day workshop reviews major big data success stories that have transformed businesses and created new markets. Click workshop title above for the fully detailed description.
Full-day: 7:30am – 3:30pm
Gain experience driving R for predictive modeling across real examples and data sets. Survey the pertinent modeling packages. Click workshop title above for the fully detailed description.
Pre-Conference Workshops - Monday, June 1st, 2020
Full-day: 7:15am – 2:30pm
This one-day session surveys standard and advanced methods for predictive modeling (aka machine learning). Click workshop title above for the fully detailed description.
Full-day: 8:00am –3:00pm
Python leads as a top machine learning solution – thanks largely to its extensive battery of powerful open source machine learning libraries. It’s also one of the most important, powerful programming languages in general. Click workshop title above for the fully detailed description.
Full-day: 8:00am –3:00pm
Machine learning improves operations only when its predictive models are deployed, integrated and acted upon – that is, only when you operationalize it. Click workshop title above for the fully detailed description.
Predictive Analytics World for Financial - Las Vegas - Day 1 - Tuesday, June 2nd, 2020
A veteran applying deep learning at the likes of Apple, Bosch, GE, Microsoft, Samsung, and Stanford, Mohammad Shokoohi-Yekta kicks off Machine Learning Week 2020 by addressing these Big Questions about deep learning and where it's headed:
- Late-breaking developments applying deep learning in retail, financial services, healthcare, IoT, and autonomous and semi-autonomous vehicles
- Why time series data is The New Big Data and how deep learning leverages this booming, fundamental source of data-
- What's coming next and whether deep learning is destined to replace traditional machine learning methods and render them outdated
As principles purporting to guide the ethical development of Artificial Intelligence proliferate, there are questions on what they actually mean in practice. How are they interpreted? How are they applied? How can engineers and product managers be expected to grapple with questions that have puzzled philosophers since the dawn of civilization, like how to create more equitable and fair outcomes for everyone, and how to understand the impact on society of tools and technologies that haven't even been created yet. To help us understand how Google is wrestling with these questions and more, Jen Gennai, Head of Responsible Innovation at Google, will run through past, present and future learnings and challenges related to the creation and adoption of Google's AI Principles.
As the economy continues its uncertain path, businesses have to expand reliance on data to make sound decisions that directly impact the business - from managing cash flow to planning product promotion strategies, the use of data is at the heart of mitigating the risks of a recession as well as planning for a recovery. Predictive Analytics, powered by Artificial Intelligence (AI) & Machine Learning (ML), has always been at the forefront of using data for planning. Still, most companies struggle with the techniques, tools, and with lack of resources needed to develop and deploy predictive analytics in meaningful ways. Join dotData CEO, Ryohei Fujimaki to learn how automation can help Business Intelligence teams develop and add AI and ML-powered technologies to their BI stack through AutoML 2.0, and how organizations of all sizes can solve the predictive analytics challenge in just days without adding additional resources or expertise
9:30 am - 9:50 am
For years, the insurance industry has been using predicative analytics to identify individual claims that have a high potential for severity so adjusters can manage those claims more strategically. If we embrace a new and innovative severity-based analytic model to assess and assign all claims at the outset, we can more effectively fuse predictive modeling and expertise. This will require us to change our approach to adjuster caseloads. The result, however, will be a more efficient allocation of claims resources and the ability to reduce the total cost of risk for clients across an entire book of claims.
9:55 am - 10:15 am
Understanding risk is a core necessity for any insurer and reinsurer. The dynamically shifting risk landscape with new risks emerging and new regulatory requirements coming into place demands flexible solutions to efficiently steer business, price risks and allocate capital. Swiss Re has developed over the last years a broad range of predictive algorithms to understand risks. These new models have generated on the one hands improvements with respect to traditional models as well as they allow to insure risks that previously were thought to be uninsurable. In this session we will deep-dive into two concrete risk prediction models used to better identify risks in motor insurance as well as in travel insurance.
10:25 am - 10:45 am
The Safety National Casualty Corporation is the leader in Excess Workers' Compensation. Predictive Analytics is used internally to identify those claims that it may not see for 7 to 10 years from today. But, given the low volume of these occurrences, the individualistic nature of each and the potential financial ramifications, finding the right balance in identification and action is not just a numbers game.
10:50 am - 11:10 am
Streaming Analytics (or Fast Data processing) is becoming an increasingly popular subject in financial organizations. The reason for this is that customers want to have notifications and advise based on their online behavior and other users' actions. Moreover, fraud detection requires real-time decision making based on transactions data. All these use cases can be covered by a proper architecture for fast data. In this session, I'll discuss the use cases, architecture, and a sample application.
This keynote address will focus on real life “Needle in a haystack” problems and why these problems are becoming much more frequent. Two case studies: defect detection as well as fraud detection models from start to finish and their chances of implementation. We’ll discuss what works and what may be holding you back from a successful implementation. The need for several solution strategies and packaging them in one delivery will also be explored.
Ari Kaplan will talk about his real-life Moneyball experiences of disruption in Major League Baseball front offices - and how artificial intelligence will disrupt every business industry. Having helped lead the adoption of data science throughout baseball, including creating the Chicago Cubs analytics department, he will lead lively discussion on how winning in baseball translates to winning across other industries, overcoming cultural resistance, and doing analytics at scale and velocity to win the race.
12:45 pm - 1:05 pm
In most organizations, the data science teams and the engineering teams build software separately. This can lead to the development of AI solutions that may not fit into the value delivery pipelines built by the engineering teams. Moreover, the feedback loop to continue learning and improving an ML model becomes challenging with this separation of teams. In this talk, you will learn about an approach of building ML and AI solutions that are developed into micro-services, along with micro front-ends, and deployed into dockerized containers for delivery of quick business value to users.
1:10 pm - 1:30 pm
For financial professionals and their attorneys, reviewing the details of transactions like mergers and acquisitions is important, but time consuming. Such transactions have specific sub-events, such as potential terminations that are associated with punitive charges. To enable supervised machine learning models to extract such granular information, human taggers must annotate sections of text associated with each sub-event. This talk will cover some of the challenges in creating this type of training data, including how to train on very small data sets and optimize the tag quality.
Quantitative finance is a rich field in finance where advanced mathematical and statistical techniques are employed by both sell-side and buy-side institutions. Techniques like time series analysis, stochastic calculus, multivariate statistics, and numerical optimization are often used by "quants" for modeling asset prices, portfolio construction and optimization, and building automated trading strategies.
Chakri Cherukuri demonstrates how to apply machine learning techniques in quantitative finance, covering use cases involving both structured and alternative datasets. The focus of the talk will be on promoting reproducible research (through Jupyter notebooks and interactive plots) and interpretable models.
The fields of machine learning and artificial intelligence are constantly looking for inspiration to better represent and model the complex relationships of the world. Inspiration can come in many forms, from the human brain inspiring neural networks, to evolution inspiring genetic search algorithms, to groups of animals inspiring swarm intelligence. Swarm intelligence is based on the natural phenomenon of groups of lower intelligence animals working together to accomplish extremely complex tasks. This can be seen in bird flocking and microbial intelligence, but most commonly in ant colonies. Ants are able to communicate complex directions and find optimal travel paths with very little individual intelligence. This concept of many individuals, who by themselves could not solve the problem, working together to find optimal solutions has been adapted into machine learning (ML) techniques. This talk will discuss the usage of a swarm intelligence algorithm, Ant colony optimization, to find optimal contact strategies for introducing new financial products or inquiring about debt collection with customers.
Predictive Analytics World for Financial - Las Vegas - Day 2 - Wednesday, June 3rd, 2020
With data scientists coming with all kinds of backgrounds and experience, how do you know which type you really need to meet your business needs? How many types of data scientist are out there? And where can you find them? What kinds of analytics can they provide for the financial services industry? Drawing from over 25 years of experience in the field with over two decades of management, Victor Lo will introduce a framework for the classification of data scientists and propose a mapping scheme between these talents and various types of projects.
One of the main components of optimally scheduling portfolio trading is the estimation of the intraday and multi-day propagation of risk and market impact. In this talk, we focus on how machine learning techniques like LSTMs, CNNs, random forests and adaptive splines can improve these estimations for better intraday and multi-day trade scheduling. We also provide examples of how these can be used both in pre-trade scheduling, as well as in real-time optimisation.
10:20 am - 10:40 am
We continue to hear about machine learning and AI and its disruption within our economy. Data science is no exception as various opinions would suggest the demise of the data science role within a more machine-driven world. In this session, we explore how the need for data science and human intervention has historically always been critical for success in financial services. Yet in a more automated and algorithmic business environment, the need for this human aspect of data science is even more paramount. Case studies and examples will highlight how the human and the machine work together in optimizing the prediction of customer behaviour within financial services.
10:45 am - 11:05 am
The US has one of the highest penetration of card payments. As the usage of card transactions increases, so does our ability to extract macroeconomic as well as consumer insights. A single card transaction, be it a face to face or an online transaction, comes with tremendous amount of metadata around location, time, device and merchants. Merchant data in particular gives direct insight into the changing patterns in consumer behavior and economic head/tail winds.
This talk will dissect how the predictive power of transaction data can help with use cases in marketing relevancy, reducing fraud, and business planning for financial institutions, merchants and even supporting growth of smart cities.
Our clients' interactions generate, on average, nearly ~3 billion rows of data every month. In order to measure and analyze client behavior related to completing servicing needs, we need to construct a client journey across channels for the transaction. Client journeys map the universe of interactions from all channels to specific servicing needs For some channels, this is relatively simple, but online behavior is high volume, messy and ever-changing.
Leaning on the related concepts underlying pointwise mutual information and TF-IDF, we developed a simple machine learning approach to differentiate between online "noise" and online behavior related to a client's intent to complete a transaction. We will discuss how the approach works, and how it allows us to create consistent measurement of the online experience that is automatic, scalable, and detects intended or unintended shifts in the online experience.
No one wants to go to jail. Making decisions based on machine learning models offers new and exciting ways to be convicted -- in court and in the court of public opinion. Models that encode bias and automate discrimination are illegal or at least unethical. Demonstrating the compliance of a model is a hurdle that regulated industries must cross for every model. Even models that seem fine to you and your regulator might sound awful when described in the media. Ethics and compliance are as important to your success as data quality, MLOps, and operationalization. This panel will discuss ethics, bias, regulatory compliance, explainability, and much more to help keep you and your models out of jail.
The latest formula for making sound investment decisions involves mining new alternative data sources, using predictive analytics, swarm intelligence and high-performance computing. It offers a more robust framework to generate data-driven investment theses. Data – from satellite images of areas of interest, automated drones, people-counting sensors, container ships’ positions, credit card transactional data, email receipt data, jobs and layoffs reports, cell phone location data, social media, news articles, tweets, online search queries – is now the most valuable commodity for Wall Street. Applying predictive analytics to these alternative data sources can help discover and contextualize actionable insights that can produce better predictions. Knowing something that only few others know affords firms a competitive edge and positions them better to forge new strategies.
In this talk, Prof. Anasse Bari of New York University, advisor to leading Wall Street firms and co-author of “Predictive Analytics for Dummies,” explains how alternative data sources and predictive analytics are driving value in the world of finance and how swarm intelligence algorithms are reinventing Wall Street. He will explain emerging algorithms, filter fact from fiction, outline successful use cases that he and his team have recently led (e.g. how social performance and consumer reviews could be used as predictive features, how to derive actionable insights from geospatial images.) Prof. Bari will also present an overview that can help you design and implement viable solutions to generate a “predictive analytics-based investment thesis.”
There are many non-profit organizations trying to help develop and build economic opportunities for developing countries around the world. Vision Fund International uses microfinance loans to jump start business opportunities in these countries. However, these developing countries don't have the credit institutions that countries like the United States have. That leaves these institutions and banks hampered on their ability to make loan decisions. This case study will go through the work provided to Vision Fund to help them develop a credit scorecard model to better predict default of customers to help them make better data driven decisions.
Post-Conference Workshops - Thursday, June 4th, 2020
Full-day: 7:15am – 2:30pm
This one-day session reveals the subtle mistakes analytics practitioners often make when facing a new challenge (the “deadly dozen”), and clearly explains the advanced methods seasoned experts use to avoid those pitfalls and build accurate and reliable models. Click workshop title above for the fully detailed description.
Full-day: 8:00am –3:00pm
Gain the power to extract signals from big data on your own, without relying on data engineers and Hadoop specialists. Click workshop title above for the fully detailed description.
Full-day: 8:00am –3:00pm
This workshop dives into the key ensemble approaches, including Bagging, Random Forests, and Stochastic Gradient Boosting. Click workshop title above for the fully detailed description.
Full-day: 8:00am –3:00pm
During this workshop, you will gain hands-on experience deploying deep learning on Google’s TPUs (Tensor Processing Units) at this one-day workshop, scheduled the day immediately after the Deep Learning World and Predictive Analytics World two-day conferences. Click workshop title above for the fully detailed description.