Predictive Analytics World for Financial Las Vegas 2018
June 3-7, 2018 – Caesars Palace, Las Vegas
This page shows the agenda for PAW Financial. Click here to view the full 7-track agenda for the five co-located conferences at Mega-PAW (PAW Business, PAW Financial, PAW Healthcare, PAW Manufacturing, and Deep Learning World).
Blue circle sessions are for All Levels
Red triangle sessions are Expert/Practitioner Level
Workshops - Sunday, June 3rd, 2018
Full-day: 8:30am – 4:30pm
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.
Two and a half hour evening workshop:
This 2.5 hour workshop launches your tenure as a user of R, the well-known open-source platform for data analysis. Click workshop title above for the fully detailed description.
Predictive Analytics World for Financial - Las Vegas - Day 1 - Tuesday, June 5th, 2018
Applied deep learning has fast become a standard tool for many industry machine learning applications. New advances in neural network techniques have opened the doors to solving problems at scale that were out of reach until recently. Because of these advances, applications such as in image recognition for self-driving cars, medical image classification, text translation, and fake news detection are both tractable and often the industry standard. In this keynote, Mike Tamir, who heads the data science teams at Uber ATG—the self-driving cars division—reveals how two key application areas of deep learning signal the broad importance of this emerging technology.
Data science, if judged as a separate science, exceeds its sisters in truth, breadth, and utility. DS finds truth better than any other science; the crisis in replicability of results in the sciences today is largely due to bad data analysis, performed by amateurs. As for breadth, a data scientist can contribute mightily to a new field with only minor cooperation from a domain expert, whereas the reverse is not so easy. And for utility, data science can fit empirical behavior to provide a useful model where good theory doesn’t yet exist. That is, it can predict “what” is likely even when “why” is beyond reach.
But only if we do it right! The most vital data scientist skill is recognizing analytic hazards. With that, we become indispensable.
DataRobot will demonstrate how innovations in Machine Learning & Artificial Intelligence can easily be leveraged to address a variety of opportunities and challenges across industries. Today, companies now have the ability to use machine learning & AI as a competitive advantage in their market, reduce operational costs, create new revenue streams, and drive higher customer satisfaction and loyalty. DataRobot's presentation will outline how scaling AI across your enterprise will deliver top & bottom line-benefits...create truly an "AI-Driven Enterprise".
The Financial industry has been one of the most successful to adopt machine learning. FICO's fraud detection systems played a pivotal role in establishing a long lasting victory for supervised neural networks 25 years ago in the credit card fraud detection space. Since then, we have continually improved behavior and unsupervised machine learning techniques, including methods from collaborative filtering and density-based models. Also important is explainable AI, which has been a part of fraud models for the last 20 years. We will discuss new innovations which are having dramatic impact on the financial systems for detecting and preventing fraud, abuse and money laundering.
11:20 - 11:40 am
This case study details how reconstruction costs following major weather events can be rapidly estimated by predicting damage to infrastructure at a granular geographic level. There is an increasing severity of major weather events impacting communities and infrastructure coupled with rising reconstruction costs. In Australia, funding models for disaster recovery relief are changing. To help get communities back on their feet, reconstruction funding is required sooner following such events, meaning public agencies need to find efficiencies in the existing machinery of government processes for disaster recovery. Implications for financial institutions and insurers will also be covered.
11:45 am - 12:05 pm
We substantially increase investment returns in large stocks by comparing the current market context with prior contexts. The approach differs from simple auto-correlation of a few stock time-series. Contexts comprise over 5000 market and economic events. We key on the unusualness of many events within a context, and show a new similarity measure to find linkages among large sets of past events, current events, and large stock investment opportunities. We benchmark the enhanced investment returns against well-known market indices. Large stock cumulative returns in 2016 were 18% compared to OEX benchmark of 9%; Large stock cumulative returns for 10yrs were 205% compared to OEX benchmark of 50%. This session explains our scientific work presented.
Machine Learning is gaining significant steam in many industries – especially financial services, where the adoption rate has been very high. Robotic and Intelligent Process Automation have been used extensively and have reached a maturity in operations and finance areas such as Risk, Compliance, Anti-Money Laundering and Know Your Customer (AML/KYC), Fraud Detection and Prevention, Loan Processing and Approvals and Governance. Financial services organizations currently are using AI surveillance tools to thwart financial crime, while others deployed machine learning for tax planning. Wealth Management leaders can now offer automated investing advice across multiple channels, and some insurers now use automated underwriting tools in their decision-making process. In this keynote presentation, State Street Vice President Radha Kuchibhotla will survey this wide range of industry movement and highlight the most important facets.
The word is all around that cognitive computing is poised to disrupt industries. You might wonder if it is real or just a meme. And if so, how does impact me or my business? The phrase ‘cognitive computing’ conjures up varying thoughts in our minds which could be exciting and scary at the same time. It’s about time that we delve deeper and develop a clear understanding of what “cognitive” buzzword is all about? How does cognitive decision-making differ from Analytics, AI, and Natural Language Processing? Financial services stand to gain most by cognitive decision-making as the greater understanding of customers, products, and the operating environment directly translates into business value. This presentation highlights the essentials of a cognitive decision-making system, such as DIWO, and will share thoughts on how such an approach can reshape the financial services industry.
2:40 - 3:00 pm
Breaking technical requirements into small enough increments that allow data scientists to deliver something of value to stakeholders every few weeks is a difficult challenge. In this session, we cover how Northwestern Mutual used an Agile Analytics approach to develop a system of predictive models that helps automate risk assessments in life insurance underwriting. We briefly introduce aspects of Scrum and Kanban along with an overview of the Standard Methodology for Analytical Models. The session concludes with an illustration on how to assess the incremental information gain from improved models within a framework of data monetization.
3:05 – 3:25 pm
This session will provide an overview of how predictive analytics helps insurers understand the financial drivers of their performance in the Affordable Care Act (ACA, aka Obamacare). Attendees will learn about:
- The development and implementation of a model that simulated patients' Health and Human Services (HHS) 2014-2016 risk scores
- Large-scale collection of detailed EDGE data to uncover drivers of performance in the ACA markets
- Key insight from the model, including that sicker patients are not driving losses for health plans due to risk adjustment costs
- Takeaways for payers and providers seeking to manage risk and profitability in an unfamiliar environment
3:55 - 4:15 pm
One of the key needs of reinsurance providers who provide insurance to commercial auto businesses is on how reinsurance premia can be aligned with the risk profile of the loan portfolios. This session will focus on how Maiden Re developed an integrated insights portal that can profile customer risk by analyzing companies they underwrite, monitor loss ratios, and identify the best prospects to pursue, using predictive analytics techniques.
4:20 – 4:40 pm
Big Data, increased automation of tools, and artificial intelligence all represent significant changes to the working environment of the data scientist. Nowhere is this more pertinent than in financial services and insurance, which have been the traditional bedrock of predictive analytics and data science. Numerous business examples from this sector will be explored, both within the area of consumer response as well as consumer risk. The discussion will focus on what has remained the same versus what has changed within the four stages of data science. Given the highly disruptive nature engulfing both financial services and insurance, what does this mean to the data scientist such that the role becomes an even more critical one during these turbulent times?
In this digital age of banking, a large part of financial institutions' time and resources is spent in fraud analysis and credit monitoring, yet knowing and engaging the customer based on their preference and ways of interacting is the new normal. It's all about knowing how to help the customer improve their standing, and even predicting when they may have challenges or knowing which product they could benefit from in the future. Predictive analytics are being more relied upon to help agents and customer service representatives service and engage the customer more effectively. Successful machine learning-driven prediction and recommendations, coupled with ML-driven bots can inherently differentiate your products and services to your customer and create a lasting relationship. This session will discuss the pieces and parts behind using ML and bots to change the customer engagement model of your financial institution.
Predictive Analytics World for Financial - Las Vegas - Day 2 - Wednesday, June 6th, 2018
In this keynote, Northern Trust Senior Vice President Andy Curtis will provide an overview on robotics, machine learning, and cognitive computing in the context of operation and process optimization. He will further discuss the benefits and challenges around implementing and maintaining machine learning-based systems.
Many Paychex clients are new businesses, with little or no established credit record. The Paychex payroll business is essentially extending short credit to our clients from the time they submit a payroll, until the time the funds complete transfer. This is a case study on dealing with the issues around Credit Risk Modeling for these new businesses.
11:20 - 11:40 am
Injured workers being away from work constitutes one of the greatest claim cost drivers. In this case study, a Return-to-Work model is introduced to predict the likelihood that injured workers will stay away from work for longer durations. This model serves to target early interventions, such as proper treatments and treatments that assist in returning to work. Such measures reduce claim costs. We explore millions of claims and hundreds of millions of payment records across 2000+ of our clients, including myriad variables. Cutting-edge statistical and machine learning algorithms (GLM, Decision Tree, Random Forest, Gradient Boosting, Neural Network, etc.) are utilized, in combination with business knowledge.
11:45 am – 12:05 pm
Regulatory changes are inevitable and often costly. The ability to adapt quickly to regulatory changes is crucial in today's competitive marketplace. In this session, we will show how using digital decisioning enabled our flagship brand, CashNetUSA, to not only quickly bring its ACH return rate in compliance with NACHA Rules Amendment of 15% but also improve its operational efficiency and customer experience. As a result, our projected loss of $18MM annually was reduced to $8.5MM.
Machine learning (aka predictive analytics) only delivers value when acted upon – that is, when deployed. Only a carefully designed management process ensures that the analytics' output is pragmatically viable for operationalization, and that company operations – your internal consumers of analytics – know best how to employ the product they're consuming.
Lead by moderator James Taylor, an industry leader in the operationalization of predictive models, this expert panel explores and expands upon PAW Business' Track 1 topic, operationalization, to provide insights as to how best to execute on the functional deployment of machine learning.
An explosion in data availability from integrated data supply chains cutting across traditional boundaries, cheap cost of data storage and massive increase in computing power are expanding the universe of business problems that could be addressed by using predictive analytics in general and machine learning in particular. While the use of machine learning to understand complex patterns and identify non-linear relationships offers a great opportunity to enhance the accuracy of predictive models, risk management as domain has relatively seen lower adoption of machine learning algorithms.
In this presentation, Vivek will discuss the key drivers for using machine learning and present a case study where machine learning algorithms were successfully used to enhance the accuracy of risk models for a large global organization as it transformed its data supply chain and downstream & upstream technology infrastructure to digest large amount of data and operationalise the analytical outcomes in more agile manner.
The use of Predictive Analytics and Machine Learning is imperative across all channels and departments in Financial Services. Angoss differentiates itself by its easy-to-use visual data science platform with intuitive workflows and best-in-class Decision and Strategy Trees. We will discuss how best to utilize the Angoss Software Suite as the ultimate complete solution.
In order to understand customer behaviors and provide better services, wealth management firms are investing greatly in data analytics. During this session, Meina Zhou will cover several examples of financial institutions that gained value with in-house predictive analytics solutions to improve wealth management services. She will also address multiple use cases built for different stages of the customer journey, including customer acquisition, customer personalization, and customer retention. She will discuss both the core analytics components and the challenges involved throughout the implementation process.
The sooner that incoming insurance claims can be identified as high risk of becoming a long latency claim, the sooner they can be prioritized and targeted with specialized resources and services for better outcomes. And improving worker outcomes for our most challenging claims is where we can have the biggest impact on the overall success of the insurance system. The Workplace Safety and Insurance Board (Canada) has used only data available at the time of claim registration to segment insurance claims by risk, as they come in. Our custom model combines random forest and conventional logistic regression techniques and is continually refined over time through machine learning algorithms.
We developed a Payment Defect Model for the John Hancock Long Term Care Insurance – Claims department. Long term care is different from most other insurance products, as it has much longer relationship with the clients, and the payout is recurring for several years. Since the reimbursement amount varies from payment to payment, detecting when over- or under-payments occur is especially challenging. Prior to this project, the Claims Audit department reached high detection levels only by monitoring nearly 50% of all payments. In comparison to a rule-based decision model, our machine learning model increased the detection rate, yet decreased manual effort by more than 60%.
In this session, we describe this project's full journey, from the problem statement to the data gathering, from the initial model and testing to a complete shut down and restart of the entire project. We break decades-old perceptions and solidified the necessity of involving subject matter experts in the model-building process.
Workshops - Thursday, June 7th, 2018
Full-day: 8:30am – 4:30pm
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.