Hear from Leading Experts
Join the thought-leaders, influencers, and your community-of-interest at Predictive Analytics World for Financial in Las Vegas, June 16-20, 2019 -- for the leading event covering the deployment of machine learning and predictive analytics for financial services. Hear from leading experts and thinkers how banks, insurance companies, credit card companies, investment firms, and other financial institutions—both Fortune 500 analytics competitors and other top practitioners—deploy machine learning and predictive modeling.
World-Class Speaker Line-Up
Check out these examples of PAW Financial's diverse array of world-class speakers:
 Richard Lee Sr Data Scientist Manulife |
Starting a Predictive Analytics Team for Financial Services Many companies regardless of their size and years in business may not actually have an analytics team or may have a team of one. During my last speaking engagement at PAW I spoke with a great deal of folks who were interested in creating analytics units but didn't really know how to go about it or were under assumption that it would be very cost prohibitive. This is a case study of a team that consists of former spreadsheet guy, grad with a fresh masters in engineering, a former rocket scientist and two former workflow coordinators. |
 Chenyu (Jim) Gao Advisory Manager, Machine Learning/Artificial Intelligence Accelerator Pricewaterhouse-Coopers |
Risk-based Credit Acquisition Modeling - An Innovative Approach to LTJ (Customer Life-Time Journey) This project sets forth the work to be performed for the Customer Acquisition Model, aiming to score the entire through-the-door (TTD) populations and eventually make optimal lending decisions. The work will be divided into two phases: Phase 1 will concentrate on building a Minimum Viable Product (MVP). This phase will leverage existing techniques used currently, where applicable, including the target, data sources and transformations. Phase 2 will conduct another round of data exploration with the purpose of identifying additional transformations to increase model lift over what was already achieved in Phase 1. |
 Mei Najim CSPA, Founder and Lead Data Scientist Advanced Analytics Consulting Services |
Statistics Methods and Machine Learning Algorithms Comparison for Financial Services Applications In this session, I will provide an overview of logistic regression, GLM logistic regression, decision tree, random forest, gradient boosting, neural networks, etc. Then, a comparison will be made through a case study about building a full life cycle of predictive model based on insurance datasets. Since the business goal through the feature engineering stages are similar given the same case study, the comparison of each method, its advantages and disadvantages, will include the feature selection, model building, model validation, and model testing stages. Also, model implementation and interpretability will be discussed and compared. Finally, we will discuss implementation of these methods in Python and R. |
A Breadth of Applications
PAW Financial 2019 delivers case studies and expertise across a range of predictive applications, including:
- credit scoring
- risk management
- predictive actuarial methods
- insurance pricing and selection
- fraud detection
PAW Financial is part of Mega-PAW – with five (5) parallel events amounting to seven (7) tracks: PAW Business, PAW Financial, PAW Healthcare, PAW Industry 4.0, and Deep Learning World.
Platinum Sponsors
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