Agenda

Understanding the intraday microstructure dynamics across different universes of stocks and markets is fundamental in designing any optimal  trading strategy, especially for trading diverse portfolios of stocks. In this talk we discuss how modern machine learning techniques can be used in conjunction with dynamical modeling of the intraday phenomena to identify those trading strategies that move away from the general country/sector classifications but rather respect the stock's particular microstructure characteristics.

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.

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.

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.

Wavelets have long been known for their strong ability in denoising and transforming signals, for example in speech recognition and image processing. More recently, Long Short Term Memory Networks (LSTMs) and Autoencoders started showing promising results in this space as well. Application is possible also to data sets generated by complex nonlinear processes with a low signal to noise ratio. Both Wavelets and LSTMs offer value through generating cleaner training data and ultimately driving deeper insights. In this session, we will show three concrete use cases: Analysis of Financial Time Series, Sales Forecasting and Credit Risk Analysis.

The use of AI in decision making processes brings efficiency and data-driven results, but also risks.  Machine learning creates models which make predictions based upon patterns learned from past data. The reasoning behind these decisions is not available to the users of the models, or recipients dealing with the consequences of the decisions. E.g., a sales person doesn't know why a business is a good lead, and credit doesn't know why credit is denied.  This is a case study on adding explanations to machine learning algorithms, so that users will have greater confidence and insight into machine-driven decisions.

Predictive models are increasingly used for important decisions such as which customers may open a financial account. These decisions affect the opportunities available to customers and drive business results. To maintain the customers' trust, it is important to be able to explain individual predictions. Likewise, it is important to explain the model logic for business managers, compliance professionals and regulators who expect fair decisions.  This is not easy when using advanced techniques such as ensemble models.  Mr. Duke will share Experian's recent advances in explainable AI technologies, with results in credit risk modeling, synthetic identity detection and fraud prevention.

"Build a better mousetrap and the world will beat a path to your door." Build a dozen mousetraps, with different triggers, bait, and alarms all meant for different species of mice, and the world will beat your door down. This presentation addresses the challenges of alert fatigue generated by successful predictive models. When your target audience is presented with multiple models recommending different and occasionally overlapping but important actions, there needs to be harmony in the message sent. This presentation examines the interactions of prioritization, frequency, severity development, consistency, messaging, and user socialization across multiple models for effective action.

While many businesses understand the value of advanced analytics in decision making, operationalizing data science can be challenging. This session reveals how Enova International has been successful at integrating traditional operations with advanced analytics to turn fraud defense into a collaborative analytics function. Over time, through combining the latest technologies in data, machine learning and decision automation with manual investigations, Enova International has been able to attract and retain top analytics talent, mitigate fraud risk, improve profitability and deliver a better customer experience.

Predictive analytics has been a buzzword for a few years now. It has seen success for a wide range of applications. Within the Insurance industry, several applications have emerged, across underwriting, claims, marketing, and beyond. In this session, we will highlight examples of success factors that help sustain predictive analytics in a workers compensation insurance environment.