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Capacity Predictor: A Machine Learning Approach to Ratings

Track: Data Science

Session Number: 3004
Date: Thu, Aug 27th, 2020
Time: 1:45 PM - 2:30 PM

Description:

Traditionally, capacity formulas have trouble conveying uncertainty in the estimate as well as incorporating conflicting wealth indicators into a coherent ratings framework. To resolve both of these issues, we use publicly available survey data from the Federal Reserve to create a machine learning model of capacity as a function of identified assets. This framework can be utilized by prospect researchers in the form of an app, or it can be used to screen your database. The presentation will cover the traditional way of rating prospects; thinking about ratings as probabilities; an exploratory data analysis of the Federal Reserve's survey data; what went into creating our machine learning rating system; and a demonstration of the rating app.
Session Type: Breakout Session (45 Minutes)

Learning Objective #1: Attendees will learn about how asset type proportions can vary among individuals of the same net worth.
Learning Objective #2: Attendees will learn how to design and implement an app to calculate ratings through machine learning.
Software or Vendor Tools: Yes
Software or Vendor Tool Details: R
Primary Competency: PR:Competency 8: Financial Capacity Evaluation and Wealth Indicators
Secondary Competency: PR:Competency 6: Research Methodology and Analysis Presentation
Tertiary Competency: DS:Competency 4: Statistical Techniques and Competencies
Intended Audience Level: Level II
Recommended Prerequisites: Some prior knowledge of machine learning is helpful to understand the framework for modeling ratings, but not necessary. Prior knowledge of R and Shiny is necessary to recreating the capacity rating app.
Shop Size: All Shop Sizes
Session Type: Breakout Session (45 Minutes)

Learning Objective #1: Attendees will learn about how asset type proportions can vary among individuals of the same net worth.
Learning Objective #2: Attendees will learn how to design and implement an app to calculate ratings through machine learning.
Software or Vendor Tools: Yes
Software or Vendor Tool Details: R
Primary Competency: PR:Competency 8: Financial Capacity Evaluation and Wealth Indicators
Secondary Competency: PR:Competency 6: Research Methodology and Analysis Presentation
Tertiary Competency: DS:Competency 4: Statistical Techniques and Competencies
Intended Audience Level: Level II
Recommended Prerequisites: Some prior knowledge of machine learning is helpful to understand the framework for modeling ratings, but not necessary. Prior knowledge of R and Shiny is necessary to recreating the capacity rating app.
Shop Size: All Shop Sizes