Presentation on theme: "Portfolio Analytics: What can the data tell us and how can we use it?"— Presentation transcript:
Portfolio Analytics: What can the data tell us and how can we use it?
Portfolio Analytics Panelists David Johnson - Managing Partner - Cane Bay Partners Chris Corcoran - VP Risk Management - MacFarlane Group Rich Alterman - SVP Business Development - GDS Link Greg Rable - Founder/CEO - FactorTrust Mark Doman - EVP Business Development - eBureau
What does the data tell us about... Successful analytic foundations Key metrics and real life analytics application Successful implementation processes
What does it take to be good at analytics and portfolio management? Culture Investment Technology People Partners Data Databases Analytic tools Measurement systems Culture/People Analytics
What kinds of people and skill sets are important? Visionary & progressive leadership Business, operations & technical competency Data analysis Database expertise Technology savvy people Internal or external statistical/statistician resources Legal & compliance
What do we mean by analytics? Does that mean using rules or scores or both or what? Scores and rules are both important and come out of analytics Rules based on known historical data can be implemented with less expense Well developed scores are more statistically sound and can incorporate many variables Ease of implementation is an important consideration The solution(s) that drive the best results should drive the decision
What key metrics are companies focusing their analytic resources around? Lead flow Redirect rates eSignature rates Conversion rates Cost of acquisition FPD Settlement rates Renewal rates Charge off rates Gross & net revenue Lifetime customer value Servicing cost
What type of decisions are lenders using analytics to make? SEO Marketing Fraud prevention Lead purchasing Bid price First loan amount Renewal loan amount Customer care Call routing Collection Debt sale
What are some key components to a good analytics process? Really good planning Knowing what you want to achieve Measurement on how you are doing Listening to the data - it may not be what you think A-B testing Vintage reporting - changes and results can be connected Continuous improvement A commitment to lose some money for the sake of learning
Best Score # Leads Worst Score There is a large group of leads that score well but fail the current decision process. Look for way to relax or eliminate certain rules and profitably grow the portfolio
What types of data are lenders leveraging in their analytic processes? Historical results data Device ID data Geo location data Velocity data Verification data ACH data Banking data Demographic data Credit data Social media data Stability data
What are the important factors to consider if you are planning on developing a score? A good definition of what the score should predict Development data that is accurate - garbage in = garbage out Internal and external data appended from time of the application Analytic tools and resources to develop the score Blind records or a hold out sample for validating the score A prudent implementation process Ongoing monitoring of score results
What are some important features in the technology stack for supporting analytics? Flexible Fast Internal database connectivity Multiple underwriting configurations Champion challenger capabilities Easily connects to external data sources Easy data import-export capabilities Score development tools Feedback loops
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