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
Analytics
Technology
People
Partners
Data
Databases
Analytic tools
Measurement systems
Culture/People
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
% of Funded Loans w/ this
Message Code
28.5%
What key metrics are companies focusing
their analytic resources around?
Lead flow
Settlement rates
Redirect rates
Renewal rates
eSignature rates
Charge off rates
Conversion rates
Gross & net revenue
Cost of acquisition
Lifetime customer value
FPD
Servicing cost
What type of decisions are lenders using
analytics to make?
SEO
Renewal loan amount
Marketing
Customer care
Fraud prevention
Call routing
Lead purchasing
Collection
Bid price
Debt sale
First loan amount
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
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
# Leads
Worst Score
Best Score
What types of data are lenders leveraging in
their analytic processes?
Historical results data
ACH data
Device ID data
Banking data
Geo location data
Demographic data
Velocity data
Credit data
Verification 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
Questions for the panel?
Thank you!