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

% of Funded Loans w/ this Message Code 28.5%

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 ScoreBest Score

# Leads# Leads

Worst ScoreWorst 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?

FlexibleFastInternal database “connectivity”Multiple underwriting configurationsChampion challenger capabilitiesEasily connects to external data sourcesEasy data import-export capabilitiesScore development toolsFeedback loops

Questions for the panel?

Thank you!