1999 Systems Engineering Capstone Conference • University of Virginia
PROVIDIAN FINANCIAL CORPORATION:
COLLECTIONS STRATEGY
Student team: R. Danielle Bailey, Ben Butler, Tim Smith, Tom Swift, Jeff Williamson
Faculty Advisor: William T. Scherer
Associate Professor, Department of Systems Engineering
Client Advisors: Lisa Fischer and Paul Hashemi
Providian Financial Corporation
Unbanked Division
San Francisco, CA
E-mail: [email protected]
[email protected]
KEYWORDS: Bayes’ Theorem, breakeven analysis,
classification, credit cards, customer segmentation, debt
collection, discriminant analysis, reject inference,
sensitivity analysis, systems methodology/analysis
ABSTRACT
The goal of this research was to develop a strategy
for collecting on delinquent accounts that maximizes
the return on collections efforts in the secured credit
card market. A three pronged approach was taken in
order to achieve this goal:
 Conceptualization of an optimal collections
strategy using the Systems Methodology,
 Examination of the current collections process for
potential leverage points, and
 Application of discriminant analysis for the
identification of parameters indicating likelihood of
delinquency and customer base segmentation to
create a new collections process.
The analysis resulted in the creation of a model of the
existing collections process that generates statistics for
performance measurement of the current process and a
basis for comparison with future changes. Identification
of customer behavior attributes indicative of
delinquency led to the development of a multi-staged
collections strategy designed to target customers.
Several recommendations are made for improvements
to the existing collections process designed to reduced
the total amount of charge off.
INTRODUCTION
Credit is the lubricant of the economy. In recent
years, credit card usage has boomed as new media
expand the boundaries of business transactions.
Unfortunately with increased credit card use, many
consumers fall into unpayable debt. Many factors,
including the associated risk of owning a small
business, the economy, and poor financial management
skills, lead to customer delinquency. These customers
damage their credit rating and cause losses to credit
issuers by leaving unpaid debt.
Seeking to tap into one of the few growth areas in
the industry, Providian Financial Corporation added to
its portfolio a product for a previously ignored
customer, one with poor or non-existent credit history –
the secured credit card. Establishing the Unbanked
Division signaled Providian’s emergence into this
untapped market. This move has proved worthwhile
because of this market’s need for credit cards and a way
to improve customer credit ratings. Providian has
perfected the front-end targeting of secured card
customers. Currently, Providian maintains $11.5 billion
in total managed loans, ranking it among the country's
ten largest bankcard issuers and the world’s largest
issuer of secured credit cards. While there are an
abundance of potential customers in this poor credit
market, there is also an abundance of high risk. Many
of Providian’s customers in this market have a high risk
of charging off, preventing Providian from retrieving
the credit debt.
Providian Financial custom-built a collections
process to recover money from delinquent accounts for
their high-risk product. This process has been used for
a little over a year and Providian feels that there are
opportunities to save money and increase profits.
Investigation was conducted to identify and exploit
leverage points in the existing collections process.
Secured Credit Cards
The secured credit card market caters to consumers
who would be shut out of the regular credit market –
23
Providian Financial Corporation: Collections Strategy
people with impaired or nonexistent credit histories.
Secured credit cards are designed to help people
establish or reestablish their credit record. The formally
bankrupt, widows, divorcees, immigrants, and college
students make up the majority of secured card
customers. “The best of poor credit quality customers”
characterizes the portion of the secured card market that
Providian targets. Counter to traditional credit card
products, secured cards represent a small, niche market.
Secured cardholders are frequently “sloppy” paying
customers. Many customers have had accounts closed
with other institutions, and/or have been forced into
bankruptcy. Because of a lack of experience with credit,
some have little understanding of a card’s
responsibilities. In exchange for establishing a savings
account that is used as collateral, customers are given a
credit line equal to or as much as twice the value of the
savings account. If the customer defaults on the
payments, the savings account is used to make
minimum payments and reduce credit loss risk.
Customers pay higher annual fees, interest charges and
late fees for the opportunity to establish a good credit
record by demonstrating the ability to pay bills on time.
The cost of collecting on these customers is also
much higher. Because of the nature of their customers,
a secured card issuer must be prepared to spend more
money servicing accounts, educating customers, and
collecting payments in order to be successful in the
market. The secured card is often the cardholder’s only
credit card; consequently, the account is much more
labor intensive. Industry figures show that for every
100 secured card accounts, issuers can expect an
average of 120 phone calls from their customers,
compared to an average of seven calls for unsecured
cards. The calls are also longer, on average 2.5 minutes
as opposed to 90 seconds for regular credit cards.
Servicing a secured card account costs $80 a year,
almost twice the cost of an unsecured account (SwannIngram, 11). Delinquency and over-drafting is much
more common and severe with secured card holders,
who default on their payments two to three times more
than regular customers (Swann-Ingram, 11). The
incidence of charge off among secured card customers
is significantly higher. Charge off is defined as money
that will not be recovered. While the deposit feature
limits the amount of the losses, it does not reduce the
frequency, which results in an annual loss of 5-15% of
the money owed for the industry (Swann-Ingram, 11).
A proactive collections strategy is vital to
maintaining profitability in this demanding market.
SYSTEMS ANALYSIS METHODOLOGY
24
A systems methodology revolving around the
central idea of turning “data into action” provided the
framework for the analysis of Providian’s Collections
Process. Based on our analysis of the process, policy,
and the provided data, we produced action items to help
improve the bottom line. The basis of this research is a
common systems methodology implementing a topdown or goal-centered approach (Gibson, 35). This
methodology generalized the problem statement,
creating descriptive and normative scenarios. After
high-level goals were created, they were broken down
into action tasks and ultimately transformed into
deliverables for client.
Descriptive Scenario
The current collections process is rather unique due
to the specific nature of its clients. Prior to this analysis,
collections process efficiency and effectiveness had not
undergone such scrutiny. Different actions, such as
mailing letters and making calls, are used to prompt
customer to settle delinquent accounts. Execution of
these actions is based upon basic account criteria such
as the amount owed. All customers are treated the
same throughout the process and some of the
differentiating factors may not sufficiently reflect the
proper collection action. No system is in place to
consolidate collections data for easy analysis,
preventing a thorough evaluation of process
performance. Based on the current scenario goals were
defined to remedy visible problems.
Providian lost 6.32% of their average loans
outstanding in 1997 due to delinquent customers
charging off. This accounts for a loss of approximately
$600 million. Providian seeks to find ways to reduce
the amount of charge off and increase the profitability
of the collection process (Providian, 32).
There are many causes for charging off – customer
death, bankruptcy, loss of employment, accidents, etc..
Currently, Providian uses call centers manned by sveral
hundred employees to call delinquent customers daily.
An automated phone dialer that automatically dials the
accounts for the employees is used to contact customers
to prompt payment. The infrastructure needed to carry
out operations is very expensive, including some of the
major costs such as office space, employee salaries,
phone service, and equipment expenses.
Normative Scenario
The utility provided by these analyses will provide
monetary reward. Foremost, Providian will retain more
of their customer base as the number of outsourced
clients decreases. The collections process will be
1999 Systems Engineering Capstone Conference • University of Virginia
altered to preempt common client transgressions,
yielding significantly higher return.
The driving forces behind a superior collections
effort are numerous, with the majority supplied by
subsequent recommendations. Providian will be
equipped with analytic tools that generate meaningful
statistics, and identify typical warning signs. These
tools range from transitional models (the simulation of
the current process), to segmentation models (the
application of discriminant analysis).
The purpose of this research is not to identify the
most refined collections process possible. Such
precision may not be possible when considering human
behavior. The ultimate goal is to advise the company
on ways to optimize the utilization of their collections
infrastructure. These areas do not need a complete
overhaul, but merely restructuring. It is hoped that the
research convinces the client to adopt the analytical
processes already commonplace in the client acquisition
process. A dynamic collections process based on
multiple types of consumer behavior will reap
substantial rewards.
A graphical representation, of the high-level goals, is
presented in Figure 1.The devised goals were used to
help divide project tasks and provide a constant focus
throughout the individual pieces of the analysis.
Axiological Component
Solving a problem with a scope as large as
improving Providian’s collection process requires
tremendous organization and focus. A significant
investment was made in outscoping to assure that all
alternatives were considered and free the project from
the restrictions of conventional wisdom. After
considerable consideration and iteration, “out of the
box” thinking produced the project goals described
above and established project direction. As seen in
Figure 1, the overall goal of increasing collections
profitability consists of three tiers. Project tasks were
divided along these tiers allowing team members to
focus on a specific aspect of the collections process.
Through research and conversations with employees
about the corporate culture, Providian’s collection
practices seem to be a manifestation of the company’s
core values. Incorporating these values into the
proposed collections strategy is key to its acceptance
and in assuring that a positive reflection of the company
is maintained. While Providian is very interested in
increasing profitability of the collections process, they
do not want to compromise their corporate reputation.
As a result, maintaining a public positive image and
therefore, reducing attrition of current profitable
customers and attracting future customers is a stated
goal determining project direction.
Goal Structure
Below are a list of high-level goals and their
components that served as the guide for conducting this
analysis.
Maintain Positive Public Image
 Keep Current Profitable Customers
 Sustain marketing power for Unbanked Cards
Increase Profitability within Collections
 Identify leverage points through simulation
 Improve collections process through customer
segmentation
 Improve the efficiency of the telephone
collections process through prioritization
Improve Providian’s Secured Card
Credit Division
Increase Profitability
within Collection
Improve collection process
through customer
segmentation
Identify leverage points
through simulation of
current process
Maintain Positive Public
Image
Keep Current Profitable
Customers
Sustain Marketing power
for Unbanked Cards
Improve efficiency of telephone
collection process
Figure 1: High-Level Goals Tree
Areas of Concentration
CURRENT COLLECTIONS PROCESS MODEL
A simulation model based on historical account data is
utilized to represent the efficacy of the current
Unbanked collections process. Within this context, the
model serves three high-level objectives: (1) description
of the status quo of Unbanked collections, (2)
interpretation of the predictability of collection events
based on aggregate and individual customer
characteristics, and (3) identification of leverage points
or areas of underperformance within the current
collections process. The simulation model helps to
address the following questions.

How do customers progress through the current
process?
25
Providian Financial Corporation: Collections Strategy





What types of customers enter the collections
process and in which ways do they progress
through the process?
How effective were collection efforts in each area
of the process?
Which types of customers are more likely to be
collected upon?
Which types of customers are likely to
collect the most from?
Which types of customers are outsourced more
frequently?
The methodology specific to this simulation model
consisted of four main components: model design,
experiment design, model building, and statistical
analysis. The model design determined both the
conceptual nature and the detailed structure of the
simulation model, and thus will be emphasized heavily.
A further component entails the experimental and
analytic capability of the model. Model building
encompassed the implementation, verification and
validation of the conceived model. Finally, the model
output required statistical analysis to meet the
objectives and answer the originally posed questions.
The design approach relates specifically to two main
functional purposes of the model: to represent the
progression of individual accounts through the
collections process and associate aggregate statistical
data with each state of the process. A state-based
approach was used to accomplish these objectives.
Accounts are categorized into predetermined states,
based on a set of account parameters. Descriptive
statistics are then attached to these states and tracked
over time.
The model may be classified as dynamic and
deterministic; dynamic due to the incorporation of time
and deterministic for the use of non-randomized data
sources. A deterministic approach was taken for two
reasons. Many interrelated parameters impact the
progression of accounts through the collections process.
Thus, the generation of probabilistic distributions
would be highly complex. Secondly, historical data
provides insight to the relationships between account
parameters and the collection status.
The states were defined according to an account’s
collection status, secured status and a dollar measure of
the bank’s exposure. Collection status determines the
delinquency of an account. An account may be current,
past due for a specified period, or outsourced from the
process. These parameters define the states represented
in Table 1 below. The transitions between states are
displayed in Figure 2 (transitions back to the current
state are omitted). Finally, the model tracks statistics
related to different areas of interest including total
26
dollar values, risk, account utilization, payment
information, costs, and profitability.
Table 1. State Definitions
State
A
B
C
D
E
F
G
H
I
J
K
L
M
N
Secured Status
Secured
Secured
Secured
Secured
Secured
Secured
Unsecured
Unsecured
Unsecured
Unsecured
Unsecured
Unsecured
Exposure
Current
High
High
High
Low
Low
Low
High
High
High
Low
Low
Low
Outsourced
*PD = Past Due
Collection Status
1-30 Days PD
31-60 Days PD
61-90 Days PD
1-30 Days PD
31-60 Days PD
61-90 Days PD
1-30 Days PD
31-60 Days PD
61-90 Days PD
1-30 Days PD
31-60 Days PD
61-90 Days PD
Figure 2: State Transition Diagram
The model was implemented in MS Access through a
complex query structure. The generated statistics were
exported to MS Excel for analysis.
This simulation model is robust, as it allows for a
wide range of experimental analysis by placing
different types of customers through the model. The
modeling approach proved to be a powerful method for
transforming complex data sets into useful descriptive
statistics. In addition, the model provided the basis for
more complex segmentation modeling, as groups of
pertinent accounts were differentiated. The model not
only serves as a tool to represent the current process,
but will also serve as a tool to gauge the efficacy of any
changes or improvements made to the process in the
future.
BREAKEVEN ANALYSIS
Breakeven analysis is a technique used to examine
the relationship between a business’s fixed costs,
variable costs, and revenues at various levels of output
to determine the combination of elements that achieve
1999 Systems Engineering Capstone Conference • University of Virginia
the breakeven point. At the breakeven point, revenue
equals total costs; revenues generated just cover
operating costs and the business is realizing neither
financial gain nor loss. Breakeven Analysis compares a
business venture’s operating revenues to its operating
expenses both directly and indirectly in the form of
fixed and variable costs.
Figure 3: Breakeven Graph
Breakeven analysis determines the efficiency of
current business operations as well as profitability and
risk associated with pursuing new business ventures, all
in terms of the existing cost structure and expected
revenue generation. Subsequently, it outlines the
conditions necessary for a new program to realize a
profit.
Breakeven analysis indicates the following:
 The unit volume level that must be achieved in
order to breakeven
 How much profit will be made at any given level
of unit volume
 How price and revenue generation changes affect
profitability
 How reducing expenses in different areas of the
company’s cost structure will impact profits,
improving financial performance
Identification of the breakeven point determines
what, if any changes to make in the cost structure to
improve financial performance (Newkirk, 2).
Breakeven Model
This method is used to measure the performance of
the current collections process, and establish a control
to measure future processes against. An Excel model
provides both computational and graphical means of
performing the analysis. Providian’s fixed and variable
costs were identified and used as model inputs. The
infrastructure used to contact customers and encourage
them to pay their debts is operationally very expensive.
Receiving payment, as well as assessing fees and
interest on delinquent accounts, generates revenues.
Modeling Results
Providian currently is operating well above its
calculated breakeven point as can be seen in Figure 3,
the graph produced based on the results of the
breakeven analysis. Providian is not only able to cover
its operating expenses; it makes considerable profit.
The shear size difference in the profit and loss regions
illustrates how successful Providian’s operations and
cost structures are currently functioning. While
Providian’s performance is impressive, it is by no
means the limit to what it can achieve. A large number
of calls results in a higher amount of dollars collected,
and Providian operates above its breakeven point and
therefore makes profit. A much lower amount of calls
however can result in Providian collecting a much
fewer dollars. The call volume can be so low that
Providian is not able to collect enough money to remain
profitable. At this level the company operates below
their breakeven point, and therefore suffers losses.
Although Figure 3 leads one to believe that
increasing profit potential is infinite, further analysis
showed that Providian’s collections process is actually
experiencing diminishing marginal returns. The
increase in profit collected is getting smaller and
smaller as the number of calls made increases. This
phenomenon is a result of the trade off between the
increased the amount of delinquent money collected
and the increased amount of money that Providian must
spend to make more calls. The development of an
optimal collections strategy assures that the company
reaches its fullest potential.
CLASSIFICATION STRATEGIES TO INCREASE
DEBT RECOVERY
The credit industry dedicates much of its analytic
skills towards the generation of complex credit-scoring
models. These are powerful tools in identifying and
capturing the most desirable customers. However, once
a client enters the system, many companies cease to use
predictive modeling (“Reevaluating Strategies to
Increase Credit Card Recoveries”, 10). It would be
advantageous to treat delinquent customers in differing
ways, based on their perceived risk. Predictive models
should exist in both the front and back-end processes.
Scoring, or classification systems within collections
do not succumb to the same pitfalls inherent in
marketing. Reject inference complicates the latter, as it
is difficult for the company to monitor the behavior of
discarded clients. Customers within collections can be
27
Providian Financial Corporation: Collections Strategy
fully observed. Predictive models could be updated
based on their entire performance, from data stored
either in-house or integrated from partner collections
agencies.
Justification
The ideal collections process is one that reacts to
different types of customers. Disjoint groups may exist
within the given population, characterized primarily by
their disposition to pay. This inclination, be it some
type of score or probability, can be divided into
separate bins. Each bin would then have a specialized
treatment for the underlying group, optimized for their
particular behavior patterns.
The integral part of the classification process is to
identify feasible groups. These groups may be
numerous, but they are interpretable through monetary
return. Will the classification routine predict whether a
customer is “good”, “bad”, or “intermediate”? Their
traditional definitions do not fit in cases such as
delinquency. Delinquent customers are obviously
risky, but they also yield high returns because of their
accrued late fees.
The only action within collections that can be
absolutely translated into monetary value is the act of
outsourcing. At this point, the customer has moved far
enough along within collections that the company (in
effect) writes off any outstanding debt. The account is
immediately sold off to collections agencies, with an
industry standard return of only $.1 on the dollar
(“Reevaluating”, 10). Outsourced customers are truly
“bad”. If the customer shows a high likelihood of
outsourcing, the company may be able to enact
preemptive measures.
Stage I:
1-30 days
Stage II:
31-60 days
Reaction
X
Good
Delinquent
Unknown
Bad
Reaction
Y
Reaction
Z
Figure 5. Staged Collections Model
The proposed model provides membership functions
for each class. Its implications will generate a new
collections strategy, shown above.
28
Upon delinquency, the model immediately scores the
account. Customers falling into the “good” class will
receive Reaction X. This entails reducing the current
collections effort on the client, or at least keeping it at
the status quo. Reaction Y is employed on “unknown”
customers, those who fall into the union between both
classes (if the classes are not adequately separated).
Reaction Z is used on the “bad” clients. It points to an
elevated collections effort, and serious consideration
given towards immediate outsourcing.
The model itself is divided into stages, or the
increasing length of time spent within collections. It is
doubtful that many clients will need further
consideration past Stage I, as the preemptive decisions
should already have occurred. Stage II is shown to
illustrate the continual reevaluation of all customers
while they remain in collections, with the aid of more
current data.
Modeling Results
Segmenting reduces the original customer base,
identifying a subpopulation of clients who could
potentially outsource. Each observation is flagged into
two classes, those who outsourced within four months
of delinquency, and those who did not. Numerous
attributes describe these levels, ranging from their
monthly payment patterns to their credit bureau history.
Two modeling techniques were considered for
model generation: binary logistic regression and
discriminant analysis. While both provide a
membership function (probability of being within one
of the groups), discriminant analysis consistently
performed better.
The initial set of attributes was reduced through
stepwise discriminant analysis. The most significant
predictors were selected based on their combination’s
contribution to between-class separability (Hand, 173).
The utility of the attribute set increases as predictors are
removed, as phenomena such as overfitting and lack of
generalisation are diminished.
The generated model and its performance is shown
in the following graph. It is essentially a sensitivity
analysis of the model under different market and
statistical conditions.
The classification rates of the model are based
solely on the identification of the outsourced clients.
Classification rates measure the percentage of time the
model correctly classifies a “bad” customer as “bad” or
a “good” customer as “good.” Further, these rates were
manipulated by changing the costs of misclassification
on the group. The response rates are a measure of the
effectiveness of any preemptive measures adopted by
the company. Alternatively, it designates the rate of
1999 Systems Engineering Capstone Conference • University of Virginia
outsourcing that the company could adopt. The vertical
axis corresponds to the possible money saved.
Money Saved at Different Performance Levels
$3,000,000
$2,000,000
If more profitable customers can be identified over
the given static base, then these clients should take high
priority . By changing the order in which accounts are
dialed in the call list, more revenue could ultimately be
collected. Adding components such as risk level or
propensity to pay as prioritization parameters may
assure that the right customers are called first.
“Tiger Team”
$1,500,000
$1,000,000
$500,000
tio
i ca
ss
if
80
4.24
100
Response Rates
60
40
47.38
20
0
$0
nR
ate
88.53
71.32
Cla
Possible Money Saved
$2,500,000
Prioritization Schema
Figure 6: Money Saved Chart
Another alternative to prioritization is hiring a team
of collections agents to solely concentrate their efforts
on collecting from “bad” customers (as derived from
the aforementioned discriminant analysis). This group
could use a more aggressive approach on a specific
subset of customers. If these methods prove useless,
Providian would not have severely damaged its public
image.
The figure shown above describes the potential
money saved per year if one (of many) predictive
models were employed. If the model correctly
classifies outsourced customers only 4% of the time,
and the response from elevated collections is only 10%,
the potential money recovered would be nearly $1
million.
The addition of more behavioral attributes, and
obviously, more data would augment these results. The
purpose of this research is not only to generate working
models, but to investigate their feasibility. It is quite
clear that substantial funds may be recovered with more
aggressive analytic measures. The question remains
whether the assumptions made were valid, and exactly
how ideas such as “elevated collections efforts” may be
implemented.
Current Process Simulation
RECOMMENDED ACTION ITEMS
Gibson, J.E. How to do Systems Analysis Ivy: P.S.
Publishing, 1991.
The following describes some feasible changes to
the existing collections process, and the Unbanked
division as a whole. The recommendations are not
absolute, and they must be iterated after specified trial
periods.
Newkirk, Jeffrey A. “Break into Profit.” Fitness
Management Fitness World Homepage. 19
February 1998. <http://www.fitnessworld.com/
library/finance/break0398.html>
Account Classification
Customer segmentation efficiently allocates
resources by identifying distinct customer types and
tailoring collections actions based on the classification.
Testing the developed classification scheme on more
data is necessary to verify its performance. Comparison
to the existing process’ results will determine if the
classification adds value.
The information used in the construction of the
process simulation and classification model is useful in
gauging the effectiveness of the collections process.
The process simulation created herein is a static model.
Updating the simulation to act as a dynamic model that
could, upon demand, calculate and evaluate current
collection information would give Providian
tremendous power in tweaking their collections process
and performance.
REFERENCES
Hand, D.J. Construction and Assessment of
Classification Rules UK: John Wiley and Sons, 1997.
Providian Financial Corporation. (1998, March) The
Providian Financial 1997 Annual Report.
San Francisco, CA: Providian Financial Corporation.
“Reevaluating Strategies to Increase Credit Card
Recoveries” The American Banker, 5 May 1997: 10.
Swann-Ingram, Alison. “Secured Cards Ideal for
Serving Subprime Market.” The American
Banker 27 Sep. 1996: 11.
29
Providian Financial Corporation: Collections Strategy
BIOGRAPHIES
R. Danielle Bailey is a fourth-year Systems
Engineering major from Ettrick, VA, concentrating in
management information systems. Her principal
contribution to the project was in the areas of breakeven
analysis, prioritization scheme conceptualization, and
industry benchmarking. Ms. Bailey has accepted a
position with Home Depot and will begin in their
Business Leadership Management Rotational Program
in Atlanta, GA, upon graduation.
Tim Smith is a fourth-year Systems Engineering
student from Manassas, Va., concentrating in
Management Information Systems. His primary
contributions to the project concerned model generation
and the assessment of the given classification rules. He
has also worked as a programmer for Fair, Isaac Inc., a
leader in generic credit-scoring models. Mr. Smith has
accepted a position with Deloitte and Touche
Consultants in Washington, D.C.
Tom Swift is a fourth-year Systems Engineering
student from Las Vegas, NV, concentrating in
management information systems. His principle
contribution to the project was database management
and data analysis and modeling. Mr. Swift has accepted
a position with Providian Financial Corporation, and
will begin working in mid-July in San Francisco, CA.
Jeff Williamson is currently a fourth year systems
engineering major and economics minor. He resides in
Oakton, VA although he considers himself a Texas
native. With this project, Jeff has concerned himself
with the understanding and modeling of the current
Providian Unbanked collections process. He has
accepted a permanent position with Providian Financial
Corporation in San Francisco.
30