Scorecards in KnowledgeSTUDIO - Angoss Software Corporation

Scorecards in KnowledgeSTUDIO
Creating scorecards using advanced
scoring models and predictive analytics
Many financial services organizations make use
of scorecards in order to mitigate risk and
minimize loss. Lenders and creditors need to
analyze the characteristics of new applicants
and existing customers in order to determine
whether individual customers are likely to
provide a profitable return on the services they
receive. Collections look at the same
information in order to determine the likelihood
of customer default. This increases the
efficiency of collections efforts and maximizes
the payments they receive from debtors.
Credit scorecards help to significantly improve
customer acquisition, reduce business loss and
comply with regulations. Angoss enables these
benefits with scorecarding capabilities in
KnowledgeSTUDIO®. Behavioral and
application scorecard creation—complete with
reject inference—is delivered through an easyto-use
interface using Angoss’ best-in-class
Decision Trees and Strategy Trees.
institution to institution, but is usually focused
on the customer’s likelihood to default on loans,
or miss a payment within a defined period of
time. The scorecard will score their personal
information based on a series of predefined
standards. Individuals are identified as “bad”
customers if their score fails to meet a certain
threshold.
KnowledgeSTUDIO puts the power of
scorecard creation in the hands of credit risk
managers and credit analysts to create
predictive scorecards that span the entire risk
management lifecycle—from acquisition
through to collections. The scorecard tool in
KnowledgeSTUDIO provides the user with
intuitive and easy to follow wizards that guide
them through every step of scorecard
development and deployment.
Application Scorecards
Application scorecards are typically used to
predict the probability that prospective
customers will fall into a certain behavior profile.
This prediction is often based on information
that an applicant provides to an institution as
part of the qualification process for a loan,
mortgage or credit card. For instance,
demographic information, existing credit score
information from reference agencies, or past
behavior on other products that the customer
may have purchased from the institution.
The application scorecard is designed to screen
candidates who are likely to display “bad”
behavior at some point in the future. This “bad”
behavior can be defined differently from
The scorecard tool employs industry standard
scorecard workflow approaches to develop and
deploy credit risk scorecards based on the
following processes:
1. Data exploration and segmentation.
2. Correlation matrix to eliminate variables that
should not be included.
3. Data partitioning into training and validation
datasets.

4. Generation of the Weight Of Evidence
(WOE) transformation using the visual
Decision Tree editor to allow optimal binning
of characteristics in order to maximize the
distinction between good and bad accounts.
5. Multivariate analysis and modeling—build
stepwise logistic regression models.
6. Model validation—visually evaluate and
compare model performance and choose
the best model for scorecard development.
7. Model scaling—scale model probabilities
into the desired scorecard, and determine
baseline and the scorecard factor. Includes
options such as: Points to Double the Odds,
Base points, Reverse Scaling and Odds at
Base Points.
8. Final scorecards are generated in standard
tabular form that can be copied and pasted
into Microsoft Office applications, or
exported in SAS, SQL, PMML and XML
code.
9. Direct scoring of scorecards can be
performed on Angoss datasets and external
files or tables.
10. Scorecard performance can be visually
assessed using the Model Analyzer.
11. The Process Map keeps an audit trial of the
entire scorecard workflow.
Reject Inference
Traditional application scorecards make certain
generalizations that make it difficult for financial
institutions to capture all “good” customers.
Inevitably, some customers that are accepted
will end up defaulting on a loan, or missing
payment, while some customers that are
categorized as “bad” and are subsequently
rejected might actually be credit worthy
(profitable) customers. This “selection bias” can
be minimized to ensure credit scorecards can
predict the behavior of the total applicant
population.
Reject inference methods are used to infer the
behavior of rejected applicants. This helps to
ensure more accurate and realistic performance
of credit scoring models for all applicants, while
identifying new customers at lower potential
risk. Ultimately, this enables lending and credit
organizations to maximize their business
growth by optimizing the risks and rewards for
individual lending decisions.
KnowledgeSTUDIO provides 3 reject inference
methods for credit application scorecards:
• Proportional Assignment – Assigns the reject
population into ”good” and “bad” categories at
random, maintaining a set proportion of “bad”
records.
• Hard Cutoff – Scores the population and
assigns the reject population into “good” or
“bad” categories based on a specific score
cutoff.
• Parceling – Assigns the reject population into
“good” or “bad” categories based on the
relative amount of “bad” customers within a
set of user defined score ranges.
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Behavior Scorecards
Rather than screening new candidates for
approval, behavior scorecards score existing
customers in order to predict the probability
they will exhibit certain types of behavior. This
can work both ways, allowing the service
provider to identify customers who are likely to
default, commit fraud, miss payments,
bankruptcy, write-offs etc., as well as recognize
customers who are likely to continue “good”
behavior, or qualify for further credit or
improved services.
This risk and reward management approach is
valuable in a variety of different scenarios.
Regulations, such as the Basel Accords, place
stringent requirements on institutions with
respect to transparency and accountability.
Meanwhile, these organizations are constantly
pressed to modify their scoring systems to
maximize profitability while mitigating losses
and minimizing costs.
To comply with these pressures, credit risk
models are being extended throughout the
entire lifecycle of financial products and
services. Behavioral scoring can feed into many
different models such as retention, account
management and collections services.
Retention
• Optimize interest rates, premium values or
credit amounts, i.e. increase the interest rate
on a loan or approve/reject line of credit
increases.
• Focus marketing campaigns and customer
retention strategies on low risk customers.
Account Management
• Allow “good” customers to go over credit
limits or past due dates.
• Determine if a credit card should be reissued.
• Offer better pricing on services or products.
Collections
• Improve collections and recovery strategies.
• Separate customers into low and high risk to
focus collections resources at appropriate
stages.
• Direct delinquent accounts to the best
collection strategy and channels: mailing, call
center, legal, doorstep.
• Optimize dialer lists.
• Suggest the next best action.
Strategy Trees
KnowledgeSTUDIO goes beyond the scorecard
capabilities that help to define the categories
used in behavioral risk and reward analysis.
Users can extend the benefits derived from
Decision Trees with Angoss’ unique Strategy
Trees.
Strategy Trees provide a familiar structure that
allows users to develop distinct customer
segments using multiple target variables. This
provides more feedback and control when
monitoring key performance indicators (KPIs)
used to classify consumers.
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This feedback is presented in a single Decision
Tree interface, eliminating the need to create
multiple trees and significantly reducing the
time it takes to arrive at various decisions.
Smarter lists contribute to increased revenues,
reduced costs, and lower risk profiles by
helping to improve customer account
management—before and after delinquency—
while proactively helping to detect and reduce
incidents of fraud and abuse.
SCORECARD HIGHLIGHTS:
Once segments have been defined within a
Strategy Tree, users can assign treatments or
actions to these segments, based on where
they fall within the “good” and “bad” spectrum
and the rules assigned to these classifications.
Treatments can include activities such as
varying credit limits, collections strategies
options or marketing campaign options. The
applied treatments can be continously
monitored by the validate strategy function.
Analysts can combine data mining models and
customer scores with user-defined strategies
and treatments to produce highly targeted lists
in support of direct and database marketing, as
well as credit and collections processes.
• Comprehensive data profiling and partitioning
• Optimal binning using Weight of Evidence for
coarse classing of variables
• Logistic regression models
• Reject inference techniques for inferring the
behavior of rejected applicants
• Model analysis and validation tools
• Scorecard scaling
• SAS, SQL, PMML and XML code generation
• Direct deployment capabilities
• Process Map for scorecard workflow audit
STRATEGY TREE HIGHLIGHTS:
• Intuitive, user-friendly tree structure to develop
segments faster
• Visualizes multiple target variables, KPIs and
treatments for significantly faster speed to
decision making and action
• Strategy verification and monitoring for on-going
development of strategies
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About Angoss Software
As a global leader in predictive analytics, Angoss
helps businesses increase sales and profitability,
and reduce risk. Angoss helps businesses
discover valuable insight and intelligence from
their data while providing clear and detailed
recommendations on the best and most
profitable opportunities to pursue to improve
sales, marketing and risk performance.
Our suite of desktop, client-server and big data
analytics software products and Cloud solutions
make predictive analytics accessible and easy to
use for technical and business users. Many of
the world's leading organizations use Angoss
software products and solutions to grow
revenue, increase sales productivity and improve
marketing effectiveness while reducing risk and
cost.
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©Copyright 2013. Angoss Software Corporation – www.angoss.com
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