BETTER DECISIONING - StatSoft
Transcript of BETTER DECISIONING - StatSoft
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BETTERDECISIONING
STATISTICA Decisioning PlatformTM
for Financial Services
Combining Predictive Analytics, Text Mining,
and Rules Management for Reducing
Credit Risk and Increasing Profits
Financial Services companies face the challenging task of making credit decisions in a complex and uncertain environment.
Decisions regarding credit limits and approvals must be
compliant with regulatory and policy rules, reflect known
segmentation (rules) among products and customers, and
incorporate standard as well as advanced and emerging
predictive risk modeling techniques, such as text mining.
The STATISTICA Decisioning Platform™ for Financial Risk
Management is a proven solution at some of the largest
and most progressive financial institutions in the world.
The STATISTICA Decisioning Platform provides the most
effective platform for financial services organizations
to combine structured and unstructured data, manage
simple and complex segmentation (pre-scoring) and policy
(post-scoring) rules, and to incorporate predictive models
and conditional scoring logic into efficient managed
decisioning flows. These flows can be directly deployed to
batch or real-time scoring environments without requiring
any “re-programming.”
With STATISTICA, the tasks of building and deploying
scorecards, scoring models, and flows can now be
completed in a fraction of the time, allowing more
models to be managed and routinely recalibrated. More
customers can be approved for credit with higher limits
without increasing the default rate. Batch scoring of all
customers and accounts will be faster, and real-time
scoring for online or other real-time applications will be
more responsive.
The STATISTICA Decisioning Platform addresses the typical array of challenges,
and scales to financial service organizations of all sizes:
• regulatory pressures to provide consistency and transparency in credit risk decisions
• a wide array of loan products to meet the needs of a variety of customer segments,
both in personal and commercial lines
• demands to increase the profitability of loan products without increased default risk
of exposure to losses
• collaborations between business stakeholders supporting credit products and the
quantitative and IT staff responsible for implementing the systems and models for
making credit decisions
The STATISTICA Decisioning Platform addresses these needs by:
• combining predictive analytics, flexible rules and rules management, and leveraging
non traditional data sources using techniques such as text mining
• providing an integrated platform in which the predictive models to support a large
number of loan products are managed, with access control, versioning, and history
to eliminate the time-consuming and error-prone process of replicating conditional
scoring models and rules for deployment
• delivering a scalable platform that can score large data volumes efficiently, and perform
real-time scoring in milliseconds while referencing sequences and combinations of rules
and conditional scoring (predictive) models, as well as logic for returning reason codes
• empowering quantitative analysts with an integrated, flexible workbench of
predictive analytics, text mining, data transformations, and graphical data analysis
for optimal credit risk modeling
The STATISTICA Decisioning Platform is the only enterprise predictive analytics
and decision management software platform:
• for use across all departments and roles (analysts, adjusters, investigators, IT engineers,
LOB managers and workers, etc.)
• that combines predictive analytics, text mining, and rules to cover all aspects of
evaluating and scoring claims, customers, and applicants. Text mining is for making
use of adjuster notes, medical reports, and other documents. Rules integrated with
predictive models translate predictions into business decisions.
“The bottom line is we need rules combined with predictive
analytics and text mining to gain more profitable customers
and decrease our risks. Some credit decisions are completely
driven by rules. Other processes take the output from
predictive models and translate them into credit decisions and
other offers. Also, rules govern our compliance with relevant
regulations. In STATISTICA Enterprise, rules and models for
different product lines are easy to manage, and applications
are scored in less than a second!”Senior VP of Risk, Major European Bank
Include:• Business Rules• Segmentation Rules• Regulatory Requirements
Model:• Credit Default Probability• Time To Default• Credit Limits
Declined
Compute andreport reasonfor rejection
ReportCredit Limit
Prepare:• Data• Predictor Selection• WOE Coding• Segmentation
Deploy:Repository forBusiness Rulesand Models
Approved
Retrieve:Historical Data
Application Data,Credit History
Build:Base Models
Applicationfor Credit
Business Consumer
AutomaticDecisioning
STATISTICA Enterprise – The server platform for delivering analytic and business intelligence applications to departments and divisions within the enterprise via centrally-managed queries, analysis templates, report templates and dashboards.
STATISTICA Text Miner – A large selection of retrieval, pre-processing, and analytic/interpretive mining procedures for unstructured text data and web pages. Optional specific solution packages.
STATISTICA Data Miner – The most comprehensive selection of data mining solutions for the enterprise, with powerful model development and deployment (solution publishing) tools and database/data warehouse integration. Optional solution packages for specific applications and domains.
STATISTICA Live Score – The server platform for integrating predictive modeling into business processes and line-of-business applications through highly-efficient, real-time scoring via predictive models managed using STATISTICA Data Miner and STATISTICA Text Miner.
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Delivering Predictions to the Right People
The STATISTICA analytics suite of applications efficiently delivers accurate predictive
models for improved decision making throughout an organization. There are several
components to make this happen:
User Personalization
Within an organization, personnel with differing skills and responsibilities collaborate
to achieve an outcome. STATISTICA includes user personalization, so that differing user
groups see the data, capabilities, user interfaces, options, and workflows specific to their
areas of responsibilities. For example:
– Quantitative analysts and risk modelers have access to the full suite of powerful
predictive modeling options.
– Business analysts define and verify the business rules and conditional scoring logic
for which predictive models to apply to which processes/products, and for when/how
to override the predictions from the models due to other business rules or regulatory
guidelines
– Lines of business workers see results and recommendations specific to their objectives
and business processes.
– Risk analysts can create and schedule the standard model evaluation, population
stability, and other reports required to monitor and document the quality of the
decisioning process.
Model Management
Within an organization, there are many areas for applying predictive analytics. For
example, predictive models can deliver recommendations for different products,
departments, customers, and so on. STATISTICA Decisioning Platform makes it easy
for the quantitative analysts who are responsible for the verification, deployment, and
ongoing management of these models. Models are managed in one central location, on
the STATISTICA server. Models are managed with versioning and history so that analysts
have complete control over which version is approved and meets regulatory requirements.
Deployment to Production
A typical bottleneck for efficient utilization of decisioning
flows to support risk management is the time required
to deploy models to the production environment. In
the STATISTICA Decisioning Platform, this process does
not require any reprogramming of data preprocessing
steps, rules logic, or prediction models. Instead, entire
decisioning flows, and the references to rules, models,
and scoring logic that they contain, can be moved from
the development to the system test environment, and
then to the production environment via simple point-
and-click operations by authorized users. Decision flows
and the components they reference are managed as
versioned templates and objects that can be moved
across environments and do not have to be recreated or
reprogrammed.
Scoring
In all the ways in which predictive analytics can be
utilized, there are different business needs that require
different types of scoring of data. In some cases, real-
time scoring is needed, such as when the information
on an insurance claim changes or when an instant credit
decision is required. In other cases, a set of data, in a
file or database or data warehouse, needs to be scored
offline. For example, a set of prospective customers is
scored based on their propensity to purchase a new
product so that customer service personnel will focus time
and attention on the prospects most likely to become
profitable customers.
© Copyright StatSoft, Inc., 1984-2012. StatSoft, StatSoft logo, STATISTICA, Decisioning Platform, and Better Decisioning are trademarks of StatSoft, Inc.DPFIN201204r01
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About STATISTICA and StatSoft
StatSoft Inc. is one of the largest global providers of analytic software. STATISTICA is an enterprise-wide, scalable,
Web-enabled system that is used by a variety of industries in mission-critical applications where predictive modeling
helps increase productivity and bottom-line profitability, while also helping protect life, improve safety, and save the
environment.
While easier to use and more cost effective than its competitors, STATISTICA is one of the most technologically advanced
tools in the industry, with uncompromising attention to detail and overall quality, which ensures success for its users.
For two consecutive years, STATISTICA has been recognized as the primary data mining tool of choice in an annual survey
conducted by Rexer Analytics of corporate and consultant data miners. STATISTICA was also recognized for receiving the
highest satisfaction ratings among those surveyed.
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Decision Rules
Predictive analytics requires both predictive modeling
and decision rules. The two work hand-in-hand. An
organization’s quantitative analysts can use the predictive
modeling and data mining approaches in the STATISTICA
platform to detect and capture patterns in their historical
data. Those patterns have a business context. For
credit risk modeling, decision rules subset and transform
the applicant’s data into segments of interpretable,
transparent customer groups, and policy (post-scoring)
rules automate the interpretation of the risk prediction
into specific next steps for the application, as well as
reason scores to communicate back to the applicant.
By incorporating STATISTICA’s new models, profits can increase 80% or more with improved acceptance rates, while defaults remain at approximately the same level.