FICO Xpress Optimization Suite · FICO ®Precision Marketing Manager FICO® Retail Action Manager...

Confidential. This presentation is provided for the recipient only and cannot be reproduced or shared without Fair Isaac Corporation's express consent.

© 2011 Fair Isaac Corporation. 1

FICO Xpress Optimization Suite

Oliver BastertSenior ManagerXpress Product Management

September 22 2011

Webinar

© 2011 Fair Isaac Corporation. Confidential.2 © 2011 Fair Isaac Corporation. Confidential.2

Agenda

» Introduction to FICO

» Introduction to FICO Xpress Optimization Suite

» Performance

» Distributed Modelling and Solving

» Case Q&A


Introduction to FICO


Profile

The leader in predictive analytics for decision management

Founded: 1956

NYSE: FICO

Revenues: $605 million (fiscal 2010)

Products and Services

Scores and related analytic models

Analytic applications for risk management, fraud, marketing

Tools for decision management

Clients andMarkets

5,000+ clients in 80 countries

Industry focus: Banking, insurance, retail, health care

Recent Rankings

#1 in services operations analytics (IDC)

#7 in worldwide business analytics software (IDC)

#26 in the FinTech 100 (American Banker)

Offices

20+ offices worldwide, HQ in Minneapolis, USA

2,200 employees

Regional Hubs: San Rafael (CA), New York, London, Birmingham (UK),Munich, Madrid, Sao Paulo, Bangalore, Beijing, Singapore

FICO Snapshot


FICO delivers

superior predictive analytic solutions

that drive

smarter decisions.

Thousands of businesses worldwide,

including 9 of the top Fortune 10,

rely on FICO to

make every decision count.


Transforming Decision Management

Sharpen customer-centric decisions

Predict customer needs and behaviorPinpoint best offer and action

PREDICT

Increase customer profitability

Reduce losses from fraud and riskConnect all decisions about a customer

PROFIT

ADAPT

Change faster and respond flexibly

Change business rules instantlyCreate a test-and-learn culture

IMPROVE

Continually improve strategy performance

Model decisions for greater controlOptimize strategies to grow faster

© 2011 Fair Isaac Corporation. Confidential.7

IMPROVE Strategy Performance

Model decisions for greater control

» Identify the decision drivers and the effects of every action

» Use the decision model as a planning tool to test changes in the business environment

Optimize strategies to grow faster

» Create analytically derived strategies to meet specified business objectives

» Design strategies with millions of variables – instantly

Uses FICO optimization software to

create analytically driven decisions on

fleet distribution and utilization

Deployed across every key market in

continental Europe

• Benefit estimated at $19 million

“FICO™ Xpress tells us, for example:

On Friday morning, bring only four

cars from Heathrow to Mayfair, and

bring another four from Stansted

Airport. The utilization of our fleet has

gone up by one or two percentage

points.”


FICO: Game-Changing Analytics

First commercially available credit scoring systems

First automated origination systems with analytics

First cross-bureau credit scores

First small business scoring systems

First neural network-based fraud solutions

First cardholder profiling for fraud

First insurance underwriting scoring systems

First adaptive control systems for managing card accounts

First credit line optimization solutions

First predictive systems for insurance fraud

First analytic systems for retailers to optimize offers

First adaptive analytics for fraud

First credit capacity scores

First score for prescription adherence

1960s 1970s 1990s1980s 2000s

FICO holds 100+ patents in analytic

and decision management technology,

with 150 more patents pending


Tools Solutions Scores Tools Solutions

Building an Analytic Advantage

Business

Intelligence

Descriptive

Analytics

Predictive

Analytics

Decision

Optimization

Summarize Past and Current Behavior

Automatically

take the ideal

action on each

individual

Target each

decision to a

customer’s future

behavior

Make different

offers to groups

of customers

Understand

the trends in

the business

Predict Future Behavior and Adapt

Decis

ion

Valu

e

Analytic Capability


FICO Product Portfolio

For Lifecycle Specific Decision Processes

Marketing OriginationCustomer

ManagementCollections and

RecoveryFraud

Management

Applications

FICO® Precision Marketing Manager

FICO® Retail Action Manager

FICO®

Origination Manager

FICO® TRIAD®

Customer ManagerFICO® Debt Manager™

FICO™ Recovery Management

System™

FICO™ Falcon®

Fraud Manager

FICO™ Insurance Fraud Manager

For Any Decision Process

Scores

B2B: FICO® Score FICO® Credit Capacity Index™

FICO® Insurance Risk Scores

B2C: myFICO®

Tools

Business Rules Management: FICO™ Blaze Advisor®

Predictive Analytics: FICO™ Model Builder

Optimization: FICO™ Xpress Optimization Suite FICO™ Decision Optimizer

Professional Services

Custom Analytics

Operational Best Practices

Strategy Design and Optimization


Introduction to FICO Xpress Optimization Suite


Xpress Optimization Suite

Solvers

Modelling

Development

De

plo

ym

en

t

LP

MIP

QP

MIQP

QCQP

MIQCQP

SLP

MISLP

NLP

MINLP

CP

MoselMOdelling and Solving Environment Language

XADGraphical user interface development using Mosel

.NET/Java/C/C++/VB

IVEDevelopment Environment

IVE-XADGUI development

Programming

Interfaces

Solver API Mosel API BCL*

GUI

* Builder Component Library for modelling in a programming language


Xpress-IVE: Mosel & Optimizer

» Editor

» Debugger

» Profiler

» Progress graphs

» Visualization

» Wizards

» Mosel extensions

» Deployment


Production Planning


Product Portfolio & Pricing Optimization

FICO Optimization Dashboard: Debt Consolidation ModuleConfidential – do not copy


Portfolio Rebalancing Solution


Facility Location with Google Maps integration


Key Features and Benefits of Xpress-Mosel

Features Benefits

» Advanced programming languages:

» Algebraic modeling language

» Procedural programming language

» Entire Mathematical Model can be stored in one place for rapid development and easy maintenance.

» Utilize different solvers in the same model

» From Mosel you can solve LPs, MIPs, MIQPs, Non-Linear problems, Stochastic problems, and Constraint problems

» Decompose & parallelize a model to take advantage of multiple CPUs/cores

» Faster solve times

» Make full use of your computing infrastructure through distributed computing

» Build a GUI exclusively within Mosel code

» Decreases development time, gets optimization in front of business user quicker

» Portable across operating systems » Mosel Model compiled in one OS can be deployed on all other supported Operating Systems, decreasing development time

» Open, modular architecture, User extensible

» User flexibility to solve the most complicated optimization problems

» not limited to/by predefined language features

» Compiled » Protects intellectual property

» Offers a variety of APIs and data connectors

» Easy deployment and works in heterogeneous environments


Xpress History and Product Focus

» 26 years of experience in modelling and optimization

» 24 years of experience in mixed integer optimization

» 12 years of experience in nonlinear optimization

» 10 years Xpress-Mosel, modelling and solving environment

» Integration of modelling and solving

» Focus on (potentially) exact solution methods

» Xpress-Solvers often can prove optimality of the solution

» They always give you information on the quality of the solution


Xpress Innovations

» Solving

» 1983: LP solver running on PCs

» 1992: parallel MIP (1997 on distributed PC/Linux networks)

» 1995/1996 : commercial branch and cut algorithm

» 1998: bound switching in dual simplex

» 2003: lift-and-project cuts

» 2009: parallel MIP heuristics

» 2010: LP/MIP solver crosses 64-bit coefficient indexing threshold

» Modelling

» 1983: general purpose algebraic modelling language (mp-model)

» 2001: algebraic modelling language combining modelling, solving, and programming (Mosel)

» 2005: profiler and debugger for a modelling language

» 2005: user-controlled parallelism at the model level

» 2010: algebraic modelling language supporting distributed computing


Xpress differentiators

» Unique capabilities for large scale optimization including ability to solve ultra-large problems (true64bit capabilities) and support for distributed modeling and optimization

» Complete set of state-of-the-art optimization engines that are robust, reliable and faster than competing solutions

» An easy-to-learn, powerful modeling and programming language, Xpress-Mosel

» The premier visual development environment, IVE, for developing mathematical models

» An intuitive drag-and-drop editor for creating GUIs that seamlessly integrate with the model for rapid prototyping and deployment

» A partner committed to solving all of your most difficult optimization problems


Xpress Optimization Suite Users


Recent enhancements

Xpress 7.1 delivers (GA Nov 2010)

» Solve much bigger problems

» The possibilities are limitless with the enhanced optimization and modeling support for solving ultra-large-scale models where the number of coefficients can exceed 2 billion.

» Solve large problems faster

» Cut solution times dramatically by leveraging distributed execution of Mosel models that can now be controlled from a master model across a heterogeneous set of machines.

» Get solutions faster with significantly improved solver performance

» Average increase of 50% arithmetic/50% geometric for multi-threaded MIP

» Average increase of 50% arithmetic/25% geometric for single-threaded MIP

» Average increase of 50% arithmetic/70% geometric for MIQP

» Significant improvements to speed and stability of quadratic simplex and SLP non-linear algorithms


Recent enhancements

» Improved developer usability

» Developers will also enjoy greater productivity from usability improvements to the development environment and enhanced modeling functionality such as the MIIS automated modeling error/infeasibility detection.

» Easier to integrate with other applications

» Optimization programmers will be pleased by the addition of simplified and more robust data exchange capabilities between Mosel and applications.

Xpress 7.2 delivers (GA April 2011)

» Exceptional public benchmark performance


Performance


Comparing Solver Performance

» Solver performance is important but not the only decision criterion

» Selection of benchmark sets

» Represent client mix of problems

» Solvable instances but not too simple

» Feasibility and optimality check of solution

» Numerically stable problems are preferred for performance benchmarks

» The only public benchmarks for optimization solvers is run by Hans Mittelmann. He frequently changes the benchmarking sets

» The best known collection of MIP instances is currently updated from version MIPLIB 2003 to version MIPLIB 2010 and will contain for the first time an agreed benchmarking subset.

» A benchmark comparison is always a snapshot of the performance of the available software at a given point in time.


The MIPLIB 2003 Experience

ProblemOld Best Known Obj.

Value (*)Xpress Improved Obj.

Value (**)GAIN

(|1-(**)/(*)|)

atlanta-ip 95.009549704 90.00987861 5.3%

msc98-ip 20980991.006 19839497.006 5.4%

protfold -30 -31 3.3%

rd-rplusc-21 171182 165395.2753 3.4%

sp97ar 664565103.76 660705646.5 0.6%

stp3d unknown 500.736 N/A

ds 283.4425 116.59 58.9%

momentum3 370177.036 236426.335 36.1%

t1717 193221 170195 11.9%

liu 1172 1102 5.9%

dano3mip 691.2 687.733333 0.5%

Op

tim

al

Un

so

lved

Solving Hard Mixed Integer Programming Problems with Xpress-MP:

A MIPLIB 2003 Case Study, Informs Journal on Computing, 2009

by Richard Laundy, Michael Perregaard, Gabriel Tavares, Horia Tipi, and Alkis Vazacopoulos


Geometric Mean is the comparison criterion of choice

» Instead of comparing the overall runtime on a given matrix set (or equivalently the average runtime or arithmetic mean on that set) the accepted way of comparing optimization solver performance is by comparing the Geometric Mean

» The presence of a few extremely small or large values has no considerable effect on geometric mean so it measures performance more accurately than arithmetic mean which is biased towards large outliers.

» The geometric mean denotes the most likely runtime you will observe for an instance of the test set.


Standard LP Problems (Barrier, Simplex)Public Benchmark by H. Mittelmann as of 29 Apr 2011, GeoMean

0

10

20

30

40

50

60

70

80

90

XPRESS CPLEX GUROBI MOSEK

Standard LP Problems (Barrier, Simplex)


Barrier on Large LP ProblemsPublic Benchmark by H. Mittelmann as of 29 Apr 2011, GeoMean

0

100

200

300

400

500

600

700

XPRESS CPLEX GUROBI MOSEK

Barrier on Large LP Problems


MIQP ProblemsPublic Benchmark by H. Mittelmann as of 29 Apr 2011, GeoMean

0

50

100

150

200

250

300

350

400

450

XPRESS CPLEX GUROBI SCIP

MIQP Problems


MIPLIB 2010 Benchmark setGeometric Mean, single threaded, MIPLIB 2010 paper

0.00

50.00

100.00

150.00

200.00

250.00

300.00

XPRESS CPLEX GUROBI


Distributed Modelling and Solving

c©2011 Fair Issac Corporation.

Schemes of parallelization

1. Simple submodel run

wait fortermination

compile/load/runsubmodel

process resultsresults

start

results

Submodel

Master

User



2. Iterative sequential submodel runs(decomposition algorithms)

process results

wait fortermination

start

run submodel

compile/loadsubmodel

results

results

Submodel

Master

User



3. Independent parallel submodels

wait fortermination

compile/load/runsubmodels

process resultsresults

start

results

start

...

Master

User Submodel 1 Submodel n



4. Communicating concurrent submodels

wait for events

process events

process results

startstart

...

compile/load/runsubmodels

results

broadcast updates/termination

events/results

Master

Submodel 1 Submodel n

User


Distributed solving

» Use all the computing power available in yourlocal network by solving (sub)models onremote machines

run master model

Local


Distributed solving


run master model run submodel

Local Remote


Distributed solving



Local Remote

» Physical location of model files, input andresult data depending on application


Distributed solving:Location of models and data

1. On local host» physical files or in memory (e.g. included in master

model or in calling host application)» a (master) model can recursively start new instances

of itself

submodel file

data, results


load

Local Remote



2. On remote host» configurable read/write access on remote machine

submodel file

data, results


load

Local Remote



3. Centralised repository» eases version control in multi-user environments

submodel file

data, results


load

Local Remote

Central repository


Distributed applications

» Multi-user application with Mosel model asdispatcher

Mosel server

User

UserD

atab

ase

Productionmachine

machineProduction

... ...

» Example: optimization applications in finance(solving large numbers of small to mediumsize problems)



» Decomposition with central data store

Database

User ...Moseloptimization master

Submodel

Submodel

» Examples: Column generation in transport orpersonnel planning; blockwise(Dantzig-Wolfe) decomposition in productionplanning



» Decomposition with remote, distributed datasources

Dat

aD

ata

User ...Moseloptimization master

Remotemodel

Remotemodel

» Example: Large-scale planning inheterogeneous computing environment


Case Studies


Portfolio rebalancing:Problem description

» Modify the composition of an investmentportfolio as to achieve or approach a specifiedinvestment profile.


Optimization application in MoselStandalone

Data files Mosel model

IVE

Output files

start applicationreturn results


Optimization application in MoselXAD GUI

Data files Mosel model

XAD

Output files

outputSummary

ration fileConfigu-

start applicationreturn results


Optimization application in MoselEmbedded into host application

Mosel model

Output files

outputSummary start application

return results

JavaData files


Optimization application in MoselAlternative interfaces

outputSummary

Data files

start application

Mosel model

XAD IVE Java

Output files

outputSummary

Data filesration fileConfigu-

return resultsoutputSummary


XAD interface:Detailed results


XAD interface:Parameter and version log


XAD interface:Multiple run summary


Some highlights

» Model:» easy maintenance through single model» deployment as BIM file: no changes to model by

end-user» language extensions according to specific needs

» Interfaces:» several run modes adapted to different types of

usages» efficient data exchange with host application

through memory» parallel model runs (Java) or repeated sequential runs

(XAD)


Aircraft routing:Problem description

» For given sets of flights and aircraft,determine which aircraft services a flight.

» Aircraft are not identical» they cannot all service every flight» a specific maintenance site must be used per plane» some scheduled long maintenance breaks

» Starting condition: each aircraft has a startingposition and a specific amount of accumulatedflight minutes


Aircraft routing:Application architecture

» Master problem: route selection» Subproblems: route generation (one instance

per plane)» parallel, possibly remote, execution of submodels

» User interface (optional): XAD GUI


Aircraft routing:Application GUI


Aircraft routing:Visualization


Aircraft routing:User interaction


Q&A

Confidential. This presentation is provided for the recipient only and cannot be reproduced or shared without Fair Isaac Corporation's express consent.

© 2011 Fair Isaac Corporation. 35

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