FICO Decision Management offers advanced analytics and real-time decision capabilities to enhance business strategies and outcomes.
| Product | Mindshare (%) |
|---|---|
| FICO Decision Management | 19.1% |
| Experian PowerCurve | 15.7% |
| Quantexa | 12.8% |
| Other | 52.400000000000006% |
It provides a robust platform designed to optimize decision-making processes through predictive analytics, data integration, and automated workflows. This system addresses complex business challenges by leveraging machine learning and AI, ensuring scalability and efficiency.
What are the essential features of FICO Decision Management?In banking, FICO Decision Management is commonly used to improve credit risk assessment. Retailers use it to personalize customer interactions. In healthcare, it's implemented for better patient data analysis. This diversity showcases its adaptability across industries.
| Author info | Rating | Review Summary |
|---|---|---|
| Project Manager at a financial services firm with 501-1,000 employees | 4.5 | I find FICO OM effective for loan origination due to its flexibility and stability. While setup was straightforward, customer service is below average and credit products need improvement. I recommend it. |
| CEO with 1,001-5,000 employees | 4.0 | No summary available |
| CEO with 1,001-5,000 employees | 4.0 | No summary available |
| CEO with 1,001-5,000 employees | 4.0 | No summary available |
Streamlined loan origination processes.
Credit products.
Two years.
No.
No.
Below average.
Straightforward.
Flexible.
Yes, we did.
I would recommend buying this product for loan origination purposes.
Decision optimizer is one of FICO’s Decision Management Tools and is designed to address some specific challenges in customer decisioning, particularly that there are often competing objectives and very large numbers of customers (and thus customer decisions) involved. Combine this with the many possible action combinations, uncertainty about what customers might do, as well as uncertainty about the actual business impact of each decision and coming up with the best approach is complex. Once you have an approach or strategy to address these issues it must still be implemented to be useful and that means handling deployment as well as convincing business users of the value of the approach.
FICO Decision Optimizer uses decision models to encapsulate the business impact of customer decisions. These models incorporate KPIs, business constraints and goals. They map possible actions to risks and opportunities and manage all the links between these elements. Once you have a model developed, Decision Optimizer runs data, all your customer records or a subset, through the model to determine the optimal actions to these customers – optimizing to maximize or minimize the targets you identified while meeting your constraints. Uses include champion/challenger comparison, what-if analysis, stress testing, exploring the efficient frontier and tuning already deployed rules for customer treatment. Decision Optimizer can be used in Basel Stress testing, early stage collections, credit line management, origination and more.
The process for Decision Optimizer involves going from data to developing a model, optimizing the model to drive scenario selection and then generating a decision strategy. Predictive analytics from FICO Model Builder or from SAS/SPSS/R etc can be fed into the models. Results can be as a set of actions or as a decision tree. Decision tree results can be driven out to set of business rules in FICO Blaze Advisor or exported using the well defined PMML model for Decision Trees. This allows the output to be integrated to various FICO solutions like FICO Origination Manager or FICO TRIAD Customer Manager as well as with the rest of the Decision Management Platform products like FICO Blaze Advisor.
Decision Optimizer is a web-launched client talking to a remote server for the compute power that supports multiple users on the same model. The initial interface includes a number of key elements. For scenario design you can manage:
Many scenarios can be defined using these elements and these scenarios can be grouped. The core of a scenario is a model of the components and how they interact. This connects the calculations, input data, metrics and treatments/actions. Each calculation has inputs and outputs that are connected by the equation or look up table. At one end of the model are the fixed facts and the account inputs and at the other are the metrics you want to optimize for and the constraints that must be met. One or more layers of calculations link these and the available treatments/actions into a network model: What you know, what you can do and what you care about.
Once scenarios have been executed and the results gather the overall results for different scenarios can be viewed in a grid for the scenarios in the group. All the various output metrics are shown so they can e compared.
For each scenario you can specify that the results must be created as a decision tree (that can be deployed) or as a set of optimal actions for each customer (for a batch process like a mailing campaign). Decision Optimizer can automatically simplify the generated decision trees to make them easy to read and deploy. One particularly nice feature allows the specification of business palatability constraints to make sure the tree will be acceptable e.g. specifying that, all other things being equal, higher credit scores should be more likely to be accepted than not, no matter what seems “optimal.” In addition tree templates can be defined that limit the attributes and bins that can be used also easing deployment and business believability.
Decision Optimizer allows portfolio level optimization of individual customer decisions and allows the results of this optimization to be deployed into a Decision Management System by generating a deployable decision tree that makes near-optimal assignments. This means that individual customer decisions can then be made that are very close to the optimal without the need to do optimization at run time.
Decision Optimizer is one of the products in our Decision Management Systems Platform Technology Report and you can get more information on it here.
FICO Xpress Optimization Suite is part of the FICO Decision Management tools suite. It can be used in conjunction with various FICO applications too. It is used directly with their Marketing product and is embedded in their Decision Optimizer product that can generate optimal rules for their Originations, Customer Management, Collections and Fraud products. Outside of financial services FICO have used Xpress to optimize retail shelf positioning and layout, energy pricing and generation (one of Xpress’ largest markets) and more generally in scheduling, logistics and planning problems.
To tackle any optimization problem you need to model the problem as an optimization problem, solve it, explore the various alternatives before deploying and maintaining the model. Xpress therefore has various features:
Customers include P&G, the NFL, American Airlines, Budget, Avis, amazon.com and a number of OEMs.
The core Xpress tool contains an editor, debugger, profiler, progress graphics, visualization, wizards, Mosel extensions and deployment. Recent releases have improved performance (always a focus for optimization tools) and added support for cloud-based solving. Xpress Insight has a number of capabilities that can be mashed up into a compound user interface:
This kind of environment is important in optimization because the result of a model is often not readily explicable – the best answer is mathematically the best but the drivers for this are too complex to be readily understood. Being able to interact with the model, test the edges, try different scenarios and compare them all helps make business owners feel comfortable with the result – the optimization equivalent of impact analysis. Without this, business owners will not be active participants in optimization (just as impact analysis tools in business rules are essential for business user rule management). Once the best model and results are clear then optimal actions/assignments can be deployed, business rules derived from the model can be deployed or the mode can be deployed itself potentially with a “remote control” wrapper to make it easy to consume in an enterprise SOA environment. These Insight tools can be embedded in HTML applications and can interact with the engines using Javascript.
You can get more information on FICO Xpress here and FICO is one of the vendors in our Decision Management Systems Platform Technology report.
I wrote about FICO Model Central, FICO’s model management product, back in February of 2012. A new release, 3.0, is shipping with a new feature called Model Deployment Accelerator for rapid deployment.
To recap, FICO Model Central automates analytic model compliance tasks, provides alerts and visualization to help you identify needed model updates, supports getting multiple vendors’ models into production, eases embedding models into decision services and integrates optimization and simulation. It comes in incrementally adoptable Foundation, Professional Development and Advanced Decisioining tiers. Model Central can manage models whether or not the new features of Model Central are used to deploy the model.
New in 3.0 is the Model Deployment Accelerator, a capability for rapid deployment of models expressed as SAS code, as PMML or loaded from FICO Model Builder, to scoring services. Model Deployment Accelerator allows models to be quickly deployed as a service, eliminating the time and potential errors involved in reprogramming the model for production. For each model the user can see every version of the scoring services generated for that model. Configuring a new service requires a model definition file, a Base SAS file for instance, and a pair of audit files (input data and expected outputs for that input data). Uploading the SAS script to Model Central converts it to Java which is then encapsulated into a callable service. These scoring services can then be integrated into a web-managed rules-based Decision Service. SAS files can include pre-processing, variable calculation, transformations, segmentation logic and other script elements as well as the scoring formula and reason code calculations.
Once the Java scoring service is generated, the uploaded audit files (or a sample from those files) can be run through the code to confirm that the results are what the user expected. An audit report is generated that highlights any differences between expected and actual results that are outside a tolerance specified by the user. The report can display just the rows with problems, helpful when there are lots of test rows. If there are problems then a corrected scoring program or audit file can be uploaded, and the test repeated.
Once a user is satisfied that the service is scoring correctly, they can deploy the model as either a standalone Java service or as a Java packet suitable for use inside FICO Blaze Advisor (FICO’s business rules management system). Once this is generated the version of the scoring service is locked so it cannot be changed (keeping verification reports, code etc available for future auditing and reference). The generated JAR file, as well implementation documentation and some supporting Java libraries , are packaged up into a zip file ready for deployment. Once the scoring service is actually deployed, or subsequently if it is retired, Model Central also allows the user to manage additional status levels, such as “In production”.
You can get more information on FICO Model Central here and FICO is one of the vendors in our Decision Management Systems Platform Technology Report.
