Experian Powercurve

Experian PowerCurve is a decision management platform upon which the company delivers a variety of customer-centric and analytically driven decisioning solutions, as illustrated in Figure 1.

Fig 01 – PowerCurve decisioning solutions

The PowerCurve platform makes use of a common component called PowerCurve Strategy Management and there are three core components: Design Studio, which provides the user interface and a common design environment for decision strategies and simulation; Decision Agent, which is the decisioning engine; and a common repository (not shown) for sharing reusable resources such as previously used strategies and so forth. It has an open architecture built on HDFS and Apache Spark that supports plug-ins from third party machine learning environments. There is also a core element called the Analytic Component Extension (ACE) framework, discussed below.

Additional capabilities are provided within the PowerCurve platform and associated solutions. For example, there is a Virtual Assistant to support customer engagement, and there are various pre-built templates to support particular functions.

Customer Quotes

“Today Alfa Bank’s employees are actively using PowerCurve to assess applications for all unsecured cash loans, credit cards, and refinancing products. We have substantially increased the application processing speed and decisioning accuracy, while maintaining the credit portfolio quality. Due to its flexibility, PowerCurve has made it possible to implement several new products in a short timeframe.”
Alfa Bank

Experian has recognised that there are four trends within the market related to operationalising analytics: a demand to use machine/deep learning models for predictive functions; issues around the operationalisation of those models; requirements around the monitoring, management and replacement (or retraining) of models; and governance issues with respect to both explainability and bias. As a result, Experian is in the process of enhancing PowerCurve, which was originally released in 2011, to support these capabilities. For example, it has introduced the ACE Framework. This provides an environment for plugging in predictive models developed within third party environments. Currently ACE supports PMML (predictive modelling mark-up language) based models as well as those developed using R or Python. H2O support is forthcoming and other plug-ins will be introduced based on customer demand.

However, there are some elements of what Bloor Research calls AnalyticOps that are not yet fully implemented, though Experian is by no means alone in this. For example, there are facilities for model auditing today, but it is not an easy and simple process. Experian plans to enhance both its explainability and model governance capabilities, adding model performance monitoring. While there are facilities for automatically deploying and testing models into production there are no workflow/approvals processes to complement this yet. The company is considering whether to implement such processes within PowerCurve or to leave this to third party tools.

AnalyticOps concerns the operationalisation, monitoring, improvement, management and governance of augmented intelligent models. As such models become more and more widely deployed, AnalyticOps capabilities will become more and more vital. Where most companies today have a handful of deployed models, if that, we expect large organisations to have thousands or tens of thousands of models to deploy and manage in the near future.

In practice, there are very few vendors that can address all, or even some, of the AnalyticOps requirements of a modern data science environment. Experian PowerCurve is one of these: it has  a business user-friendly design environment enabling business users to easily include models in decision strategies and is in process of adding and enhancing capabilities for identifying bias, model explainability, model performance monitoring and model management. In this context it is noteworthy that the company supports a methodology known as FACT for fairness, accuracy, (responsibility to the) customer, and transparency (including both explainability and auditability). By way of contrast, most vendors in this space have no model management capabilities at all, few have any capabilities with respect to bias and explainability is by no means common.

The Bottom Line

However, PowerCurve has not historically been marketed as a generic decisioning platform but only as providing underlying capabilities to support the various decision management solutions offered by Experian. While the latter remains a valid strategy, it means that PowerCurve remains a well-kept secret. Given that many of the well-known data science platforms cannot match the capabilities of PowerCurve when it comes to AnalyticOps – at least at this time – we would like to see PowerCurve marketed more aggressively to a wider market.