Predictive Analytics In Banking
With its enormous repositories of transactional and customer profile data, the banking industry is rich with potential for the application of predictive analytics. There’s no better example of applied predictive analytics in banking than Pega’s business process management (BPM) and customer relationship management (CRM) solutions for the financial services sector. Eight of the top 10 global banks use Pega technology for its unique ability to not only implement but also operationalize predictive analytics in banking.
How Pega Implements and Operationalizes Predictive Analytics in Banking
Pega is the leading provider of BPM and CRM systems that enable businesses to intelligently automate their back- and front-office processes. At the core of the Pega Build for Change® platform is a central decisioning hub with two complementary components:
Pegasystems and Finextra conducted a bank and corporate client experience survey, exploring customer sentiments around sales, onboarding, KYC and servicing.
Read this Pegasystems survey of more than 1,000 participants – a combination of consumers and business decision makers from retail banking organizations.
- A predictive analytics engine that ingests and analyzes diverse data types—including unstructured “big data”—to develop predictions about the likely behaviors of individual customers. In the case of predictive analytics in banking, this may mean projections about a particular customer’s receptiveness to different marketing offers, or about their propensity to repay an outstanding debt, for example. Together with this predictive analysis, the engine also supports adaptive analysis whereby projections are continually updated and adjusted in response to a customer’s interactions with the business.
- An automated decisioning engine that applies configurable business rules to analytic and contextual information to determine the Next-Best-Action to take within a given business process at a given moment (such as an in-progress customer interaction). The engine supports a range of decisioning methods including declarative rules, decision trees, decision tables, and more.
Decisions generated by the decisioning engine are shared in real time with point systems (such as customer service desktops) through a web services layer. In this manner, predictive analytics are brought directly and immediately to bear on day-to-day bank operations.
Examples of Predictive Analytics in Banking
The ability to operationalize predictive analytics in banking is evident in many of Pega’s banking operations solutions. For example:
- Pega’s Next-Best-Action Marketing solution uses predictive analytics to determine the very best cross-sell, up-sell, or retention offer to deliver to a specific customer at a given point in time, through outbound or inbound channels.
- Pega collections software uses predictive analytics to guide collections personnel step-by-step through customer interactions, determining the optimal treatment plan based on the customer’s past behavior, their value to the business, and their responses during the course of the interaction.
Pega also applies predictive analytics to other commercial and retail banking operations such as churn management, risk management, and more.