Risk Analytics

Balance Growth & Mitigate Loss with Credit Lifecycle

Credit lenders and insurers face the need to nurture long term relationships with customers in order to increase margins and revenues. It all starts by understanding customers, their needs, preferences and goals, and providing an improved customer experience.

The Angoss analytics software suite helps help Chief Risk Officers and data/risk analysts manage loss within its risk tolerance, while pursuing financial objectives and complying with Basel II and Basel III regulations. Applying data mining to existing data and creating models with predictive analytics provides CROs a complete picture of risk management process within the credit risk lifecycle.

Data and risk analysts can easily:

  • Profile payers versus non-payers
  • Segment applications into behavioral profiles and personas
  • Build models on days past due
  • Score portfolios and predict behavior
  • Create what-if scenarios
  • Employ reject inference

Benefits of using Angoss predictive analytics for risk management throughout the credit risk lifecycle management include:

  • Lowering transaction costs and time-lags associated with decisions to extend credit to consumers and businesses
  • Improving the ability to optimize individual credit decisions, and overall portfolio composition and performance
  • The ability to analyze, assess and benchmark performance of credit portfolios over time
  • Analyzing fraud patterns and model fraud cases using neural networks to build more effective strategies faster
  • Visualize portfolio analytics with scorecards and optimized strategies with multiple KPIs
  • Analyze claim portfolios for insight into the key drivers of claims fraud and abuse costs and loss ratios
  • Comply with Basel II and Basel III
  • Get up and running quickly

Angoss can help your organization balance growth and mitigate loss through the credit lifecycle.

Risk Analytics


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The Forrester Wave™:
Big Data Predictive
Analytics Solutions,
Q2 2015



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