THE CHALLENGE

The business was experiencing a multitude of issues that included:

• A lack of existing capability to deploy models
• Technical constraints encountered in concluding upfront and ongoing analysis
• A lack of structured and complete data
• An undefined pattern library and ability to govern the model risk lifecycle 

BUSINESS OBJECTIVES

• Faster time to deploy models through developer self-service
• Smaller technology footprint by managing only the resources and tools required
• Greater flexibility and capacity in supporting the deployment

SOLUTION IMPLEMENTED

The solution involved implementing a digital model lifecycle leveraging Cloud technology and a suite of applications to manage and monitor model development. This included the development of an execution environment that would serve for model CI/CD, while incorporating a best practice architecture as well as security and data services.

BUSINESS BENEFITS REALISED

The benefits to be gained were numerous, however these were the benefits immediately identified:

• 50% decrease in model development time
• 25% decrease in IT and Business costs
• Easier Python code deployment given the modelled risk management use cases
• Standardisation across the governed model deployment lifecycle 

THE CHALLENGE

The business was experiencing:

• Sophisticated fraud attacks
• The impact of limited staffing capacity for fraud investigations
• Unprecedented speed and velocity of same day attacks
• The effect of detection on the customer experience
• Rare events that created significant losses on the organisation

BUSINESS OBJECTIVES

The goal was to partner with a first-in-class vendor that would implement a platform to:

• Proactively identify irregularities in the banking cycles
• Manage banking fraud risks more intelligently
• Enhance the systems while implementing a platform-centric approach
• Improve the data integrity and data services

SOLUTION IMPLEMENTED

The solution implemented was a platform and suite of applications for Fraud Detection & Investigation. This enabled a converged solution resulting in a common reference architecture paired with a set of interfaces, an advanced analytics engine that would leverage AI & ML capability and real-time detection engines, all underpinned by an orchestrated data layer that supports different processing utilities. This was deployed in the Cloud thereby leveraging  and enabling the business’ digital strategy.

BUSINESS BENEFITS REALISED

The business was able to witness immediate benefits through:

• The consolidation of data in providing a 360-degree customer view
• 60% increase in positive detection rates
• Yielded 4x improvement in scenario productivity