Decisioning engines for lending and other banking use cases have traditionally been focused on analytic modeling of data to determine coefficients and entering that information into rules-based decisioning systems. With cloud deployment, platforms, and composability growing in banking technology, financial institutions can augment traditional decisioning systems and processes with in process, real time integration to AI-driven analytic models to add predictive lift to loan decisioning.

Intelligent decisioning platforms:

·Increase decisioning flexibility and speed to create and change products fast in response to competition and market changes (cloud, componentization, real time).

·Optimize decisioning by being able to integrate new data sources, analytic methods, and models. This also enhances product cross-sell and bundling capability.

·Reduce technical debt and achieve digital transformation by 1) replacing older systems, 2) migrating loan decisioning systems from on-premise to cloud deployment, and 3) consolidating single product systems onto an enterprise platform.

·Improve credit, fraud, collateral, and operational risk management, increase predictive lift of credit decisions, capture more fraud attempts, improve loan pricing, and reduce loan delinquencies and foreclosures.