Risk and compliance management includes control at the business level, compliance and risk functions to review the work of first-line, and audit of work by the second line of defense. It offers a single channel of regulatory content, enables a collaborative process, recognizes changes to regulatory content, facilitates pre-population of content, delivers best practice guidelines and change management.
The use of AI in GRC aids financial services firms in creating organizational competencies, working on PoCs/Pilots, and consulting their regulatory partners to adopt AI successfully. The predictive capabilities of AI help organizations proactively comply with their strategies.
Rubiscape’s mission is to help firms de-risk their business cost-effectively and accurately and create an integration that makes compliance seamless, repeatable, and more scalable.
Lenders can decide the creditworthiness of borrowers by examining datasets supplied by their digital footprint, especially borrowers with little or no credit history.
AI can help manage risks and opportunities based on more advanced factors as compared to upper limits for risk appetite, estimations, and responses.
Using past data, risk managers decide on resource allocation. Machine learning helps them focus efforts on locations that need more attention.
Risk managers alter input data to find the impact on predicted outcomes. GRC professionals explore a multitude of models, make predictions, and continue to repeat and refine them.
ML removes risk-scoring subjectivity by using a model to determine data-driven risk scores which avoids the manual and human risk scoring process.
AI makes better use of complementary technologies like robotic process automation to improve processes, refine computer-based decisions, and improve algorithms.