RiskBusiness is the leading supplier of augmented governance, risk, audit and compliance solutions to the financial services sector, with the largest share of the data interchange consortium platform market.
RiskBusiness led the adoption of hosted Software-as-a-Service (SaaS) governance, risk, audit and compliance solutions, incorporating an unique data protection and encryption methodology, delivered through annual subscription. RiskBusiness’ risk content and risk intelligence subscription services are supported by risk advisory services, recognised as subject leading in accountability, three lines of defence, non-regulated financial services and data classification taxonomies.
Based in Birmingham in the United Kingdom’s Midlands, RiskBusiness is an equal opportunity employer aligned to environmental, social and governance principles of sustainability and protection.
Problem: Governance, risk management, audit and compliance has to add value to the business, not be a reactive or veto function stopping the business doing what it is tasked to do.
Solution: Combine active machine learning language models with intellectual capability coupled to harvesting internal and external unstructured data to deliver the most accurate, unambiguous, relevant and current risk and business intelligence, focussed on facilitating proactive integrated informed decision making, risk awareness and comparative assessment across the enterprise.
Strategy: Leverage risk content with deep machine learning, concept search and language modelling technology capability to deliver a new, proactive and real-time risk intelligence platform.
RiskBusiness is creating an uncontested market space for predictive analytics and risk intelligence in the global financial services industry, designed to service executive management, front-line business and the audit, governance, risk and compliance functions. By applying our unique supervised deep learning approach, coupled to our proven taxonomical structures, vast arrays of internal and external, structured and unstructured, seemingly unlinked data sets, can be translated using machine learning language models to deliver real-time industry comparable assessment to enable informed risk decision making.