Risk Mitigation Partners offers complete data transparency for risk management, finance, regulatory reporting and performance, taking multiple, disparate data sources into an integrated centralized data architecture.

Regulatory requirements and the financial risk imperative are driving the need for a consolidated view across all businesses and across all types of risk, including market, credit and liquidity risk. At the same time, demand for this information is expanding beyond the traditional domain of the chief risk officer. Increasingly, compliance officers and finance departments want access to this data, to drive their own analyses.

The data sets required to meet these new requirements vary between functions, businesses and financial instruments, and by geography and regulatory environment. The result is a highly complex data requirement.

Adding to this complexity is the fact that firms need to use multiple data sources, data types and data formats, and different delivery timeframes to meet the business, risk and regulatory requirements they face. Different areas of the business may need access for the same underlying data – whether it’s the description of an economic event, or details of a trade, deposit, loan, derivative security or service guarantee – but for different purposes. Furthermore, different business areas often use different definitions for similar data.

Any data structure needs to deliver the reports and analytical tools required by a range of internal recipients. Each firm will have its own unique requirements in terms of the data and tools needed to support improved insight and analysis, and deliver change.

At a high level, Risk Mitigation Partners offers complete data transparency for risk management, finance and regulatory reporting, taking multiple, disparate data sources into integrated, centralized data architecture. Creating data consistency across the operations allows firms to yield efficiency benefits from the data itself.

The Risk Mitigation Partners data architecture applies a set of complex business rules that transform the existing information from internal systems, which collectively is unstructured, into new information of business value.

• The data transformation process is closely monitored by tracking tools, continuously measuring and recording information about each data event.
• Data is further validated and reconciled through user defined rules, improving the quality necessary to confidently reach business decisions.
• An end-to-end auditing capability from event to disclosure offers full insight into the data flow, the manipulation and transformation of data as well as the event origin and data target.

By standardizing data sets and setting harmonized common vocabularies across different business processes, firms can begin to break down the silos, whether geographical, functional or defined by asset class.