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The important thing to note while implementing an analytics-based risk management system is that one size does not fit all. Risk management is subjective and must be comprehensive enough to cover all business functions. Moreover, it is not ideal to have an ad hoc approach to risk management; what works best is a systemized, priority-driven approach. This priority must be based on the probability of risk event backed by predictive modelling based on big data analytics.
The DNA of an ideal risk management program is a common risk platform developed and integrated with reliable shared data. Here exposure is managed using analytical modeling to forecast risk in multiple scenarios.
What big data does risk management is that is erases the dependence on traditional trending analyses. This backward-looking position has changed thanks to big data analytics and it is now easier to determine outcomes or even predict occurrences using identified data patterns. This helps businesses navigate quickly through crisis and arrive at decisions faster and surer.
The use of data analytics to analyze, measure, model, and predict risk is a growing capability among leading enterprises. These new tools can add a unique advantage in avoiding, identifying, and responding to risk. The “sense and respond” attitude has fast become outdated with the advent of big data and analytics.
Interestingly, predictive risk analysis and management is the means to the end, and not the end itself. This approach is the protection against operational risk events, and not a Band-Aid solution. The idea is not as simplistic determining “acceptable risk” and mitigating it; it is far more complex and evolved. Here, “predict and act” is the strategy, and analytics renders tactical advantage.
The use of data analytics to analyze, measure, model and predict risk is a growing capability among leading enterprises.
As a sophisticated advantage, risk management systems are flexible enough to integrate current market trends and risk regulations and help respond to future consequences.
Having said that, it is ultimately data that transforms into greater decision-making insight.
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