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Identify emerging threats with predictive intelligence

What’s your type?

​When it comes to sensing and discovering emerging threats, what risk monitoring type—reactive, integrated, or predictive—are you? Learn how implementing a predictive intelligence program can help your organization better prepare for and manage risk.

Why aren’t organizations better at identifying emerging risks?

​Many organizations don't have enough time, tools, and information to anticipate significant emerging risks. Yet C-suite executives are expected to manage emerging risks every day—and to provide accurate and confident insights to stakeholders on what went wrong, what's happening now, and what could go wrong. Sounds like there could be a disconnect.

The traditional approach to risk management, which relies on risk indicators and management programs, can't keep pace. An advanced analytics approach could help—it can enable organizations to stay ahead of current risks and improve how they respond to and manage emerging risks.

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What are some specific benefits of predictive intelligence for your organization?

Predictive intelligence goes beyond traditional risk management. It uses analytical tools to monitor and capture emerging risks—that is often the missing piece. Many organizations monitor risks without harnessing advanced analytics methods. But when they supplement a traditional intelligence program with predictive intelligence, they stand to gain advanced warning and increased awareness of external threats. This can help stakeholders improve how they manage and respond to emerging risks.

Predictive intelligence can also help solve complex challenges across industries, such as:

  • Industrial technology and operational failures. For automotive, retail, distribution, and technology companies, predictive intelligence can provide actionable risk information on critical infrastructure components and products.
  • Early warning of potential employee misconduct. Within the financial services industry, intelligence on changes in employee behaviors could indicate potential conduct lapses, changes in employee sentiment, and policy breaches.
  • Alerts and warnings on workplace safety incidents. Power and utilities organizations can apply predictive intelligence to data on safety breaches, maintenance information, and more to reduce unexpected and adverse outcomes.
  • Complex outsourcing and vendor management. Life sciences companies can draw from external data sources, such as news, government publications, and publicized incident reports, to provide visibility into third-party operations that often lack transparency.

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What are some strategies for monitoring risk?

Three different types of risk monitoring occur throughout the risk management lifecycle. They each serve a different purpose in the process:

  • Reactive risk monitoring. This is the initial monitoring mechanism where the organization tracks and reports loss events after they happen. Central to this technique is the ability to respond post-event with a remediation plan and prevent the recurrence of similar events.
  • Integrated risk monitoring. The next stage of monitoring uses passive and active risk, performance, compliance, and control indicators to objectively report on risk performance thresholds periodically or in near real-time.
  • Predictive risk monitoring. This technique can help organizations discover potential risks and threats, including types of risk not covered by existing risk indicators. Predictive intelligence helps modernize an established risk management framework, taking it from periodic risk reporting to real- and near real-time risk reporting.

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What is the process for implementing a predictive intelligence approach?

Predictive intelligence can help turn risk, controls, and performance information into preventative and actionable insights. But before predictive intelligence can be put into action, organizations should carve out a plan that starts with identifying the types of risk and where they are coming from.

  • Define predictive intelligence scope. Management and risk governance teams identify prioritized risk events to better track and monitor on a continual basis.
  • Identify precursors of risk events. Each risk identified within scope is analyzed to identify indicators or incidents that precede risk events and provide reliable indication of an event occurrence.
  • Identify data sources. Each risk event precursor is prioritized and mapped to internal and external data sources, which can supply the baseline data for analysis and predictive modeling.
  • Develop static and self-learning predictive algorithms. Through combined analysis of internal and external precursor information, a predictive analytics algorithm is selected and applied to predict or detect the heightened occurrence and likelihood of a risk event.
  • Initiate predictive intelligence generation. Risk governance functions start collecting the baseline data for each risk category and apply risk predictive algorithms to generate alerts and notifications. Results are reported and continually evaluated.

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Can a predictive intelligence program be established in your organization?

With the right strategy in place, organizations that make predictive intelligence a priority are better positioned to modernize their monitoring processes both tactically and strategically. Here are three considerations for organizations initiating a predictive intelligence program:

  • Start smart. Initiate a predictive risk program by identifying risks that have high impact, available performance data, low forecasting complexity, and insufficient current capability.
  • Scale fast. As the proof of concept matures, expand predictive intelligence use cases to other areas of the organization.
  • Execute effectively. Establish performance measures and review predictive intelligence monitoring outcomes to determine how to integrate predictive intelligence more fully into the current portfolio of risk management activities.

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