Part 3 of a 6-part blog series.
In order to eliminate the risks and losses that come with organizational challenges, you first have to spot them coming. While this may sound like white magic, it’s possible today with the power of predictive risk analytics.
Predictive analytics is revolutionizing traditional risk assessment by giving us a data-driven structure for better and more accurate fortune-telling capabilities. By assessing and transforming risk drivers, themes, and behaviors into future insights, we can stop threats dead in their tracks and flip potential catastrophes on their head.
What are Risk Analytics for Associations?
Traditional risk assessment methods for associations are often complex, subjective, and difficult to automate. These traditional (and oftentimes manual) approaches to assessing risk include surveys, workshops, and monitoring industry trends.
But newer predictive analytics approaches are fully automated and provide your association and its board with organization-wide risk awareness (and associated risk assessment) for strategic planning and preventative purposes.
Ranking risks gives you a weighted risk assessment based on likelihood and impact scales. Once you have this insight into the likelihood of a disaster, you can then move forward with your predictive risk management processes (coupled with some good ole’ fashion human interaction) to prevent it.
In fact, the goal of predictive risk analytics is to assess and estimate with a high degree of certainty the operations that ARE and ARE NOT conducive to the success of your association so you can then build decision-making support systems.
Conducting risk assessment with predictive analytics also allows you to prove at any point in time that you’re meeting the expectations of regulators, examiners, and board of directors, as well as your members, investors, team members, and communities.
You should also link risks to activities to prioritize what activities need to be monitored. This prevents emerging threats from harming overall organizational metrics and KPIs, and creates an organization-wide culture around risk-aware and risk-conscious decision making. And culture eats strategy for breakfast.
What areas can be supported by predictive risk analytics?
Membership is the foundation of your association. You can use big risk analytics to automically gauge waning membership numbers and respond by bolstering member experience and targeting.
Systemic operational threats
Using predictive analytics in operational risk management is useful for identifying and addressing threats related to data governance, compliance, efficiency, operational losses, and capital impacts.
Brand and image management
You can use risk analytics to empower your organization and its reputation, align strategy and culture, and keep tabs on member and stakeholder confidence. For example, predictive risk intelligence allows you to scan social media and behavioral patterns. You can then use this arsenal of data to map social and industry changes as well as the reasons behind a potential or growing problem to take charge of shifting scenarios.
Using predictive analytics in financial risk management ties in with forecasting. For example, you can use risk analytics to identify and address limited resources by prioritizing resource-building campaigns and initiatives.
You can even use predictive risk analysis and monitoring to detect a natural or manmade hazard that may have negative humanitarian consequences.
Using predictive algorithms, you can determine the likelihood and impact of a hazard in a defined period and the risk it presents. This will allow you to circumvent the hazard by implementing protective structures and other defenses.
Image Source: UNHCR
Here are the different humanitarian-related threat categories:
- Hydro-meteorological (floods, landslides, storms, droughts) and geophysical (earthquakes, volcanic eruptions, tsunamis) hazards.
- Armed conflicts and civil unrest.
- Epidemics and pandemics like Covid-19.
- Drastic changes in the socio-economic environment (e.g. surges in prices of essential goods, trade bans and restrictive legislation, and human rights violations).
- Environmental hazards (industrial accidents, severe pollution).
What data can we analyze to mitigate risk?
Socio-demographic factors: gender, age, position, education, marital status
Program details: member engagement, interactions, attendance, feedback
Giving behaviors: changes in amounts, seasonal trends, donor feedback
- Number of risks identified (per project, program, process and/or organization-wide)
- Percentage of key risks monitored
- Number of risks that occurred (i.e. became issues)
- Number of risks that occurred more than once
- Predicted risk severity compared to actual severity
- Number of risks that were not identified
- Percentage of key risks mitigated
- Cost of risk assessment
The Wrap Up
Predictive risk analytics is a process of using data mining, machine learning, and artificial intelligence to make predictions for the future and provide actionable insights on what would happen under given circumstances.
Creating a culture of risk-sensitive decision making using predictive risk intelligence safeguards your association against imminent threats and helps you stay a step ahead of potential challenges.
Want to learn more about how you can assess and minimize risks before they happen? Read The Association Retention Playbook: The 5-Step DIY Membership Retention Strategy and Workbook for more actionable information your association can start using today.