What are the best ways to identify trends and patterns in operational risk data?

1 Use a risk taxonomy

A risk taxonomy is a hierarchical classification of the sources, categories, and subcategories of operational risk. It helps you to organize your operational risk data into meaningful and consistent groups that reflect your business objectives, processes, and controls. By using a risk taxonomy, you can easily compare and aggregate your operational risk data across different units, functions, and regions, and identify the most prevalent and significant risk types, drivers, and scenarios.

2 Apply descriptive statistics

Descriptive statistics are numerical measures that summarize and display the main features of your operational risk data, such as the mean, median, mode, standard deviation, frequency, and distribution. They help you to understand the central tendency, variability, and shape of your operational risk data, and identify any outliers, anomalies, or clusters that may indicate unusual or abnormal risk events. By applying descriptive statistics, you can gain a quick and simple overview of your operational risk data and spot any trends or patterns that require further investigation.

3 Perform trend analysis

Trend analysis is a technique that examines how your operational risk data changes over time, such as monthly, quarterly, or annually. It helps you to identify the direction, magnitude, and rate of change of your operational risk data, and evaluate the effectiveness of your risk management actions and strategies. By performing trend analysis, you can detect any increasing or decreasing trends in your operational risk frequency, severity, or exposure, and anticipate any potential future risks or opportunities.

4 Conduct root cause analysis

Root cause analysis is a method that investigates the underlying factors and reasons that contribute to your operational risk events. It helps you to identify the causal relationships between your operational risk data and the internal or external factors that influence them, such as human errors, system failures, process inefficiencies, or environmental changes. By conducting root cause analysis, you can discover the hidden or latent causes of your operational risk events, and develop effective corrective and preventive actions to reduce or eliminate them.

5 Use data visualization

Data visualization is a process that transforms your operational risk data into graphical or pictorial representations, such as charts, graphs, maps, or dashboards. It helps you to communicate and present your operational risk data in a clear and engaging way, and highlight the key insights and findings that may otherwise be overlooked or obscured by numbers and text. By using data visualization, you can enhance your understanding and interpretation of your operational risk data, and identify the trends and patterns that matter most to your organization.

6 Apply machine learning

Machine learning is a branch of artificial intelligence that uses algorithms and models to learn from your operational risk data and make predictions or recommendations based on them. It helps you to analyze large and complex operational risk data sets that may be difficult or impossible to process manually, and uncover hidden or complex patterns and relationships that may not be apparent or intuitive. By applying machine learning, you can improve your operational risk data quality and accuracy, and generate new and valuable insights and solutions for your operational risk management.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?