According to Eric Siegel, Author of ‘Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die’, Predictive analytics is the technology that learns from experience (data) to predict the future behaviour of individuals in order to drive better decisions. This certainly sums it up for sure!
Predictive analytics models are a combination of statistics and model techniques to predict future results. Based on current and past data, patterns are formed and examined to project the future results considering that the patterns will repeat.
Predictive analytics provides businesses with tools, techniques to forecast results. In the current times, availability of data is limitless and with change being constant and with smarter technology, business operations have become more complex and prone to risks. And with the rise of new and complex risks, the use of data analytics to mitigate these risks have become important. It uses internal and external data to identify, measure and mitigate risks.
According to a recent report, the global risk management market was valued at $12.6 billion in 2022, and is projected to reach $52 billion by 2032, and is growing at a CAGR of 15.4% from 2023 to 2032. (Ref.)
Predictive risk management solutions can be applied in almost all the industries and here are a few major ones: supply chain, healthcare, banking and finances, E-commerce. There are different kinds of predictive models that businesses can choose based on available data and requirements:
- Regression model predicts continuous variables, for example the number of days the stock will last based on the current demands.Classification models predict categorical variables, for example whether a machine is likely to fail or not.
- Time series model predicts future outcomes based on past data, for example the demand forecast.
Here’s how predictive analytics helps in effective risk management:
1. Risk identification – Predictive analytics helps is early detection of risks in business operations, business processes and markets. For businesses, risks can rise from internal or external factors, and these can be a potential threat to achieving business goals. By integrating internal and external risks, predictive analytics can identify potential risks.
2. Risk assessment – Predictive analytics helps organisations to measure and assess the risks. From operational to functional level, organisations should analyse the risk with the available data. This allows organizations to prioritize risks based on their severity and allocate resources effectively to mitigate them.
3. Response to risk – An effective risk response understands various available options and decides on the best available option with its consequences and impacts. Predictive analytics also tracks and monitors the effectiveness of the selection option.
4. Risk monitoring and reporting – Identification of key risk indicators for each of the risks and timely monitoring of the risk is more important today with the changing landscape of business. It is important to identify both lead and lag indicators and identify the most relevant data source for monitoring these. Relevant risk reporting can be achieved by integrating the risk management lifecycle on an integrated technology platform.
Predictive analytics is a powerful tool for risk management that organizations can use to anticipate, assess, and mitigate risks effectively across various business processes. By using data-driven insights and predictive models, organizations can enhance their risk management capabilities, protect their assets, and achieve success in an increasingly complex and uncertain business environment.
Need assistance with your risk management strategy? Get in touch with our team today. CRG Solutions delivers Intelligent Digital Workforce solutions to help our customers innovate, automate and standardize their business processes to ensure superior customer experience and improved process efficiencies.
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