Risk prediction models: How they work and their benefits

What is a risk prediction model?

Risk prediction models use statistical analysis techniques and machine learning algorithms to find patterns in data sets that relate to different types of business risks. In doing so, they enable data-based decisions optimized for particular risks and business opportunities as part of risk management initiatives. AI increasingly plays a role here too.

In the case of the clothing retailer, a risk prediction model can analyze past sales data, customer demographics, market trends and other variables to forecast sales by product. The model assesses the risk of understocking or overstocking specific items, accounting for uncertainty and providing probabilities of different outcomes.

This kind of sales forecasting model doesn't specify what to order. Instead, buyers can see which items have a high risk of excess inventory. They can then adjust their purchasing plan accordingly to mitigate that risk. Mitigation doesn't always mean ordering fewer goods. Instead, a retailer might consider upfront contingency measures, such as a discounting plan or a reseller contract for potential overstocked goods. Increasingly, businesses that have adopted circular economy practices repurpose unsold items in other ways.

But all these strategies become more effective with a risk prediction model providing advance insight to likely outcomes and potential risks.

Industry use cases for risk prediction models

Risk prediction models are used across many business scenarios and industries, spanning both physical and digital domains. In addition to retail, the following are other applications for them:

• Credit risk models predict the risk of customer loan defaults, helping banks set credit limits. Banks and other financial services firms also use risk models for fraud detection, portfolio risk analysis and anti-money laundering efforts.
• Churn models forecast the risk of customer attrition. Telecommunications companies use these to improve retention offers and calling plans.
• Actuarial models in insurance assess risk factors for claims so policies are properly priced.
• Clinical risk models in healthcare analyze patient data to identify people who are prone to hospital readmission or potential disease complications, which guides interventions.
• Risk models for public health threats, environmental events and geopolitical instability are widely used by government agencies.
• Cybersecurity is a growing concern for every organization. Risk prediction systems can detect anomalies and identify security threats before attacks occur.
• Disruption risk analysis for events like material shortages or natural disasters has become critical for supply chain managers -- for example, to account for ships getting stuck in the Suez Canal.

Business benefits of effective risk prediction models

In addition to helping businesses understand and manage risk in their decision-making, effective risk prediction models can provide several other benefits:

• Fraud prediction. This helps banks, credit card companies and other businesses preemptively detect and halt unauthorized transactions, avoiding financial losses.
• Predictive maintenance. With early insight into the risk of equipment failures, companies can catch issues before they require expensive repairs. Doing so optimizes maintenance spending, prevents disruptive downtime, and ensures business continuity and workplace safety.
• Increased customer satisfaction. Effective risk management also prevents problems that could affect how customers view a company. Improving satisfaction levels reduces customer churn and the need for costly customer acquisition campaigns.
• Enhanced customer trust. Risk prediction models also help businesses build trust with customers. It isn't only equipment that can be proactively managed. Predicting customer needs or potential issues lets businesses address concerns before they become problems -- a forward-thinking approach that builds customer confidence in a company.
• Better patient care. In healthcare, risk models can identify patients who will benefit most from preventive care and other actions that improve patient outcomes.
• More agile risk management processes. With models continuously monitoring for business risks, organizations can respond faster to emerging threats and changing market conditions. This builds better business resilience.

Risk prediction models can't solve every business problem. But they're effective in many business planning and management scenarios that involve decisions with inherent risk.

How risk prediction models work

To better understand how predictive risk management can best serve an organization based on its specific needs, let's look at how these models work. The following are some common techniques for developing risk prediction models:

• Logistic regression models. They're often used when the outcome of interest in a risk modeling project is binary. For example, a logistic regression model can predict whether or not loans will default based on factors such as income, credit score and loan amount. The result will be a risk score of the likely outcome for individual loans. Logistic regression is fast and effective with very large data sets.
• Decision tree models. These models use a tree-like graph of decisions and potential outcomes. They make predictions by navigating through the tree based on input variables, allowing for an intuitive and visual understanding of complex processes. Decision trees are commonly used in customer segmentation and fraud detection.
• Support vector machines. SVMs, as they're commonly known, are not mechanical devices. Rather, an SVM is a classification algorithm that divides data into distinct categories, such as high-risk and low-risk customers. The process is similar to logistic regression, but if there are many customer attributes in the data, SVMs can handle the complexity better. On the other hand, SVMs focus on the classification aspect, not on providing probabilities for the outcomes. As a result, a logistic regression model might be easier to understand and interpret; for many risk modeling scenarios, that's important to build trust in the process.

Organizations can also now look to newer AI techniques. Neural networks are a type of deep learning algorithm inspired by the human brain rather than statistical techniques and commonly used in AI applications. Neural networks recognize complex patterns in data -- where even skilled data scientists might not fully understand the underlying relationships between the variables.

Another advantage of neural networks is they can be trained on large amounts of data, which is especially useful for risk prediction initiatives with a lot of historical data available. However, these models can also be computationally expensive to train, hard to interpret and difficult to explain to business executives.

Generative AI may have a role to play in risk prediction too. It potentially can improve the performance of neural networks for risk prediction. For example, generative AI can be used to create synthetic data comparable to the real-world data a neural network will encounter. This can help the neural network identify patterns in data more accurately, especially if you don't have large data sets.

Companies are exploring other AI and machine learning techniques, such as reinforcement learning and natural language processing (NLP), for predicting and managing risk. For example, reinforcement learning, which improves machine learning models by trial and error, can be used to train AI agents to make decisions that minimize risk. NLP is a type of AI that understands and processes human language. It can be used to extract and classify information from text data, such as customer feedback forms or social network posts, that might be relevant to risk prediction.

Best practices for developing a risk prediction model

Risk prediction models can be difficult to implement in practice. Creating an effective risk prediction model takes careful planning and execution. Here's some high-level guidance on best practices and what to look out for in the model development and deployment process:

• Understand the data and ensure it's clean. High-quality data is the foundation of accurate models. Relevant data sets should be identified and preprocessed to address missing values, duplicates, inconsistencies and other data quality issues. To help with the identification step, business subject matter experts can provide advice on useful data sources and fields based on key risk factors.
• Choose the right model. Different modeling techniques are suited to specific risks an organization wants to predict. Choosing which technique to use is not just about model performance and accuracy but also flexibility and ease of understanding the results generated by the model.
• Avoid bias and ensure interpretability in models. As AI-driven models become more prevalent, ensuring transparency and fairness will become more crucial. Data scientists should check for hidden AI biases that could skew risk predictions. Prioritizing models that are easily interpretable also builds trust and accountability with business stakeholders.
• Make compliance a priority. In many cases, risk prediction models must adhere to regulations governing data privacy, fair lending, employment practices and other aspects of business operations. Close collaboration with legal teams may be needed to maintain regulatory compliance as you develop risk models. Also consider industry codes of conduct and internal rules on the use of data.

In addition to these best practices, bear in mind that risks evolve. To keep up with that, continuously monitor models in production use, test their ongoing relevance and retrain them on new data as needed. Some businesses use dedicated model monitoring systems to check for deteriorating performance over time. Others simply retrain their models on a regular schedule.

Getting started with risk prediction models

When developed and used properly, risk prediction models are powerful tools that complement organizational knowledge and gut instinct with algorithmic forecasts. Risk managers and business leaders can use them to quantify the once unquantifiable. Despite some technical challenges, predictive risk modeling and management need not be a dive into the abyss. Start small on model development and validation with the following steps:

1. Identify a business process prone to uncertainty and potential risks, such as sales forecasting, equipment maintenance or customer retention.
2. Audit existing data related to that process and its associated risks to ensure you have good quality inputs to work with in the modeling process.
3. Read available case studies from peer companies, risk management software providers and data science platform vendors to see what has worked elsewhere.
4. Build a basic prototype model as a pilot project, with an emphasis on transparency, ethics and trust. Performance and accuracy can be improved over time, but values and principles are difficult to retrofit on a model later.
5. Use insights generated by the model to optimize risk-related business decisions and processes on an experimental basis at first before starting to rely on it more fully. Even then, keep human oversight of the predicted risks as a critical check in your risk modeling methodology.
6. Adopt a mindset of continuous model improvement. Risk prediction models require ongoing maintenance, tuning and governance throughout their lifecycle.

Whatever business a company is in, it's already managing risk. It may simply do so with experience and intuition rather than data and repeatable processes. Risk prediction models add a new tool to an organization's risk management portfolio -- a powerful and practical one to complement rather than fully replace its own sense of what lies ahead.