Risk Prediction Models: How They Work and Their Benefits | TechTarget

What is a risk prediction model?

Risk prediction models use statistical analysis techniques and machine learning algorithms to find patterns in data sets related to different types of business risks. AI increasingly plays a role in their development, too. The models enable organizations in various industries to make data-based decisions about particular risks and business opportunities as part of risk management initiatives.

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 business uncertainty and calculating the 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, the 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 into likely outcomes and potential risks.

Industry use cases for risk prediction models

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

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 the following 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 as well as workplace safety.
• Increased customer satisfaction. Effective risk management 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 proactively manage customer relationships. Predicting customer needs or potential issues lets organizations 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 potential business risks, organizations can respond faster to emerging threats and changing market conditions. This increased agility 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

Often used when the outcome of a risk modeling project is binary, logistic regression is fast and effective with very large data sets. 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, generating a risk score of the likely outcome for individual loans.

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

An SVM isn’t a mechanical device; rather, it’s a classification algorithm that divides data into distinct categories, such as high-risk and low-risk customers. While the process is similar to logistic regression, SVMs can handle complex data sets — for example, ones involving many customer attributes — more effectively. 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, and for many risk-modeling scenarios, that’s important for building trust in the process.

Cox proportional hazards models

This specialized class of survival analysis models is particularly valuable for predicting time-to-event outcomes, such as patient survival rates, equipment failure timing or customer churn periods. Cox models estimate how various risk factors affect the hazard rate — i.e., the probability of an event occurring at any given time. They’re widely used in medical research for predicting disease progression, in finance for credit risk assessment over time, and in manufacturing for reliability analysis.

Accelerated failure time models

While Cox models predict relative risk, AFT models directly predict actual time-to-event, making them valuable for business planning and resource allocation. Instead of saying, “Customer A has a 50% higher churn risk than Customer B,” an AFT model might predict that Customer A will churn in eight months, while Customer B will churn in 12 months. This information is often more actionable for business executives planning marketing interventions, maintenance schedules or inventory management. AFT models are also used in engineering to predict equipment lifespans and optimal maintenance schedules.

AI’s growing role in predictive risk modeling

Organizations can now incorporate AI into risk management applications, including the use of newer AI techniques to create risk prediction models. Neural networks are a type of deep learning algorithm inspired by the human brain rather than statistical techniques. Commonly used in AI applications, they 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 modeling 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.

Nonetheless, the combination of a type of neural network called a transformer model with large language models (LLMs) is revolutionizing risk prediction by bringing advanced NLP capabilities to the risk assessment process. Transformer models and LLMs that use them can analyze unstructured text data from sources like news articles, social media posts, regulatory filings and customer communications to identify emerging risks. These models excel at understanding context, handling multiple languages and processing textual information that traditional statistical models can’t easily incorporate.

Generative AI (GenAI) applications in risk prediction include scenario generation for stress testing models, creation of synthetic data sets for modeling rare events, and writing explanatory narratives for risk model outputs to improve stakeholder understanding. For example, GenAI tools can simulate thousands of potential risk events for scenario analysis in climate risk modeling; create realistic customer data for fraud detection model training that preserves privacy; and explain complex risk scores for regulatory compliance filings and customer communications.

In addition, AI agents and agentic AI systems with predictive capabilities are emerging as sophisticated tools for autonomous risk monitoring and risk response. These systems can continuously monitor multiple data streams, automatically adjust risk parameters based on changing conditions and take preventive actions within predefined parameters. For instance, an AI agent might automatically adjust credit limits when it detects changing customer behavior patterns or immediately flag unusual trading activities for further investigation. Reinforcement learning, which improves machine learning models by trial and error, can be used to train AI agents to make such decisions.

Best practices for developing a risk prediction model

Risk prediction models can be difficult to implement in practice. Creating an effective 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 the specific risks an organization wants to predict. Choosing which technique to use is not just about model performance and accuracy but also flexibility and the ability to easily understand 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 might 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 modeling best practices, bear in mind that risks evolve. To keep up, continuously monitor models, 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 high-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 business values and principles are difficult to retrofit into 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. However, it might 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 augment rather than fully replace its own sense of what lies ahead.

Editor’s note: This article was updated in July 2025 for timeliness and to add new information.

Donald Farmer is a data strategist with 30-plus years of experience, including as a product team leader at Microsoft and Qlik. He advises global clients on data, analytics, AI and innovation strategy, with expertise spanning from tech giants to startups.