How to Use Machine Learning for Third-Party Risk Management

Machine learning (ML) is defined as “the capability of a machine to imitate intelligent human behavior.” This artificial intelligence (AI) technology is often embedded in reporting and analytics tools to make sense of large amounts of seemingly disconnected data. ML can have a significant impact on vendor risk management and supplier risk management as organizations look to better predict and mitigate potential cyber and physical risks.

This post describes seven ways to use ML in your third-party risk management (TPRM) program, and it examines common types of data to include in your analysis.

Seven Ways to Use Machine Learning Analytics for Third-Party Risk Management

ML analytics are transforming the way companies manage their vendors and supply chains. These technologies enable companies to better understand supply chain risks and opportunities, make more informed decisions, and respond quickly to potential disruptions. Below are some of the more well-known uses of ML and AI within supplier risk management.

1. Identifying Cybersecurity Risks from Patterns in Large Volumes of Data

Machine learning algorithms can analyze large volumes of data to identify patterns and anomalies that may indicate potential supply chain risks. For example, you can use ML algorithms to detect anomalies in network traffic, Dark Web chatter, and paste site activity to examine unusual patterns of data transfer, exposed data, or unauthorized access attempts. This can help you identify potential threats associated with vendors or suppliers who provide critical products and services to your company or have access to its systems.

2. Forecasting Supply Chain Disruptions Using Predictive Analytics

Machine learning algorithms can be used to predict future risks in the supply chain. For example, you can use predictive analytics to analyze supply chain data for potential risks, such as disruptions from natural disasters or political instability. Collecting, collating and analyzing a rich set of third-party data can help your company proactively predict and manage risks and take action to mitigate the impact of any disruptions. This data can include physical locations of providers, manufacturing location, geo-political events, shipping patterns, weather patterns, cyber events and more.

3. Analyzing Disparate Data Sources to Improve Supplier Selection Decisions

Machine learning algorithms can help your company make more informed decisions about its vendor management and supply chain strategies. For example, you can use ML algorithms to analyze a wide range of data sources to identify potential ESG risks associated with third-party vendors or suppliers. By analyzing news articles and social media posts, ML can identify suppliers with poor environmental records or those that have been involved in controversial social issues, which can influence your company's decisions to contract with those suppliers.

4. Automating Processes

Machine learning automate many of the tasks involved in vendor management and supply chain management. For example, you can use ML algorithms to automate supplier onboarding, supplier qualification, and third-party risk assessments.

5. Forecasting Demand

Your supply chain specialists can use ML and AI to predict demand for specific products or services, enabling them to adjust inventory levels and production schedules accordingly.

6. Predicting Equipment Failures

Your operations teams can use historical analysis and ML to predict equipment failures or maintenance requirements, enabling supply chain managers to address issues before they become major problems.

7. Analyzing for Fraud

You can use machine learning algorithms to identify suspicious patterns or anomalies in financial data, enabling procurement teams to detect and prevent fraud.