By Patrick O’Neill, Founder and President, Redhand Advisors
Today more than ever businesses must address increasingly complex risks that can impact their operations and bottom line. Many are turning to artificial intelligence (AI) to help them. AI can empower companies to identify, assess, and mitigate risks in real-time, but to effectively harness this tool, your system must have the right data first.
Here are eight ways to use AI in your risk management strategy.
1. Risk assessment
Once the resources and governance structures are in place, organizations can start developing AI models for risk assessment. These models leverage machine learning (ML) algorithms to analyze vast amounts of data and identify patterns, trends, and anomalies that indicate potential risks.
Organizations that continuously train and refine these models can enhance the accuracy and effectiveness of their risk assessment capabilities, enabling them to make proactive decisions and take preemptive action. This provides a wholistic view of a business’ risks, enabling you to determine which risk management strategies to focus on. Using AI allows your organization to efficiently compile data and interpret it more effectively.
2. Risk monitoring
AI-powered risk management also enables organizations to enhance their risk monitoring and early warning systems to detect emerging risks in real-time. By leveraging AI technologies, organizations can automatically monitor various data sources to identify potential risks as they emerge. This proactive approach empowers companies to take immediate action and mitigate risks before they escalate.
Case in point: AI can monitor social media, news sources, and market trends to assess and predict reputation risks for companies, allowing organizations to respond in a timely manner to potential PR nightmares.
3. Automate risk mitigation processes
AI can automate risk mitigation processes to streamline and accelerate response times. By leveraging AI-powered automation, organizations can implement intelligent workflows that automatically trigger risk mitigation actions based on predefined rules and thresholds. This eliminates manual intervention, reduces response times, and ensures consistent and efficient risk management practices across the organization.
Case in point: Let’s say a claim is filed that automatically triggers the need for a safety audit. In lieu of waiting months to organize the audit to determine the cause of the accident, AI can help assess who should perform the audit (an employee, or the automated system). In this scenario, AI is starting a process based on criteria established and pre-programmed, efficiently moving the audit along to not only determine the cause of the accident but to resolve the claim faster.
4. Claims management
AI has the ability to process and analyze information more quickly than humans, allowing organizations to quickly pinpoint trigger data that can result in:
- Faster claims processing times
- Improved triage of potential risks
- Enhanced predictive analytics to project the severity of a claim
- Auto adjudication of claims
- Automated fraud detection
- Identified potential subrogation opportunities
Cases in point:
- Fraud detection: AI models can analyze historical claims data to identify unusual patterns or anomalies. When a claim deviates from the norm in terms of its characteristics, cost, or timing, it can raise a red flag for further human investigation. Using AI to detect fraud in claims not only improves the accuracy of identifying fraudulent claims but also helps reduce false positives, optimizing the efficiency of claims management in the process.
NOTE: While AI can enhance fraud detection, human expertise and oversight are still essential to make the final determinations and decisions in complex cases. It’s the combination of AI and human judgment that creates a robust and effective fraud detection system to better manage claims.
- Subrogation of claims: The ability of AI to analyze claims data also can help insurers identify subrogation opportunities and recover costs by recognizing when another party is liable for a claim. AI can review claim notes, documents, and correspondence to identify relevant information about subrogation cases, including details about the incident and responsible parties.
NOTE: The key is to train the AI or ML to detect buzz words or particular language associated with subrogation. It can even scan and “read” notes from a police report that identifies who was at fault for an incident, for example, confirming your insured wasn’t at fault.
5. Operational risk management
There are operational risks associated with doing business that AI can help address. AI can be designed to detect anomalies in processes and provide early warnings, reducing the likelihood of operational failures. It also can analyze historical operational data to identify patterns, trends, and anomalies that may indicate potential risks. This includes data related to equipment performance, process failures, and employee behavior.
Case in point: Forecasting is an essential component of managing your operation’s risk. AI can help you analyze data and make decisions about what could happen in the future, revealing trends not only on claims made but on near miss data as well. Being able to see trends in near miss data helps reveal potential scenarios that could result in injury or data in the future.
Also consider how the Internet of Things (IoT) is revolutionizing building management. IoT can monitor an array of equipment and processes within an organization and collect and share data on an array of equipment, providing organizations with enhanced monitoring capabilities to detect when a device is not operating correctly and could potentially cause a loss.
For example, building management systems can include leak detection technology, which is designed to monitor water flowing through a building’s pipes, alerting building operators when a leak occurs; some can even shut off the water entirely. This allows organizations to address an issue before it turns into a claim.
6. Cybersecurity risk management
Organizations must implement robust security measures to protect sensitive data and ensure compliance with regulatory requirements. Cybersecurity risk management helps an organization protect their digital assets. AI can help identify and mitigate cybersecurity risks by monitoring network traffic, detecting vulnerabilities, and responding to threats in real-time. With AI, your system is working behind the scenes 24/7, helping your organization proactively address cyber threats versus scrambling to react when a breach is discovered.
Case in point: Cybersecurity monitoring tools are designed to continuously evaluate your internet and email traffic and identify threats before they occur. Organizations can establish controls to improve security in areas that were the subject of a breach and even prevent potential hackers from getting into your system in the first place.
For example, malware, denial-of-service attacks, and social engineering are three common cyberattacks that cybersecurity measures can help address. Cybersecurity monitoring tools can reveal trends in the types of incidents your organization is facing, allowing you the opportunity to speak with your insurer about adding additional cover limits if a cyberattack occurs.
7. Legal compliance
Identifying, preventing, and mitigating potential legal issues helps organizations better manage their risk. AI can assist in legal risk management by analyzing contracts, legal documents, and case law to identify potential legal risks and compliance issues.
Case in point: AI tools can “review” your contracts, identifying if a policyholder is under their policy limit requirements for a specific vendor, noting unfavorable terms within the contract, and even point out exclusions within a contract that would put a business at risk.
AI could even be used to divvy up the task of analyzing a contract, with AI evaluating specific aspects and the legal expert reviewing the more important details. This can help ensure that long contracts are properly reviewed and your organization is sufficiently covered.
8. Underwriting
By analyzing vast amounts of data, including customer data, claims history and more, AI-powered underwriting algorithms can identify risk factors and predict future claims, helping insurers make more accurate underwriting decisions. This allows insurers to price policies more accurately, reducing the risk of financial losses. This also helps ensure an organization has the right coverage, which can save organizations money in the long run.
Case in point: Historically, underwriting has been a manual process with limited data. Now, with the help of AI, underwriters can pull a vast amount of data to put into AI models to help flag items that could affect the underwriting process and potential premium costs. This helps identify potential losses or trends that haven’t been picked up historically but have the potential to affect loss exposure. This also can help an underwriter identify where an organization needs additional coverage or increased limits.
With AI, underwriters can assess broader industry data and determine how it can affect specific businesses and overall claims trends. Knowing the frequency of when a specific claim occurs can help an underwriter more accurately price claims and ensure an organization has the right amount of coverage as well.
Leverage AI to enhance risk management
The world is in the early stages of AI use, and the above are just a sampling of some of the early AI use cases to improve risk management practices. As AI continues to evolve and have access to the right data, additional opportunities to harness the power of AI will emerge.
To learn more about how AI can help your organization, schedule an inquiry call with Redhand Advisors today.