Robot Thinking

AI and the Future of Risk Management

As technology continues to advance and evolve at rapid rates, many business entities are changing the way they operate. One area of technology that can vastly improve operations is automation.

Some people believe artificial intelligence and robotics will negatively impact the job market, putting humans out of work. However, the sole purpose of artificial intelligence and robotics is not to take jobs from humans—it’s to help organizations become more efficient, leaving humans to focus on specialized work. In this article, we will explore how artificial intelligence and robotics impact how workers’ compensation claims, CAT claims, and even claims fraud are analyzed.

What is Robotics, Artificial Intelligence, and Automation?

Robotics is the study of machines that perform duties traditionally completed by humans. Robots are actual physical machines while computer engineering, on the other hand, communicates to the robots how to perform these duties. Embedded in all this automation is the human factor. For the time being, even with increased automation, humans still must use code to write commands to communicate with the physical robots. This is where artificial intelligence comes into play.

Artificial Intelligence is created by humans using algorithms. These algorithms are coded using real-time data to make human-like decisions. Robots are the physical machines that use this human intelligence to perform actions. With every decision being made, the machine learns and applies this learning for future decision-making.

In recent years, a highly automated workflow process has been utilized by the risk management industry. The process is generally referred to as Robotic Process Automation (RPA). A high volume of data can be processed by RPA 24/7 with little human intervention. For example, RPA processes can quickly aggregate claims intake data and produce reports that were previously done manually by intake personnel. Businesses adopting RPA generally find a decent return on investment as it saves a substantial amount of work hours and offers a high level of accuracy. RPA is not considered artificial intelligence, however, because it is not designed to make independent decisions on behalf of humans.

What Will the Future Look Like?

AI can be very helpful in the insurance arena. Workers’ compensation claims, for example, can be seamlessly analyzed using AI. To achieve this, AI can search and assess bill lines, detect comorbidities, and make logical decisions on diagnosis costs. Anomalies can easily be highlighted to assess workers’ compensation claims and create reports for auditing purposes.

AI can also help detect catastrophic claims early. AI predictive models can looks at trends and help calculate the potential costs. These models will allow for detection of any abnormalities early, so change can happen sooner rather than later to get back on track. Predictive models can also detect whether a claim will be litigated. According to Clara Analytics, “We have been pretty successful in predicting future litigation status with a high level of accuracy (92%+) using day one data.” The combination of early detection and a plan to handle these at risk claims, can help reduce the involvement of attorneys and reduce costs.

Like workers’ compensation claims, the process of resolving property damage claims can also be improved using AI. Models and algorithms can also help detect claim fraud; these claims can easily be closed out, saving businesses time and money. Businesses can also save time using AI technology, particularly in periods of high demand, such as during or after natural disasters when the claim load increases significantly. At times like these, manual processing makes it difficult for employees to close out claims quickly. As AI technology is incorporated, these claims can be closed out faster, making claim loads more balanced.

Incorporating AI and machine learning can also help with district operations. For example, AI can help reduce chances of cyber security breaches, detecting threats and analyzing behavior and network traffic. Machine learning and AI go hand in hand, and as these technologies learn from every task or duty, they remember these items, “learn” them, and apply those learnings to the next task.

A Brief Glimpse

This brief look into the world of AI highlights the potential for automation and advancement in the coming years. As research and development continues, districts and industry professionals should explore opportunities to automate, utilizing these processes to help analyze and interpret information, streamline procedures, and produce optimal outcomes that can save time and money.