Carriers will find themselves facing another series of unprecedented challenges heading into 2021. S&P predicted in July that the U.S. P/C industry will see a 100.7 combined ratio in 2020, following two consecutive years where it was below 100. The COVID-19 pandemic continues to amplify these rising ratios.

As the pressure mounts to stunt the trend on this ratio, insurers will look to areas where they can control their own destiny on the expense side of their business to squeeze as much productivity and efficiency out of those dollars as possible. This means innovation—driven by data, automation and other leading-edge technologies—will be in the forefront to deliver on their promise of results.

Doing so will require carriers to become technology and data experts, and that shift is already happening. One way is through venture capital arms that fund new technology startups to tackle the problems they see in their business, like what Munich RE and EMC Insurance have done. Even brokers want in, with incubator programs like BrokerTech Ventures launched to build the next generation of technical solutions for their specific distribution challenges. Delivering on this shift will require carriers to understand how to master integrating this technology into their operations.

As technology becomes more commoditized and ubiquitous, it will be easier to harvest value from it. Data visualization tools like PowerBI and Tableau, for example, are allowing companies to create an organization-wide, data-informed culture, where anyone at the desk level can become an analyst. Many other technologies are trending in the same direction (robotic process automation), while others may never get there (blockchain). Let’s take a deeper dive into use cases for three top technologies that can help insurers bend their combined ratio curve in the near- and long-term future.

Robotic Process Automation (RPA)—It’s become the go-to technology for tackling back-office tasks, from submission entry, underwriting renewal prep, claims entry or others that are repetitive or mechanical. RPA increases efficiency and, when done properly, impacts business outcomes like customer experience.

But while almost every carrier has at least reviewed multiple RPA solutions, some have struggled with deployment. Taking an existing paper- or people-based process and slapping RPA on top of it doesn’t work. Success comes when you take a holistic, integration-based approach. Start by re-engineering the process to be automation ready; deploying the robots where the rote or mechanical steps exist; plugging in people where judgment remains necessary; and tracking data throughout to understand the impact and improvement opportunities and overall performance.

Reviewing how data is input into a process is important to make it automation ready. For example, if you want your RPA solution to complete a quote on a list of properties provided by a broker, consider how the broker sends you the information. If it’s in the body of an email, it can’t be automated. If it’s structured data in an Excel file, however, a robot can read the data, enter it into the system and provide you with a quote quickly. The goal is to develop a straight-through process, untouched by human hands.

Additionally, most firms that start to dabble in RPA will soon learn the integration of various technologies creates a successful implementation. Take, for example, the clearance and submission process. Documents are typically submitted by email and then need to be run through a data capture engine, which feeds your quoting engine. These steps require coordination from a workflow engine, which orchestrates a series of robots and external technologies. Deploying a robot is becoming easier, but deploying automation solutions requires a keen eye for integration.

Artificial Intelligence/Machine Learning (AI/ML)—Once reserved for only the largest insurers, AI and ML are becoming easier to implement but with varied impact. The most common use case for carriers is in risk selection for less complex exposures like small commercial BOP policies or workers compensation. AI/ML allow carriers to assess risk using submitted data, enrich that data with additional information from third-party sources to automate and streamline underwriting decisions, and quote the risk autonomously. Another common use case for ML is in claims adjudication, where ML algorithms use data to determine whether to pay specific claims.

In the future, these decisions taken by models will become more complex. Some companies are using ML and the Internet of Things (IoT) devices to do exactly that. A prime example is Tesla, which in 2019 announced its intention to start its own captive insurance program, a project fueled by data Tesla collects from its IoT devices—your car. Eventually, AI, ML and IoT will help carriers create various proactive risk management strategies and programs to predict, deter and mitigate risk in ways many didn’t think possible. Smart homes are a fertile ground for these innovations right now.

Blockchain—Currently, very few compelling blockchain uses cases exist in insurance. However, policy transactions—or contracts associated with a policy—are perfect for this technology. The secure nature of blockchain holds the potential promise of reducing or even eliminating E&O risk from the entire insurance value chain. The complexity of our industry, however, means real-world implementations—and the network effect required for broad adoption—are far in the future.

While disruptive technologies like blockchain may be 10 years away from changing insurance, RPA, AI and ML will continue to bring near-term change. For example, take the process of requesting an endorsement. Right now, it’s 100 percent people-based. A broker sends an email with the request. The carrier receives the email. Someone evaluates the request. Another person requotes it, calculates the premium change and sends it back to the broker. The broker then reviews it, ensures it’s accurate, attaches it to a policy and sends it via email to the insured.

Very soon, that same process might be completed without human touch across all lines of business in the industry. A seamless exchange of information between carrier and broker could allow an endorsement request to go into a carrier’s AI/ML-enhanced system directly. The ML algorithms will determine whether the request can proceed with or without an underwriter’s review. Requests that don’t need underwriter review can undergo a straight-through process, where the carrier’s system generates a new quote or policy, calculates the premium change and sends it back to the broker’s system automatically. With the touch of a button, the broker can review it and send it to the insured. A process that once took hours or days could be completed in minutes.

For carriers that want to maintain profitability and continue to prosper in this market, creating transparency around data—and enabling a seamless transaction of that data between carrier, broker and insured—is a must. Data and technology today fuel the ability for insurers to both survive and thrive. Unlocking your operational effectiveness will propel enhanced customer and business partner experience and let you stand out in the face of increased customer and shareholder demands.