In 2020, the U.S. insurance industry was worth a whopping $1.28 trillion. High premium volumes show no signs of slowing down and make the American insurance industry one of the largest markets in the world. The massive amount of premiums means there is an astronomical amount of data involved. Without artificial intelligence technology such as machine learning, insurance companies will find it nearly impossible to process all that data. This will create greater opportunities for insurance fraud to occur.
Insurance data is vast and complex. This data is comprised of many individuals with many instances and many factors used in determining the claims. Moreover, the type of insurance increases the complexity of data ingestion and processing. Life insurance is different from automobile insurance, health insurance is different from property insurance and so forth. While some of the processes are similar, the data and multitude of flows can vary greatly.
As a result, insurance enterprises must prioritize digital initiatives to handle huge volumes of data and support vital business objectives. In the insurance industry, advanced technologies are critical for improving operational efficiency, providing excellent customer service, and, ultimately, increasing the bottom line.
ML can handle the size and complexity of insurance data. It can be implemented in multiple aspects of the insurance practice, and facilitates improvements in customer experiences, claims processing, risk management, and other general operational efficiencies. Most importantly, ML can mitigate the risk of insurance fraud, which plagues the entire industry. It is a big development in fraud detection and insurance organizations must add it to their fraud prevention toolkit.
Here is how insurance companies are using ML to improve their insurance processes and flag insurance fraud before it affects their bottom lines, and how ML can fit within your insurance organization.
What is machine learning?
ML is a technology under the AI umbrella. ML is designed to analyze data so computers can make predictions and decisions based on the identification of patterns and historical data. All of this is without being explicitly programmed and with minimal human intervention. With more data production comes smarter ML solutions as they adapt autonomously and are constantly learning. Ultimately, AI and ML will handle menial tasks and free human agents to perform more complex requests and analyses.
What are the benefits of ML in the insurance industry?
There are several use cases for ML within an insurance organization regardless of insurance type. Here are some top areas for ML application in the insurance industry:
For insurers and salespeople, ML can identify leads using valuable insights from data. ML can even personalize recommendations according to the buyer’s previous actions and history, which enables salespeople to have more effective conversations with buyers.
Customer service and retention
For a majority of customers, insurance can seem daunting, complex and unclear. It’s important for insurance companies to assist their customers at every stage of the process in order to increase customer acquisition and retention. ML via chatbots on messaging apps can be helpful in guiding users through claims processing and answering basic frequently asked questions. These chatbots use neural networks, which can be developed to comprehend and answer most customer inquiries via chat, email or even phone calls. Additionally, ML can take data and determine the risk of customers. This information can be used to recommend the best offer that has the highest likelihood of retaining a customer.
ML uses data and algorithms to instantly detect potentially abnormal or unexpected activity, making ML a crucial tool in loss prediction and risk management. This is vital for usage-based insurance devices, which determine auto insurance rates based on specific driving behaviors and patterns.
Fraud is rampant in the insurance industry. Property/casualty insurance alone loses about $30 billion to fraud every year, and fraud occurs in nearly 10% of all P/C losses. Overall, insurance fraud steals at least $80 billion every year from American consumers. ML can mitigate this issue by identifying potential claim situations early in the claims process. Flagging early allows insurers to investigate and correctly identify a fraudulent claim.
Claims processing is notoriously arduous and time-consuming. ML technology is the perfect tool to reduce processing costs and time, from the initial claim submission to reviewing coverages. Moreover, ML supports a great customer experience because it allows the insured to check the status of their claim without having to reach out to their broker or adjuster.
Why is ML so important for fraud detection in the insurance industry?
Fraud is the biggest problem for the insurance industry, so let’s return to the fraud detection stage in the insurance lifecycle and detail the benefits of ML for this common issue. Considering the insurance industry consists of more than 7,000 companies that collect more than $1 trillion in premiums each year, there are huge opportunities and incentives for insurance fraud to occur.
Insurance fraud is an issue that has worsened since the COVID-19 pandemic began. Some industry professionals believe that the number of claims with some element of fraud has almost doubled since the pandemic.
Here are the various stages in which insurance fraud can occur during the insurance lifecycle:
Application fraud: This fraud occurs when false information is intentionally provided in an insurance application. It is the most common form of insurance fraud.
False claims fraud: This fraud occurs when insurance claims are filed under false pretenses (i.e., faking a death in order to collect life insurance benefits).
Forgery and identity theft fraud: This fraud occurs when an individual tries to file a claim under someone else’s insurance.
Inflation fraud: This fraud occurs when an additional amount is tacked onto the total bill when the insurance claim is filed.
Based on the amount of fraud and the different types of fraud, insurance companies should consider adding ML to their fraud detection toolkits. Without ML, insurance agents can be overwhelmed with the time-consuming process of investigating each case. The ML approaches and algorithms that facilitate fraud detection include:
Deep anomaly detection: During claims processing, this approach will analyze real claims and identify false ones.
Supervised learning: Using predictive data analysis, this ML algorithm is the most commonly used for fraud detection. The algorithm will label all input information as “good” or “bad.”
Semi-supervised learning: This algorithm is used for cases where labeling information is impossible or highly complex. It stores data about critical category parameters even when the group membership of the unlabeled data is unknown.
Unsupervised learning: This model can flag unusual actions with transactions and learns specific patterns in data to continuously update its model.
Reinforcement learning: Collecting information about the environment, this algorithm automatically verifies and contextualizes behaviors in order to find ways to reduce risk.
Predictive analytics: This algorithm accounts for historical data and existing external data to detect patterns and behaviors.
ML is instrumental in fraud prevention and detection. It allows companies to identify claims suspected of fraud quickly and accurately, process data efficiently, and avoid wasting valuable human resources.
Implementing digital technologies, such as ML, is vital for insurance businesses to handle their data and analytics. It allows insurance companies to increase operational efficiency and mitigate the top-of-mind risk of insurance fraud. Working with a data consulting firm can help onboard these hugely beneficial technologies.