5 Machine Learning Use Cases in the Insurance Industry

In 2020, the US insurance industry was worth a whopping $1.28 trillion. The US insurance industry is one of the largest markets in the world. The sheer amount of premiums means there is an astronomical amount of data. Without artificial intelligence technology such as machine learning, insurance companies will have an almost impossible time to process all this data, creating greater opportunities for insurance fraud.

Insurance data is vast and complex, composed of many individuals with many cases and many factors used in determining claims. In addition, the type of insurance increases the complexity of receiving and processing data. Life insurance is different from auto insurance, health insurance is different from property insurance, and so on. While some of the processes are similar, the data can vary greatly.

As a result, insurance businesses must prioritize digital initiatives to process massive amounts of data and support vital business objectives. In the insurance industry, advanced technology is critical to improving operational efficiency, delivering excellent customer service and ultimately increasing the bottom line.

ML can handle the size and complexity of insurance data. It can be applied to multiple aspects of insurance practice and facilitates improvements in customer experience, claims processing, risk management and other overall operational efficiencies. Most importantly, ML can mitigate the risk of insurance fraud that plagues the entire industry. This is a major development in fraud detection and insurance organizations should add it to their fraud prevention toolkit.

In this post, we outline how insurance companies are using ML to improve their underwriting processes and spot insurance fraud before it affects their bottom line. Read on to see how ML can fit into your insurance organization.

What is machine learning?

ML is a technology under the umbrella of AI. ML is designed to analyze data so that computers can make predictions and decisions based on identifying patterns and historical data. All this without explicit programming and with minimal human intervention. With more data production comes smarter ML solutions as they adapt autonomously and continuously learn. Ultimately, AI/ML will handle menial tasks and free human agents to perform more complex queries and analyses.

What are the benefits of ML in the insurance industry?

There are several ML use cases within an insurance organization, regardless of the type of insurance. Below are some top application areas of machine learning in the insurance industry:

1. Leading management

For insurers and marketers, ML can identify potential customers using valuable data insights. ML can even personalize recommendations based on a buyer’s past actions and history, allowing marketers to have more effective conversations with buyers.

2. Customer service and retention

For most customers, insurance can seem daunting, complicated and unclear. It is important for insurance companies to help their customers at every stage of the process to increase customer acquisition and retention. ML through chatbots in messaging apps can be very useful for guiding users through claim processing and answering basic FAQs. These chatbots use neural networks that can be developed to understand and respond to most customer inquiries via chat, email or even phone calls. Additionally, ML can take data and determine customer risk. This information can be used to recommend the best offer that is most likely to retain a customer.

3. Risk management

ML uses data and algorithms to immediately detect potentially unusual or unexpected activity, making ML a key tool for loss prediction and risk management. This is vital for usage-based insurance devices, which determine car insurance rates based on specific driving behaviors and patterns.

4. Fraud Detection

Unfortunately, fraud is rampant in the insurance industry. Property and casualty insurance alone loses about $30 billion to fraud each year, and fraud accounts for nearly 10% of all P&C losses. ML can mitigate this problem by identifying potential claim situations early in the process. Early flagging allows insurers to investigate and correctly identify a fraudulent claim.

5. Claims Processing

Claims processing is notoriously difficult and time-consuming. ML technology is a tool to reduce costs and processing time, from initial claim submission to coverage review. Additionally, ML supports a great customer experience as it allows policyholders to check the status of their claim without having to contact their broker/adjuster.

Why is machine learning so important to fraud detection in the insurance industry?

Fraud is one of the biggest problems for the insurance industry, so let’s go back to the fraud detection stage of the insurance lifecycle and detail the benefits of ML for this common problem. Given that the insurance industry is comprised of more than 7,000 companies that collect over $1 trillion in premiums each year, there are enormous opportunities and incentives for insurance fraud to occur.

Insurance fraud is a problem that has worsened since the start of the COVID-19 pandemic. Some industry professionals believe the number of claims with some element of fraud has nearly doubled since the pandemic.

Below are the various stages at which insurance fraud can occur during the insurance life cycle:

  • Application fraud: This fraud occurs when false information is intentionally provided on an insurance application. This is the most common form of insurance fraud.
  • False claim fraud: This fraud occurs when insurance claims are filed under false pretenses (ie faking death to collect life insurance benefits).
  • Forgery and identity theft fraud: This fraud occurs when a person tries to file a claim on someone else’s insurance.
  • Inflation Fraud: This fraud occurs when an additional amount is attached to the total bill when the insurance claim is submitted.

Based on the size of fraud and the different types of fraud, insurance companies should consider adding ML to their fraud detection tools. Without ML, insurance agents can be overwhelmed with the time-consuming process of investigating each case. Machine learning approaches and algorithms that facilitate fraud detection are as follows:

  • Deep anomaly detection: During claims processing, this approach will analyze real claims and identify fake ones.
  • Supervised Learning: Using predictive data analysis, this machine learning algorithm is the most commonly used for fraud detection. The algorithm will mark all input information as “good” or “bad”.
  • Semi-supervised learning: This algorithm is used for cases where information labeling is impossible or very complex. It stores data on critical category parameters even when the group membership of the unlabeled data is unknown.
  • Unsupervised Learning: This model can flag unusual transaction actions and learns specific patterns in the data to continuously update its model.
  • Reinforcement Learning: Gathering information about the environment, this algorithm automatically examines and contextualizes behavior to find ways to reduce risk.
  • Predictive analytics: This algorithm considers historical data and existing external data to discover patterns and behaviors.

ML is a fraud prevention and detection tool. It enables companies to identify suspected fraudulent claims quickly and accurately, process data efficiently and avoid wasting valuable human resources.

Conclusion

The adoption of digital technologies such as ML is vital for insurance businesses to be able to process their data and analytics. This allows insurance companies to increase operational efficiency and reduce the underlying risk of insurance fraud.

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