Machine learning (ML) continues to transform how different industries operate and the insurance industry is no exception. Data scientists are developing machine learning models to create smarter and automated processes in the areas of claims, cross-selling, underwriting, and risk management.
The ultimate goal of integrating data science into these functions is to save time and money on repetitive and recurrent tasks.
Claims processing is one of the biggest functions within an insurance business. Depending on the type of insurance, there can be a plethora of claims that are routine and low-risk. Having a claims analyst manually approve these types of claims can result in long processing times, which could lead to customer dissatisfaction. The claims analyst’s time will be best spent focusing on claims above a certain threshold in regards to risk and total dollar amount.
In this blog, we’ll walk through how implementing an ML model can help improve your claims processing.
Why Build an ML Model for Processing Claims?
According to McKinsey, digital claims can reduce expenses by 25-30 percent by improving effective claim handling and increasing customer satisfaction. Transforming the entire claims process requires a fresh look at your current process. Breaking down the factors that influence claims approval will help identify data sources that are necessary to build claim-processing models.
Common factors in claims processing models are:
- Total loss
- Litigation risk
- Fraud risk
- Speed to services
Having a holistic picture of your claims process will help you strategize on how to incorporate AI.
The goal of developing a claims processing model is to automate a certain level of decision-making based on business factors. The model can segment the claims into particular categories and can even have a score associated with them for how likely they are to be approved. From there, the claims can be automatically approved or given to the analysts in a prioritized list to help them decide where to spend their time.
For example, if a particular claim has a high risk of litigation, it will most likely get routed to an agent for review. However, a claim that is for a small amount and is for a common issue can be approved automatically so that the agent’s time is spent on the bigger issues. This automation generally occurs when the AI model provides outputs to robotic process automation (RPA) technology that actually routes the claims in a process called intelligent automation.
How to Build a Claims Processing Model
Building the desired model requires a secure method for extracting data from existing documents or databases and cleaning the data so that it can be used in a machine learning algorithm. The most common data used for claims processing models are:
- Historical transactions
- Account information
- Text data
- Image data
Historical Transactions & Account Information
Customer account history and transactions can be used to help determine unusual transactions. Let’s consider a customer who owns several homes and insures each one with your company. If this particular customer has had similar claims at each house in six-month increments, the model would flag this account as suspicious.
This may not be fraudulent after investigating the root cause, but it is deviating from the norm. However, if the same customer has only had one claim in several years for a small dollar amount, that may be an opportunity to automate that claim.
Most claim forms require explanations and descriptions of the situation. Having a large amount of freeform text data and extracting text from these documents with data scraping techniques is critical. While the text itself is useful to an individual, ML models need it to be processed further with natural language processing (NLP) to remove any noise coming from the claim forms.
If part of the claims process is to upload photos associated with the claim, a separate computer vision (CV) model will have to be developed to process the images. The outcome of the computer vision model can be used as variables into the claims processing model.
You can also set certain thresholds for automatic approvals regardless of what images are attached. For those claims that need additional processing, the agent reviewing the claim can open the images and determine whether or not the claim should be approved.
After all the input data is cleaned, a clustering or classification algorithm is trained to determine an outcome based on past behaviors. Clustering algorithms find trends within the data based on behaviors of past claims and puts them into a group. When a new claim is going through the process, it can be assigned to a given group based on its attributes.
If the new claim is assigned to a group that is associated with high risk, that claim can be reviewed by an analyst. If it’s associated with a low-risk group, then it has the potential of being automatically approved.
A classification model learns from past claims that were either approved or rejected and assigns a score to a current claim to determine if it should be accepted or rejected. If a new claim receives a score indicating it as high risk, it can be rerouted for approval. Otherwise, there is the opportunity to automate its approval. Providing a numeric score, rather than an associated group, allows for analysts to sort a list of claims based on the score and prioritize their work.
Once either model is created, it can be connected to actions in your customer service portal or claims agent system providing a smoother end-to-end connection of your operational processes.
How to Implement Digital Claims Using Snowflake
A modern technology stack helps enable data scientists to create a digital claims process for insurers. All structured data, like historical transactions, account history, and text data can efficiently be stored in a platform like the Snowflake Data Cloud. Any external data, like Experian, can also be shared through Snowflake’s data sharing capabilities.
After the digital claims model has been trained and validated, it needs to be stored and accessible to generate predictions on new data. This process is called inferencing. Your claims model can be deployed leveraging Snowpark’s Python user-defined functions (UDFs) to package the model and run inference workloads on Snowflake compute.
The last step is to use the model’s predictions in your internal processes. To create a completely hands-off process, the model predictions can be integrated into your claims processing platform to automate approvals that meet a certain threshold. Reporting through visualization tools, like Tableau or Sigma Computing, can help your claims processing teams prioritize which claims to investigate.
You can integrate the model’s decision straight into your claims processing program for a completely hands-off process.
Providing faster and potentially automated claims processing for routine items can improve customer satisfaction with your service, potentially resulting in more sustained business. It can also help you determine an objective method for processing routine claims.
Interested in implementing claims processing ML models at your organization? The ML experts at phData would love to help make this a reality for your organization. Contact our ML team today to get started.