Insurance is a highly regulated industry that is evolving as the industry faces changing customer expectations, massive amounts of data, and increased regulations. A major issue facing the industry is tracking insurance fraud. Trying to track insurance fraud is an expensive and time-consuming process. Already large volumes of data are growing exponentially as new sources of insurance claim information make identifying fraudulent insurance claims even harder.
Insurance organizations are increasingly using cloud-based, GPU-accelerated artificial intelligence (AI) and machine learning (ML) predictive analysis models to improve the customer experience and identify insurance fraudulent claims quickly and accurately. The AI solution can also provide insights into new services, products and help increase revenue for the insurer.
What is insurance fraud?
According to the NAIC Center for Insurance and Policy Research organization, “Insurance fraud occurs when an insurance company, agent, adjuster or consumer commits a deliberate deception in order to obtain an illegitimate gain.” Examples of fraud include overstating the amount of vehicle damage or bodily injury following a vehicle accident. Fines and possible jail terms can be levied based on the type and severity of the fraud.
Federal Bureau of Investigation (FBI) statistics state, “The total cost of insurance fraud (non-health insurance) is estimated to be more than $40 billion per year. That means insurance fraud costs the average U.S. family between $400 and $700 per year in the form of increased premiums.”
Traditional methods of tracking insurance fraud are inefficient
Claims data can increasingly come from sources besides written documents such as phone calls, images, online information, and data from vehicle sensors. The new information and sheer volume of data make it difficult for manual evaluation by insurance claims staff to determine the validity of the claim and if there is potential fraud. The existing manual evaluation process takes valuable staff time but also limits opportunities for data-driven insights from new data sources that could improve insurance products and services.
Building an effective AI insurance fraud solution
AI is specifically suited to detect insurance fraud because it picks up patterns that humans can’t easily interpret. Insurance organizations are increasingly using AI and ML predictive data models running on modern infrastructure to analyze claims data to help locate anomalies that indicate evidence of potential fraud.
Moving to a cloud-based GPU-accelerated infrastructure provides faster processing and training for ML inference models needed to analyze the massive amounts of data to locate fraudulent insurance claims. As described in this article, “A GPU consists of hundreds of cores performing the same operation on multiple data items in parallel. Because of that, a GPU can push vast volumes of processed data through a workload, speeding up specific tasks beyond what a CPU can handle.”
According to a Forrester survey, “What organizations need are prebuilt, configurable AI cloud services. Cloud AI services allow developers to access a depth of AI capabilities via APIs for fueling application innovation without requiring data science experience.” Moving to a cloud-based AI solution that includes prebuilt AI models results in faster deployment time, and gives organizations access to AI models that have been responsibly built and tested.
NVIDIA’s “State of AI in Financial Services” survey found that the use of AI for fraud detection for know your customer (KYC) and anti-money laundering (AML) compliance was one of the top AI solutions implemented between 2021 and 2022.
Many insurers are still running old legacy technology that is due for an upgrade. The benefits of legacy system modernization for insurance organizations include detecting insurance fraud, a reduction in costs, business agility, improved productivity, and user experience.
Technology partners provide cloud-based, GPU-accelerated AI insurance fraud solutions
The partnership between Microsoft and NVIDIA makes NVIDIA’s powerful GPU acceleration available to insurance organizations. The Azure Machine Learning service integrates the NVIDIA open-source RAPIDS software library that allows machine learning users to accelerate their pipelines with NVIDIA GPUs. The NVIDIA TensorRT acceleration library was added to ONNX Runtime to speed deep learning inferencing. Azure supports NVIDIA’s T4 Tensor Core Graphics Processing Units (GPUs), which are optimized for the cost-effective deployment of machine learning inferencing or analytical workloads for insurance organizations.
NVIDIA also has toolkits to aid developers in implementing AI. The breadth of NVIDIA’s software development kits (SDKs) are important because insurance claims include audio data, imagery, and other information that require deep learning to analyze at scale and speed with accuracy. For example, Quantiphi is using the NVIDIA DeepStream SDK, NVIDIA TensorRT SDK, and NVIDIA Transfer Learning Toolkit as developer tools supporting their AI insurance Claims solution.
Microsoft cloud-based solutions for insurance fraud
Moving to the Microsoft Azure cloud solution provides insurance organizations with a complete set of computing, networking, and storage resources integrated with workload services capable of handling the requirements of insurance algorithm processing.
The Microsoft AI platform contains tools such as natural language processing (NLP) and optical character recognition (OCR) that can automatically analyze documents and phone calls to aid in fraud detection. These tools provide the ability to look at rich data previously excluded from fraud detection. For example, claims assessors at AI-powered insurance companies could gain insights from video, photographs, audio, and vehicle telemetry, which would allow them to better understand liability and help determine if there are data patterns suggesting fraud.
Insurance organizations can use the Azure Cosmos DB to store and track data and Microsoft Power BI for further analysis of the data. Using the Azure Synapse Claim Fraud Analytics Solutions by UB Technologies Innovation, Inc (UBT) predictive modeling tool aids in identifying suspected fraudulent claims.
Insurance organizations are required to track and report potential insurance fraud. There is massive growth in claims data that must be analyzed coming from new sources such as photographs, audio, online documents, and information from vehicle sensors. The amount and complexity of the data makes manual analysis by claims staff difficult and often ineffective.
Using AI and ML algorithms running on cloud-based GPU-accelerated solutions can analyze patterns in claims data to accurately identify which claims are potentially fraudulent. The AI solution helps insurance organizations save staff time but can also provide insights for new sources of products or services to increase business revenue.