How Intelligent Automation Helps Insurers Implement AI-driven Fraud Detection
March 16, 2021 / Insurance, Intelligent Process Automation, Use Case
As insurance companies seek to apply artificial intelligence technology, one clear application for it is detecting fraudulent claims. But training AI models to detect fraud requires data – lots and lots of data. Insurance companies typically have much of the data they need in-house – and applying intelligent document processing can get it in a format that AI engines can use.
Automated insurance fraud detection brings numerous benefits. In reduces loss ratios and helps make it easier to enable straight-through processing, so legitimate claims are paid out faster – making for happier customers.
The data dilemma
Implementing an automated fraud detection system involves training an AI model to identify signs of fraud in a claim. That’s where the data comes in – because the more data you use to train the model, the more effective it will be at recognizing patterns that indicate fraud.
Insurance companies can buy data from outside sources, but it will have to be normalized such that it works with your AI system. That can be challenging given the data is likely to be unstructured and in various formats – PDFs, images, perhaps Word documents or even hand-written notes from adjusters.
An insurance firm’s own historical data is also a good source, but the same issue applies – it will be largely unstructured content.
Why RPA, OCR templates fall short
Dealing with unstructured data means automated document processing approaches that rely on optical character recognition (OCR) and templates, or that use robotic process automation (RPA), will be largely ineffective. Such approaches work well only with highly structured documents where you can identify fields from which to extract data.
Processing unstructured content requires an intelligent document processing solution. By taking advantage of AI technologies including machine learning and natural language processing, such tools can read even unstructured documents much like a human does and understand the context behind each document or image.
Using such a tool, an insurance company could far more quickly process lots of historical documents, normalize the data and feed it to the AI engine for modeling. That goes for both in-house data and any data acquired from third party sources.
Indico Intelligent Process Automation
The Indico Intelligent Process Automation platform, for example, comes with tools that make it a simple matter to label the sorts of data you want to extract from a document. In just a few hours, a business process expert – someone who understands what to look for in the document – can label maybe 200 documents, enough to build a good working model.
Indico’s platform is built on a database of some 500 million labeled data points, enough such that it can understand the context behind virtually any kind of unstructured content. It then takes advantage of AI technology called transfer learning to enable users to bring that that massive database to bear on their own use cases by labeling actual documents involved in the process they want to automate.
It’s a viable approach to quickly garnering lots of data that can then be used by another AI tool to detect fraud. But don’t stop there. The Indico platform can also be used to automate other document-intensive insurance processes, including auto insurance claims, life insurance claims, loss run reports and more.
Using Indico, insurance companies often see reductions in process cycle times of 85% along with a 4x increase in process capacity and an 80% reduction in human resources.
To see for yourself how intelligent automation can help you quickly normalize your historical or third party data to create AI fraud detection models, as well as automate myriad other insurance processes, just arrange a free demo. Or, if you have any questions, feel free contact us.