As insurance companies seek to apply artificial intelligence technology, one precise application 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 gets it in a format that AI engines can use.
Automated insurance fraud detection provides numerous benefits; it reduces loss ratios and allows 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 is at recognizing patterns that indicate fraud.
Insurance companies can buy data from outside sources, but it must 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 historical data is also a good source, but the same issue applies – it will be mostly unstructured data.
Why RPA and OCR templates fall short
Dealing with unstructured data means automated document processing approaches that rely on optical character recognition (OCR) and templates or use robotic process automation (RPA) will be ineffective. Such methods work well only with highly structured documents to identify fields from which to extract data.
Processing unstructured data requires an intelligent document processing solution. By taking advantage of AI technologies, including machine learning and natural language processing, such tools can read unstructured documents much like a human does and understand the context behind each document or image.
Using such a tool, an insurance company could quickly process lots of historical documents, normalize the data and feed it to the AI engine for modeling.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.
Intelligent Process Automation
The Indico Intelligent Process Automation (IPA) platform includes tools that make it simple to label the sorts of data you want to extract from a document. A business process expert – an employee who understands what to look for in the document – can label maybe 200 documents, enough to build a good working model in just a few hours.
Indico’s platform includes a database of some 500 million labeled data points, enough such that it can understand the context behind virtually any kind of unstructured data. It then takes advantage of AI technology called transfer learning to enable users to bring that massive database to bear on their use cases by labeling actual documents involved in the process they want to automate.
Using IPA, insurance companies often see 85% reduction in process cycle times, 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 other insurance processes, arrange a free demo. Or, if you have any questions, feel free contact us.