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Using claims, policies, emails, and loan applications to detect fraud can seem too time-consuming for companies battling the agile insurance fraudster. In this whitepaper, you will learn how to use an analytical approach to bring together disparate data, visualize it, and share it—at the speed of fraud. By mixing and matching data, then visualizing it in revealing ways through the creation of dashboards, you can detect fraud quickly and share these insights with key stakeholders. Data can be your most effective weapon against fraud.
We've also pulled out the first several pages of the whitepaper for you to read. Download the PDF on the right to read the rest.
Creative. Resourceful. Ingenious. Words often used to describe a fraudster’s scheme. Instead of letting nefarious characters own these concepts, use them to identify and fight back against fraud.
You probably already have the information you need to detect fraud: claims, policies, emails, and loan applications. But using these as a strategic weapon to detect suspicious patterns can seem too time-consuming or even impossible. It’s not.
Use an analytical approach that equips you to bring together disparate data, visualize it, then share it. This approach means that you can detect the patterns behind a range of activities, rather than getting stuck in one silo of data at a time. It fundamentally shifts the fight against fraud to your advantage.
Uncovering a pattern of fraudulent credit card activity, for example, becomes easier when you
consider the location, frequency and merchant type for the claim together with credit card applications.
Does it make sense that one merchant has more credit card fraud claims than others in the same line of business at the same mall? Are your suspicions heightened when you notice a jump in credit card applications in the same town?
Once equipped to analyze disparate data in one place, you want to find relationships within the data that suggest behavior is amiss. Visualizing your data is the key to quickly identifying patterns and outliers.
Mapping your claims data, for example, could reveal a sharp uptick in emergency room visits in three neighboring counties with historically low ER activity. Is there a sudden rash of accidents or is a new effort underway to acquire unnecessary prescription meds? Visualizing your data tells you immediately that more investigation is required.