Fraudsters are all around us, especially in the fields of finance and insurance. An opportunist exaggerates damage on a household insurance claim after a storm. A chancer claims on their travel insurance for an item they never owned or never lost. A shady health professional provides a phony assessment for a bogus “slip and trip” compensation claim. A gang organises systematic identity theft in order to fake insurance claims on an industrial scale. These cases may not grab headlines that often but, trust us, they happen more frequently than you think.
Fake claims result in increased costs for insurers and increased premiums for businesses and individuals. The Insurance Fraud Bureau of Australia estimates the cost to be $2.2 billion every year—around 10% of total claims. Make no mistake: your pocket is being picked by people perpetrating insurance fraud.
Battling Insurance Fraud with Data Analytics
Big Data is an opportunity for business owners to fight back. According to Forbes, this resource is already available for your business. Big Data can be structured or unstructured. Sources can be internal or external, traditional or digital. Big Data is all around you. You just need the right data analytics and management platform to turn all this data into information and the information into useful and actionable insights.
There are immediate opportunities to detect insurance fraud in your business, even now. This report lists three data-driven tools to help assess insurance claims for fraud which are…
Linking to accurate and comprehensive historical information will improve the assessment of any claim. Patterns of repeated claims for an item or injury can help ensure you identify suspicious claims and potential fraudsters. There are already several providers who have aggregated available fraud information into their own databases which can be utilised to compare with your own information, for a fee.
Synthesising the available information helps you make better decisions. Pictures and infographics are the often best way to simplify large volumes of complex information. This is important because the people most able to make immediate sense of the insight are those on the front line of operations: the business process people. Their particular domain knowledge enables them to infer cause and effect much quicker than perhaps even data analysts.
Investigation apps and software
Turn the available data into checklists and provide one-touch reporting and you will streamline the claims process. This reduces the scope for human error and reduces the need for inefficient manual reports. No need for meetings or phone calls if all the information is accessible to everyone who needs it and in many cases is proactively delivered as alerts when anomalies appear.
Predicting and Preventing Insurance Fraud with Big Data
There are several Big Data options that are now available for companies to achieve higher success rates in terms of predicting and even preventing insurance fraud.
Cause-and-effect can be difficult to demonstrate when you assess risk. However, certain behaviours, attributes, and scenarios are known to have strong associations with insurance fraud. Predictive models can integrate the known risks to produce a simple value or ranking of what to watch out for, giving managers a better idea of what to expect, thereby improving possible prevention protocols.
One step further however is the use of digital tools such as semantic processing and artificial intelligence which move the needle from probabilistic assessment to deterministic assessment. That is to say that if A and B occur, then C (the fraud) definitely will, based on multidimensional data insights.
Neural networks are systems of hardware and software modelled after the human brain. These systems can learn and cope with novelty and innovation. With proper management, customisation, and implementation, neural network technology can detect new and systematic insurance scams. With this technology, you can mitigate the risk of damage from newer fraud schemes before they result in catastrophic losses. This is the case with semantic processing and artificial intelligence as mentioned previously.
Mountains of information are buried in documents, recordings, case notes, surveys, and other sources online and offline. Accessing specific information manually that might indicate fraud is a challenge and can be inefficient and time-consuming. Applications and automated processes which extract and synthesise relevant text, voice, or financial information can make this task much easier and in less time, putting less burden on managers and analysts so they can focus on pinpointing possible insurance fraud incidents and on the preparatory and preventive measures against them.
A Big Data Success Story in the Fight against Insurance Fraud
Last year, the NSW insurance watchdog enlisted the state’s data analytics centre to combat a spike in compulsory third-party (CTP) insurance fraud. Speaking to itnews.com.au, Andrew Nicholls of the State Insurance Regulatory Authority described how “cold-calling claims farms” have sent the level of claims “off the Richter scale.”
Data analysis of state-wide insurer reports exposed this trend. Predictive modelling revealed that the scam threatened the viability of the CTP insurance scheme. Cross-referencing with police motor accident records identified the likely cause—insurance fraud. Data analytics enabled investigators to zero in on the perpetrators using factors such as high correlations between claims from lawyers and verification by medical professionals; geographic clusters; batches of delayed claims; multiple claims from members of the same family; and claims with multiple insurance providers in the same window of time. The agency’s watch-list is now reversing this worrying trend.
While data analytics certainly represents the future of doing business, that doesn’t mean you have to wait to reap its rewards when it comes to improving profits and fighting fraud. Data management platforms such as Latize Ulysses already offer companies today an intelligent, robust, and effective way to harmonise information from different sources and properly derive Big Data insights which you can use to make the right business decisions moving forward.