Data mining digs the up dirt on compensation fraud

Operating under a tight Government budget in a difficult climate for insurance companies throughout Australia, NSW's WorkCover Authority found investing in data mining tools a "necessary business requirement" in its ongoing effort to cut down on fraud.

WorkCover, which manages workplace injury management and workers' compensation systems in NSW, established a compliance improvement branch to focus on claimant fraud, under-insurance and non-insurance.

To this end, the branch found it essential to build a research team and initiate a data mining project. The aim was twofold: to identify employers making inaccurate insurance premium payments and to cut down on illegitimate insurance claims.

The first step was to establish the data mining, analytical research team (DMART) and roll out data mining tools.

Dr Peter O'Hanlon, team leader of data mining research at WorkCover and original member of DMART, said the investment in data mining software was a "necessary business requirement".

"There were enough savings calculated to justify the expense of going this way. To not go this way would have cost so much more," he said.

"This branch had been established and the analytics driving the activity was fundamental. It was never going to be a big branch and the budget was tight. But we had the money to spend to see the potential savings [from this investment]."

O'Hanlon would not comment on the tougher climate for the insurance company, but said, "there has been a greater impetus to see to it people pay us premiums and to cut down on fraud."

Before the data mining tool implementation, investigations into premium avoidance and insurance fraud was done in a reactive manner, according to O'Hanlon. "We have developed technology that helped us pro-actively seek out and detect cases of fraud and underpayments of premium," he said. "WorkCover did not have this focus in the past when looking at both areas."

In order to focus on inaccurate premium payments, such as the under-declaration of wages, the team conducted targeted wage audits. It can take three to 12 months to do an audit and, with about 350,000 employers paying premiums to the authority, audits can be a costly process, causing a major inconvenience to employers. With the system O'Hanlon and his team can get an employer history and a score of risk for each employer in the program.

"The system identifies audits not to do, those that would be a waste of time and money," he said.

In order to focus on inaccurate premium payments, such as the under-declaration of wages, the team conducted targeted wage audits. It can take three to 12 months to do an audit and, with about 350,000 employers paying premiums to the authority, audits can be a costly process, causing a major inconvenience to employers. With the system O'Hanlon and his team can get an employer history and a score of risk for each employer in the program.

"To run an investigation, we create a data set that contains known cases with our collected historical information. Then, using different decision trees and neural networks, we build a model that predicts behaviour. We validate that it works with previous known cases. Finally we apply that model to cases that we haven't looked at. From this we find potential new cases."

Targeted audits helped the team see a return on its investment.

"We can now do 1000 wage audits a month, previously we did 300 to 500 a month. So we increased the number and improved the selection process. We doubled the premium collected two years in a row. This is due largely to the implementation of data mining and is part of the ROI," O'Hanlon said.

The system also helps the team pinpoint fraudulent WorkCover claims. Although there are fewer cases of claimant fraud, compared to under-insured employers, O'Hanlon said those few cases can generate significant losses to the company.

"One particular case of [detected] fraud alone saved us $700,000 when we caught it. We picked it up as a result of the program," he said.

"From the outset, we recognised the changing nature of fraud cases over time. So we're not locked into a system for good, because the nature of fraud will change."

O'Hanlon could not disclose the investment into the program, but said the authority realised its ROI within the first three months. The program has been running since October 2001.

"This was measured in actual savings to the scheme, such as the premiums collected," he said.

"WorkCover has received $11 return for every one dollar it invested in cracking down on claimant fraud, and $6.30 for every dollar in wage audit."

It took one and a half years to get the data mining tool to a stage where DMART felt its risk management system was working effectively, and just months for the tool to be running completely, according to O'Hanlon.

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