In the insurance industry, the prevailing wisdom is that insurance fraud costs organizations an average of between 5% and 10% of annual revenue. Many people suggest that the cost of fraud is already included in the insurance industry’s operating models as “a cost of doing business” which reflects higher cost structures for insurers and higher premiums for the insured.
Fraud is a drain on profitability – and a reduction in fraud goes directly to the organization’s bottom line.
To put the impact of fraud in the insurance industry in perspective – and to quantify the value of investments in counter fraud analytics solutions – consider the following insights from a simple, but highly effective Monte Carlo analysis using the property and casualty insurance industry as an example:
- Estimates for the annualized cost of fraud in the property and casualty insurance industry, as a percentage of top-line revenue, range from 0% to more than 25%.
- Over the past five quarters, average earnings before interest, taxes, depreciation, and amortization (EBITDA) for the property and casualty insurance industry ranged between 11.85% and 18.95% of top-line revenue. (EBITDA is an indicator of the overall profitability of a business.)
- Analysis of these facts alone shows that the annualized cost of fraud has a significant business impact: under the status quo, the median annual cost of fraud equates to about 59% of TTM EBITDA, with an 80% confidence interval of between 15% to 99%.
In other words, for every dollar of the property and casualty insurance industry’s profitability, the annual cost of fraud is most likely to be about 60 cents — with a 10% likelihood that it will equate to nearly an extra dollar of cost. Is this an acceptable “cost of doing business?”
Take this analysis to the next level:
- Over the past five quarters, the price to earnings (P/E) ratio for the property and casualty insurance industry ranged between 5.70 and 29.59. (The P/E ratio is an indicator of the multiple that investors are willing to pay for every dollar of earnings.)
- Incorporating these facts into the analysis shows that under the status quo, every $1 in fraud reduction that falls to the bottom line translates to between $10.80 and $24 in market valuation, with a median P/E multiple of about 17.7.
This simple, fact-based analysis makes it clear that business discussions about fraud in the insurance industry need to begin by fully appreciating the significant impact of fraud — not only the cost to the insurer, which is increasingly unaffordable, but also the social implications of fraud, which is increasingly unacceptable.
Add to this the reality that under the status quo, companies consistently report instances of fraud happens up to five years or more before being detected, with a median duration of about 20 months. This revelation is a primary source of value for incremental investments in counter fraud analytics solutions: the ability to detect fraud sooner — before paying fraudulent claims.
By taking full advantage of the integration and advanced capabilities currently being offered by leading counter fraud solution providers — including predictive analytics and cognitive computing — the enterprise counter fraud department can expect to achieve significantly better outcomes. Such as faster detection and investigation; lower losses, higher profitability, and higher market valuation; increased productivity and lower operating costs for counter fraud activities; and new opportunities for competitive advantage keeping insurance rates low.
It is time to end out-of-date, business-as-usual thinking about insurance fraud. This simple analysis makes it abundantly clear that senior business leaders need to bring the days of viewing fraud strictly as a “cost of doing business” being absorbed into the organization’s standard operating model to a swift end.
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