This is the theme of our post today.
A fraud case is being investigated, an Insured has filed a claim for rear ending other vehicle. But it was detected by the diligent Insurance adjuster that this is a dubious claim.
How did he do this? He made use of software tools and techniques and found a similar trend of the insured's past claims.
The savvy adjuster has saved the insurance company possibly thousands of dollars just by detecting 1 fraud case. Think of the big picture - how careful investigation and early fraud prevention and detection techniques can save Insurance companies hundreds of millions of dollars.
Advnatages of early Fraud Detection
Following are the reasons why an insurance organization would detect or prevent fraud cases
2> Guard against future adverse risk selection
3> Optimal and accurate product pricing due to saved costs
4> Pass the profits and revenue saved, to Shareholders and insured (via reduced premiums or dividends)
Insurance Organizations have always been hit by fraud claims and in the current economic scenario, they are trying their best to identify these fraud cases and thereby minimize their losses. Today literally tens of billions of dollars are lost by insurance companies due to fraud claims or due to over inflated claims.
Structured and organized Data is something that can help curb or prevent these fraud cases by providing uniform and superior claims information. There are several tools and software available in the market that will allow insurance companies to identify and reduce these fraud cases.
When an insurance company pays for a claim, it involves Indemnity payments and expenses (Legal + Claims Handling + Other). All this is paid on basis of its earned premiums and income from investments.
For e.g. If an Insurance company has an earned premium of $100 and pays out $80, it has a loss ratio of 80%. The rest of the 20% accounts for Administrative expenses, operating costs, profits etc for the Organization.
So logically if an organization eliminates or even reduces these fraud payments and associated expenses like legal costs, investigation costs etc, that would add to the profits of the organization.
Tools and resources to achive this
Normally organizations have experienced Underwriters, Adjusters, SIU departments that carefully scrutinize and help to identify and avoid dishonest claims and thereby save company money. They also make use of Third party investigators who specilize in handling and investigating certain types of Fraud cases.
Now these type of cases are not Line of Business specific and thus occur in every line of business (Auto, GL, Property, Workers Comp etc).
Fraud Indicators and Claim Ranking
Based on the company experiences + the historical claims data collected + analyzing fraud claims from various third party agencies, Insurance organization can come up with their own Predictive analytics solutions or make use readymade tools available in the marketplace which help analyze the data and come up with scoring models that help reduce the total claims outcome.
e.g. There are few red flags identified on a claim, the predictive analytics tool applies these to the claim and assigns it a claim rank or score.
These claims with a high score are then passed on to an investigator for further inquiry.
1> Insured uses several different mailboxes for addresses.
3> Insured has medical bills which are overinflated
4> Insured is on a medical disability leave and wins the National Marathon championship
5> Insured has a claim on property much above the actual cost of damage.
6> Insured has filed a theft claim for a high value and secured property like an antique picture.
7> Multiple Claims for same occurrence filed with different insurers.
ISO ClaimSearch
There are some third party organizations like ISO ClaimsSearch which diligently collect P&C Insurance Claims data of hundreds and millions of Insureds. Insurance companies in turn make use of ISO ClaimSearch for a fee to research prior claims history of their insureds, identify claim trends and pattern. The Insurance companies then, will feed their Insurance data to ISO ClaimSearch so as to strengthen the ISO database.
- By improving on their claims processes
- Having Data Models that are well structured
- Having strong Business rules and validations in place that identify every entry point into the system
- Technology and process improvements are the key to good and clean data which will help in better analysis
Predictive analytics
Predictive analytics consists of data analysis techniques and methods which help to develop predictive models used for trend forecasting. These tools typically assign a predictive score which is very similar to an individual's credit score obtained from various credit bureaus. The credit bureaus also similarly look at various criteria for a consumer (viz Income, credit history, balances, loan etc) and come up with a credit score for that individual.
Data Mining and Analysis
Data is first mined from various data sources within an organization along with the relationships between the data elements. This data is then analyzed with the help of various Predictive models this helps an organization in achieving its fraud curbing objectives.
The data that is obtained is analyzed for identifying trends and relationships which may point to some wrong doing.
1> Have consistent and structured data
3> Budget and preparation throughout organization for implementation and have process changes for clean data entry points
Above post highlights the importance of having organized and structured data. The advantages are manifold, in all domain verticals and not just insurance.
Predictive analytics helps insurance companies identify potential fraud needle claims in all of its millions of claims haystack. In any case, all these are crime preventive and minimizing techniques.
I have a question, can I filed an title insurance claim if my property value went down because the person who sold my house didnt disclose that they filed an easement after my contract was signed and two days before my closing date?Claims pages
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