Of late, claim fraud has plagued the USA
Insurance industry, and it has left them reeling. Even though the exact depth
of such scams is unknown, everyone within the industry agrees that the chunk is
significant and needs some severe fightback to get over. Modern problems
require modern solutions, and the peculiar situation, in this case, involves
solutions by the insurance software companies in the USA.
It would have been easier if there was only
a single party involved. Unfortunately, all of it seems to be pre-planned and
carried on regularly by a group of people. Insurance solutions such as
an effective procedure for identification, investigation, and other stringent
methods have been developed and implemented to counter the ingenuity of such
fraudsters,
All of it seems to be incapable of churning
the desired results. It has forced the insurance software companies in theUSA to develop CFNA or Claims Fraud Network Analysis, which consists of
three modern fraud detection methods, all of which we are covering in today’s
article.
Components of CFNA
Social
Network Analysis
Under this methodology, data is fed into
the extract transform and load tool, which is them transformed and sent to a
warehouse. The information can be as simple as a prior conversation or as
complex as a list of rejected claims.
The analytics team uses the data and tries
to figure out the risk of fraud based on several factors. They use techniques
such as sentiment analysis, text mining, and social network analysis, baked in
the fraud identification and predictive modeling process.
After the analysis is complete, the model
assigns it a score depending on which further action follows. The SNA model can
be of great help for those insurance software companies in USE whose data
arrives fast and required limited processing.
Predictive analytics
Predictive analysis is another solution to
counter the rise in claims fraud. It pushes the insurance organization to
become proactive and forward-looking, thereby anticipating behavior and
outcomes.
Predictive analytics uses several
techniques to capture relationships amongst various factors. Once it
establishes the link, it assesses risk based on specific criteria and assigns
score or weight.
At the onset, we cannot implement
predictive analytics in the entire organization. It has to be on a project
basis, growing and learning with time. People would realize its value over time
and would gradually adapt it for their benefit.
Social
CRM or Social Customer Relationship Management
We all know how useful CRM is and how it
can help organizations to improve their relations with the end-users. But
social CRM is different. It is more a process than being a platform or a
technology. With its help, insurance software companies in
the USA are linking social media to the CRM software.
It has mutual benefits. It provides greater
insight to the insurance company about the clients, whereas it improves
transparency for the other party to the contract. Social CRM gathers data from
several social media platforms, and it acts as a reference for the software. It
then analyses the data and gives a response.
Investigators then carry out their
processes to ascertain if the opinion of the automation software is correct or
not. Even though the tool is relatively sound, it cannot be fruitful alone; you
need to carry out other processes to verify the genuineness of the claim.
Conclusion
Even though being
reluctant, insurance companies in the USA have come a long way in adopting
analytics software to provide them a proactive solution to their ever-growing
claims fraud issue. With more data in hand, it is time that the insurers start
utilizing it to provide themselves a solution to reduce their losses.
