Claims Fraud Network Analysis (CFNA)

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 the USA 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.

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