Data Brokers: Mitigating Fraud in Healthcare Data

By Nathan B. Smith

According to Juniper Research, between 2020 and 2024, online fraud will cost organizations more than $200 billion. 1 The astounding quantity is a result of the intelligence and multiplicity of attack avenues in fraud efforts. And although fraudsters have modified their methods to avoid detection, banks are fighting back harder than ever.

Between 2020 and 2024, companies will lose more than $200 billion to online fraud. The astounding quantity is a result of the intelligence and multiplicity of attack avenues in fraud efforts. Graph analytics, however, has given banks a new tool in the fight against fraud. According to Richard Henderson of TigerGraph, these methods may be used to combat financial crime by analyzing the connections between individuals, phones, and bank accounts to discover symptoms of fraudulent behavior and aid banks in identifying suspicious activity in a sea of data (Hendersoon, 2020).

Discussion

For storing and searching for data created for data with many connections, like Facebook, Instagram, LinkedIn, and Twitter, graph databases are a particularly strong option. It has become necessary to develop new storage and analysis systems that organize irregular data with a flexible schema due to data multiplication and data type variety. sustaining optimal data scalability while assuring high levels of speed, which is a challenge. Relational databases are far less effective in managing and analyzing networked data. Some of the most widely used graph databases include Neo4J, OrientDB, AllegroGraph, ArangoDB, and InfiniteGraph. Fernandes & Bernardino (2018) research the most crucial characteristics of a full and effective program with features like sharding, scalability, and query languages.

The detection of fraud, waste, and abuse (FWA) is a significant yet difficult issue. In a combined commercial and academic research project, Liu, et al. (2016) outline a technique for monitoring huge healthcare datasets for suspicious activity. Every healthcare dataset may be thought of as a heterogeneous network made up of millions of patients, tens of thousands of pharmacies, hundreds of thousands of physicians, and other entities. To identify suspicious people, suspicious interactions between people, unexpected changes over time, unusual geographic dispersion, and anomalous network structure, graph analysis tools are created. The so-called “Network Explorer” visualization tool gives customers a clear overview of the data and gives them the option to filter, choose, and zoom into specific network features as needed. The method, developed by researchers at the Palo Alto Research Center (PARC), Yahoo, and Massachusetts Institute of Technology (MIT), has been implemented on several sites and databases, both public and private, and has found numerous overpayments with a monthly potential worth of several million dollars.

Conclusion

Healthcare fraud is a crime with victims. Each year, it results in losses of billions of dollars for both corporations and people. It may result in higher health insurance costs, the need for unneeded medical treatments, and higher taxes. Medical professionals, patients, and anyone who purposefully mislead the medical system to get illegal benefits or payments may all be guilty of healthcare fraud.  For both public and commercial insurance plans, the FBI is the principal agency responsible for investigating health care fraud (FBI, 2022). 

Artificial intelligence is a vital weapon in the battle against fraud. For a low-risk provider (a "qualified doctor") and a highly risky provider (a "bad doctor"), an organization can create graph-based machine learning characteristics. These features are used to train artificial intelligence to hunt for these profiles throughout huge healthcare datasets (Hendersoon, 2020).

References

FBI. (2022). Scams and safety: Healthcare fraud. FBI Website: https://www.fbi.gov/scams-and-safety/common-scams-and-crimes/health-care-fraud

Fernandes, D., & Bernardino, J. (2018). Graph databases comparison: AllegroGraph, AnangoDB, InfiniteGraph, Neo4J, and OrientDB. n Proceedings of the 7th International Conference on Data Science, Technology and Applications (DATA 2018), (p. 2018). https://doi.org/10.5220/0006910203730380

Hendersoon, R. (2020). Using graph databases to detect financial fraud. Computer Fraud & Security, 2020(7), 6-10. https://doi.org/10.1016/S1361-3723(20)30073-7

Liu, J., Bier, E., Wilson, A., Guerra-Gomez, J. A., Honda, T., Sricharan, K., . . . Davies, D. (2016). Graph analysis for detecting fraud, waste, and abuse in healthcare data. AI Magazine, 37(2), 33-46. https://doi.org/10.1609/aimag.v37i2.2630



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