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Healthcare Fraud and Waste Detection

Healthcare insurance fraud and waste is a pressing problem, which causes substantial costs in insurance companies. Due to large amounts of claims submitted per day, review of individual claims or providers is a difficult task. This is an automated pre-payment decision support tool enhancing the claim management process by detecting suspicious claims.

The problem

One of the most important problems of the insurance industry is fraud which causes substantial losses. The US National Health Care Anti-Fraud Association (NHCAA) estimates that the financial losses due to health care fraud are in the tens of billions of dollars each year. A conservative estimate is 3% of total health care expenditures, while some government and law enforcement agencies place the loss as high as 10% of annual health outlay, which could mean more than $300 billion. The FY 2020 Medicare FFS estimated improper payment rate is 6.27%, representing $25.74 billion in improper payments. The FY 2020 national Medicaid improper payment rate estimate is 21.36%, representing $86.49 billion in improper payments.

The purpose

Detect and eradicate health insurance fraud

The solution

An AI-based tool that assesses all claims, and flags possible fraudulent claims or wasteful, prior to reimbursement.

The benefit

The benefits are the following:

administrative cost reduction
operational cost reduction
inspection cost minimization
expenditure control
claims prediction
strategic advantage in pricing
strategic advantage in underwriting
ultra-high ROI

Indicative data

The mandatory fields to carry out the outlier detection are: 'Invoice code','Insurance Claim Number', 'Date Admitted', 'Date Discharged', 'Service Provider', 'Final ICD10 code', 'DRG','Quantity', 'Service Provider Description', 'Net Amount per service', 'Total Net Amount', which are derived from historical data.
The optional fields contain the values of historically recorded characteristics such as: 'Chemo Therapy', 'Emergency', 'Immuno Therapy','Radiation Therapy'

Table 1. Sample table of user input data

Invoice code	Insurance Claim Number	Date Admitted	Date Discharged	Service Provider	Final ICD10 code	DRG	Quantity	Service Provider Description	Net Amount per service	Total Net Amount	Chemo Therapy	Emergency	Immuno Therapy	Radiation Therapy
21346	324567	02/03/2019	03/03/2019	Hosp A	D30	YO8X	25	Medicines	600€	1870€	0	1	0	0
21346	324267	02/03/2019	03/03/2019	Hosp A	D30	YO8X	91	Medical Supply	550€	1870€	0	1	0	0
21348	324854	02/03/2019	04/03/2019	Hosp B	K80	HO8X	56	Medicines	180€	3200€	0	0	0	0
21347	324954	02/03/2019	04/03/2019	Hosp B	K80	H08X	75	Medical Supply	850€	3200€	0	0	0	0

System prerequisites:

Toolbox accepts xlsx or csv files.
The names of the variables must necessarily match the names as listed in table 1

Output:

After the data has been entered by the user and after a short period of time for its automatic analysis:

A report of the results is extracted from the system.
An excel is exported with the results of the outlier detection.

Note: For any clarification you need regarding the content of the use case or any information related to the collection or validity of your data please contact us.