Automatic fact verification systems increasingly rely on large language models (LLMs). We investigate how parametric knowledge biases in these models affect fact-checking outcomes of the HerO system (baseline for FEVER-25). We examine how the system is affected by: (1) potential bias in Llama 3.1's parametric knowledge and (2) intentionally injected bias. When prompted directly to perform fact-verification, Llama 3.1 labels nearly half the claims as "Not Enough Evidence". Using only its parametric knowledge it is able to reach a verdict on the remaining half of the claims. In the second experiment, we prompt the model to generate supporting, refuting, or neutral fact-checking documents. These prompts significantly influence retrieval outcomes, with approximately 50\% of retrieved evidence being unique to each perspective. Notably, the model sometimes refuses to generate supporting documents for claims it believes to be false, creating an inherent negative bias. Despite differences in retrieved evidence, final verdict predictions show stability across prompting strategies. The code is available at: https://github.com/eibakke/FEVER-8-Shared-Task

Machine learning could become a new weapon in the fight against Medicare fraud. Machine learning can be a useful tool in detecting Medicare fraud, according to a new study that can recover anywhere from $ 19 billion to $ 65 billion lost in fraud each year. Researchers at Florida Atlantic University's College of Engineering and Computer Science recently published the world's first study using Medicare Big data, machine learning, and advanced analytics to automate fraud detection. They tested six different machine learners on balanced and unbalanced data sets and eventually found that the RF100 Random Forest algorithm would be most effective in detecting potential cases of fraud. They found that unbalanced data sets are more than balanced data sets when scanning for fraud.

Machine learning could become a new weapon in the fight against Medicare fraud. Machine learning could become a useful tool in helping to detect Medicare fraud, according to a new study, potentially reclaiming anywhere from $19 billion to $65 billion lost to fraud each year. Researchers from Florida Atlantic University's College of Engineering and Computer Science recently published the world's first study using Medicare Part B data, machine learning and advanced analytics to automate fraud detection. They tested six different machine learners on balanced and imbalanced data sets, ultimately finding the RF100 random forest algorithm to be most effective at identifying possible instances of fraud. They also found that imbalanced data sets are more preferable than balanced data sets when scanning for fraud.

Using a highly sophisticated form of pattern matching, researchers from Florida Atlantic University's College of Engineering and Computer Science are teaching "machines" to detect Medicare fraud. About $19 billion to $65 billion is lost every year because of Medicare fraud, waste, or abuse. Like the proverbial "needle in a haystack," human auditors or investigators have the painstaking task of manually checking thousands of Medicare claims for specific patterns that could indicate foul play or fraudulent behaviors. Furthermore, according to the U.S. Department of Justice, right now fraud enforcement efforts rely heavily on health care professionals coming forward with information about Medicare fraud. "The Effects of Varying Class Distribution on Learner Behavior for Medicare Fraud Detection With Imbalanced Big Data," published in the journal Health Information Science and Systems, uses big data from Medicare Part B and employs advanced data analytics and machine learning to automate the fraud detection process.

IMAGE: This is Taghi M. Khoshgoftaar, Ph.D., co-author and Motorola Professor in FAU's Department of Computer and Electrical Engineering and Computer Science. Using a highly sophisticated form of pattern matching, researchers from Florida Atlantic University's College of Engineering and Computer Science are teaching "machines" to detect Medicare fraud. About $19 billion to $65 billion is lost every year because of Medicare fraud, waste or abuse. Like the proverbial "needle in a haystack," human auditors or investigators have the painstaking task of manually checking thousands of Medicare claims for specific patterns that could indicate foul play or fraudulent behaviors. Furthermore, according to the U.S. Department of Justice, right now fraud enforcement efforts rely heavily on health care professionals coming forward with information about Medicare fraud.

With the overall increase in the elderly population comes additional, necessary medical needs and costs. Medicare is a U.S. healthcare program that provides insurance, primarily to individuals 65 years or older, to offload some of the financial burden associated with medical care. Even so, healthcare costs are high and continue to increase. Fraud is a major contributor to these inflating healthcare expenses. Our paper provides a comprehensive study leveraging machine learning methods to detect fraudulent Medicare providers. We use publicly available Medicare data and provider exclusions for fraud labels to build and assess three different learners. In order to lessen the impact of class imbalance, given so few actual fraud labels, we employ random undersampling creating four class distributions. Our results show that the C4.5 decision tree and logistic regression learners have the best fraud detection performance, particularly for the 80:20 class distribution with average AUC scores of 0.883 and 0.882, respectively, and low false negative rates. We successfully demonstrate the efficacy of employing machine learning with random undersampling to detect Medicare fraud.