Unit 5: Data Analysis and Mining Techniques for Fraud Detection
Expert-defined terms from the Advanced Certificate in Healthcare Fraud Investigation Best Practices course at London School of Business and Administration. Free to read, free to share, paired with a globally recognised certification pathway.
**Anomaly detection #
** The process of identifying unusual patterns or outliers in data that do not conform to expected behavior, which may indicate fraudulent activity in healthcare claims. Related terms: outlier detection, unusual pattern detection.
Concept #
Anomaly detection is a key technique used in healthcare fraud investigation to identify claims or providers that are significantly different from the norm. This can include identifying individual claims that are outside of a expected range, or patterns of behavior among providers that are not consistent with their peers. Anomaly detection algorithms can be based on statistical methods, machine learning algorithms, or a combination of both.
Example #
An anomaly detection algorithm may identify a provider who consistently bills for a higher number of procedures per patient than their peers, or a cluster of claims that all contain a specific diagnosis code that is not typically associated with the procedure being billed.
**Cluster analysis #
** A technique used to group similar data points together based on shared characteristics, which can be used to identify patterns of fraud in healthcare claims. Related terms: segmentation, data mining.
Concept #
Cluster analysis is a technique used to identify groups of data points that are similar to each other, but different from other groups. In the context of healthcare fraud investigation, cluster analysis can be used to group providers or claims together based on shared characteristics, such as the types of procedures they bill for or the demographics of their patients. By identifying these groups, investigators can identify patterns of fraud that may not be apparent when looking at individual claims or providers.
Example #
A cluster analysis may identify a group of providers who all bill for a high number of a specific procedure, but who are located in different parts of the country. This may indicate a fraud scheme that is being carried out by multiple providers.
**Data mining #
** The process of automatically discovering patterns and relationships in large datasets, which can be used to identify fraud in healthcare claims. Related terms: knowledge discovery, predictive modeling.
Concept #
Data mining is the process of automatically discovering patterns and relationships in large datasets. In the context of healthcare fraud investigation, data mining can be used to identify patterns of fraud that may not be apparent when looking at individual claims or providers. Data mining techniques can include statistical analysis, machine learning, and artificial intelligence.
Example #
A data mining algorithm may identify a pattern of claims that are being submitted by a specific provider, but that are not consistent with the patterns of other providers. This may indicate fraudulent activity by the provider.
**Decision tree #
** A machine learning algorithm used to classify data points based on a series of decisions, which can be used to identify fraud in healthcare claims. Related terms: tree-based model, classification algorithm.
Concept #
A decision tree is a machine learning algorithm that classifies data points based on a series of decisions. The algorithm starts with a single question, and then splits the data points into different groups based on the answer to that question. This process is repeated, with each split leading to a new question, until the data points are fully classified. In the context of healthcare fraud investigation, decision trees can be used to classify claims as fraudulent or non-fraudulent based on the answers to a series of questions about the claim.
Example #
A decision tree may start with a question about the diagnosis code on a claim, and then split the data points into different groups based on the answer to that question. For example, if the diagnosis code is for a condition that is typically treated in an outpatient setting, the algorithm may classify the claim as non-fraudulent. If the diagnosis code is for a condition that is typically treated in an inpatient setting, the algorithm may classify the claim as fraudulent.
**Feature engineering #
** The process of selecting and transforming variables in a dataset to improve the performance of machine learning algorithms, which can be used to identify fraud in healthcare claims. Related terms: variable selection, data preprocessing.
Concept #
Feature engineering is the process of selecting and transforming variables in a dataset to improve the performance of machine learning algorithms. In the context of healthcare fraud investigation, feature engineering can be used to select and transform variables in a dataset of healthcare claims to improve the performance of a fraud detection algorithm. This can include selecting variables that are known to be associated with fraud, or transforming variables to make them more useful for the algorithm.
Example #
A feature engineering algorithm may select the variables "procedure code" and "billing amount" from a dataset of healthcare claims, and then transform the "billing amount" variable by taking the logarithm of the value. This transformation may improve the performance of a fraud detection algorithm by making it easier for the algorithm to identify claims that are significantly different from the norm.
**Neural network #
** A type of machine learning algorithm inspired by the structure and function of the human brain, which can be used to identify fraud in healthcare claims. Related terms: deep learning, artificial intelligence.
Concept #
A neural network is a type of machine learning algorithm inspired by the structure and function of the human brain. A neural network is composed of interconnected nodes, or artificial neurons, that process and transmit information. Neural networks can be used to identify patterns in large datasets, and can be used to classify data points as fraudulent or non-fraudulent.
Example #
A neural network may be trained on a dataset of healthcare claims to identify patterns of fraud. The neural network may then be used to classify new claims as fraudulent or non-fraudulent based on the patterns it has learned.
**Outlier detection #
** The process of identifying data points that are significantly different from other data points in a dataset, which can be used to identify fraud in healthcare claims. Related terms: anomaly detection, unusual pattern detection.
Concept #
Outlier detection is the process of identifying data points that are significantly different from other data points in a dataset. In the context of healthcare fraud investigation, outlier detection can be used to identify claims or providers that are significantly different from the norm. Outlier detection algorithms can be based on statistical methods, machine learning algorithms, or a combination of both.
Example #
An outlier detection algorithm may identify a claim that has a much higher billing amount than other claims for the same procedure, or a provider who consistently bills for a higher number of procedures per patient than their peers.
**Predictive modeling #
** The process of using data and statistical algorithms to predict future outcomes, which can be used to identify fraud in healthcare claims. Related terms: machine learning, data mining.
Concept #
Predictive modeling is the process of using data and statistical algorithms to predict future outcomes. In the context of healthcare fraud investigation, predictive modeling can be used to identify claims or providers that are likely to be fraudulent. Predictive modeling techniques can include statistical analysis, machine learning, and artificial intelligence.
Example #
A predictive modeling algorithm may be trained on a dataset of healthcare claims to predict which claims are likely to be fraudulent. The algorithm may then be used to classify new claims as fraudulent or non-fraudulent based on the patterns it has learned.
**Segmentation #
** The process of dividing a dataset into smaller, more homogeneous groups based on shared characteristics, which can be used to identify patterns of fraud in healthcare claims. Related terms: cluster analysis, data mining.
Concept #
Segmentation is the process of dividing a dataset into smaller, more homogeneous groups based on shared characteristics. In the context of healthcare fraud investigation, segmentation can be used to group providers or claims together based on shared characteristics, such as the types of procedures they bill for or the demographics of their patients. By identifying these groups, investigators can identify patterns of fraud that may not be apparent when looking at individual claims or providers.
Example #
A segmentation algorithm may identify a group of providers who all bill for a high number of a specific procedure, but who are located in different parts of the country. This may indicate a fraud scheme that is being carried out by multiple providers.
**Statistical analysis #
** The process of using mathematical techniques to analyze data and identify patterns, which can be used to identify fraud in healthcare claims. Related terms: data analysis, data mining.
Concept #
Statistical analysis is the process of using mathematical techniques to analyze data and identify patterns. In the context of healthcare fraud investigation, statistical analysis can be used to identify patterns of fraud in a dataset of healthcare claims. Statistical analysis techniques can include hypothesis testing, regression analysis, and time series analysis.
Example #
A statistical analysis algorithm may be used to identify a pattern of claims that are being submitted by a specific provider, but that are not consistent with the patterns of other providers. This may indicate fraudulent activity by the provider.
**Time series analysis #
** A type of statistical analysis used to identify patterns in data that are collected over time, which can be used to identify fraud in healthcare claims. Related terms: trend analysis, seasonal analysis.
Concept #
Time series analysis is a type of statistical analysis used to identify patterns in data that are collected over time. In the context of healthcare fraud investigation, time series analysis can be used to identify patterns of fraud in a dataset of healthcare claims that have been collected over a period of time. Time series analysis techniques can include trend analysis, seasonal analysis, and autoregressive integrated moving average (ARIMA) models.