AI-BASED FRAUD DETECTION FOR HEALTHCARE INSURANCE INDUSTRY
Read the case story about AI based Fraud Detection for Healthcare Insurance Industry
Table of Content
- What is Fraud?
- The Market Size and Growth of Insurance and Fraud Marketplaces
- Fraud Cases
- The Consequences of Fraud Detection Cases
- How B2Metric Helps?
- How to work it in such a case?
What is Fraud?
Fraud is one of the challenging problems for the insurance industry. It impacts not only on every type of insurance from non-life insurance to health insurance but also on their customers due to added payments and costs. Furthermore, it also affects society in general because insurance fraud could be used to fund criminal activity. Day by day the number of cases of it has been increasing, especially after Covid-19. Thus, this increase in insurance fraud cases has resulted in huge financial loss annually for both insurers and customers. This is the reason the problem should be solved.
The Market Size and Growth of Insurance and Fraud Marketplaces
Healthcare fraud is an international issue. There has been a significant rise in the population seeking health insurance in different countries all around the world. For instance, as per Statista, 297 million people in the United States had health insurance in 2020, an increase from approximately 257 million health insured people in 2010. The global fraud detection and prevention market size is USD 25.66 B in 2021. The market is expected to grow from USD 30.65 billion in 2022 to USD 129.17 billion in 2029. AI has an important role for healthcare fraud detection. It may enable new opportunities for the both global and local fraud analytics industry.
There are different types of fraud cases all around the world. For example, fraud can be committed in insurance, banking and healthcare sectors etc. The cases include providing incorrect and incomplete information in applications for insurance or answers on an insurance form or submitting claims for a loss based on untruthful situations. Drug fraud, medical fraud and insurance fraud are in healthcare fraud cases.
What is far more common, however, is the type of fraud that the private healthcare sector encounters regardless of the jurisdiction. A sample of these are:
• Unnecessary medical intervention/tests etc
The Consequences of Fraud Detection Cases
Insurers take action against those that commit fraud. The results can include:
• Non-payment of claims
• Cancellation of the insurance policy
• The insurer seeking costs incurred (for example for experts in assessing the claim)
• Subsequently being unable to obtain insurance and other financial services
• Making a report of the case to the police for further investigation
• A criminal record
How B2Metric Helps?
The approach to identifying insurance fraud also differs among countries. One of the ways to reduce fraud detection cases is the adaptation of machine learning techniques to allow for developing predictive accuracy.
B2Metric is revolutionizing the detection and management of fraud by delivering unparalleled AI-driven insights to help health plans take action with a high speed and accuracy. With B2Metric, your plan can identify the highest value cases in a short period of time and deliver on top priorities in areas such as: medical, opticianry and pharmacy.
B2Metric Auto-ML provides insurance specific features for the industry such as customer risk scoring, churn prediction and fraud detection problem solutions. One of the products of B2Metric called B2ML Studio Hunter enables users to automatically discover, visualize and narrate important findings (such as correlations, exceptions, clusters, drivers and predictions) in datasets, without requiring people to build data visualizations, create models or write algorithms. Augmented capabilities are differentiating features in platforms across D&A. They’re also a key factor accelerating the convergence between analytics and data science.
How to work it in such a case?
At first, the data scientists analyze the data and create project planning for the MVP version of B2Metric Hunter Fraud Detection platform. It’s a SAAS online platform that can be instantly implemented as an on-premises solution to any Healthcare Insurance companies.
First of all, we try to understand the variables representing various behaviors and properties of optics and their relationships with each other. It also has some logic errors like negative customer ages or negative brute prices, so they all have to be cleaned up before exploratory data analysis and ML modelling.
After that, the scientists have ingested the datasets into B2Metric ML Studio Data Ingestor then run Fraud models and the details of models are explained in detail in the sections below.
B2Metric Auto-ML Hunter fraud use case user flow diagram indicates briefly the user personas and flows diagram below.
B2Metric Auto-ML Fraud models training, retraining and prediction mechanism works the system below. This training module runs these following steps:
B2Metric Auto ML Hunter Fraud Alert Platform Design
- The platform allows you to upload data to be modelled from your database like Amazon DynamoDB, Oracle, PostgreSQL, MsSQL, MySQL, Stripe, Google Analytics, Google Big Query, SAS or from your computer as a CSV, XLS(X), TXT, PARQUET file.
- You can run the supervised based classification or regression models, unsupervised based clustering models and anomaly detection models on the platform.
During the ML modelling and statistical insights generation for the use case of DATA SCIENCE/ DATA MANIPULATION IN ORDER TO GAIN INSIGHTS FROM THE MARKET with the data of ‘Historical data of reimbursement requests from opticians to insurance companies’. Algorithms and conclusions of technical details are listed below.
1.Exploratory Data Analysis: Referring to the critical process of performing initial investigations on data so as to discover patterns,to spot anomalies,to test hypotheses and to check assumptions with the help of summary statistics and graphical representations.
2.Cluster Analysis: Identify its various customer segments, and then conduct cluster analysis to see if any such segments share similar characteristics (e.g. objectives, pain points, perceptions, demographics, preferences, etc.) that are distinctly different from other segments.
3. Factor Analysis: Shed light on what combination of aspects, characteristics or priorities are most important to a certain type of customers (group).
4. Discriminant Analysis: Predicting membership in a group (or population or cluster) based on measured characteristics of other variables. Subsequently, it is requested to be based on the previous results (market analysis, etc.) to: Extract outliers from the dataset (according to several axes of analysis). Carry out an in-depth analysis of these data to identify fraud movements
5. Fraud Detection using Multiple Regression: Predict the value of a variable based on changes to two or more variables. For the reason that the target feature is categorical, it will be a classification model.
6. Adaptive Learning & Continues Learning of Fraud Models: Continues learning and auto retraining for fraud models is one of the crucial points for the new clients, new opticians and transactions data generated. Here is the system architecture below has designed for Almersy’s Fraud modelling adaptively learning solution in B2Metric Auto-ML Hunter framework.
As B2Metric, we apply software quality assurance which is a process which works parallel for the development of our AI based AutoML systems and Fraud platform software. It focuses on improving the process of development of software so that problems can be prevented before they become a major issue. Software Quality Assurance is a kind of Umbrella activity that is applied throughout the software process.
Software Quality Assurance has:
- A quality management approach,
- Formal technical reviews,
- Multi testing strategy,
- Effective software engineering and data science technology,
- Measurement, reporting and A/B, unit testing mechanism.
These are the possible risks that can happen during the projects but we have already found suitable ways to handle these problems within our team.
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