Insurance Fraud Detection Using Machine Learning: What You Should Know | News (2023)

Fraudulent insurance claims cost insurance companies and consumers in Europe €13bn annually. Insurance fraud is rife, especially in the property, automotive, and healthcare sectors. Insurance companies are recognizing the need to adopt digital innovations urgently to reduce instances of fraudulent claims and better prepare for future threats.According to a report by Forrester, global investments in Insurtech exceeded $15B in 2021.

How can AI and machine learning help your organization detect insurance fraud more effectively?

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How to Detect Insurance Fraud

Investigating fraudulent claims iscostlyand time-consumingfor insurers. It is physically impossible for insurance companies to do a thorough check ofthe thousands of claims that enter their systems daily.

Early computerized systemscoulddo so much – only allowingrudimentary analysisandsearch for fraudulent indicators known as red flags.A biglimiting factor with this system is thatfraudulent claims had to fit into a particular template or else they would not be recognized.Therefore, new technology is a blessing to insurance companies, providinggame-changing solutionsto enhance and automate processes along the insurance value chain.

Nordic insurance companies have alreadymodernizedtheir fraud detection processes with RPA, whichassists in verifying information located in different sources to detect the right data. Using RPA, an insurance company recorded adecreased claims cycle timefrom 6–10 minutes to 90 seconds.

That being said, howdo insurers ensure the utmost accuracy in filtering out fraudulent claims?This is where machine learning comes in.

MachineLearning to theRescue

AI is known for simplifying menial tasks and freeing human agents to do more complex analyses. In terms of insurance fraud detection, machine learning applies aspects of AIto give systems the ability to improve from experience with no extra programming byanalyzinglarge, labeled data sets.

Machine learning can improve fraud detection techniques in the following ways:

  • Processes data in a short period of time.
  • Highlights where connections can exist between various factors that human eyes cannot detect.
  • Applies various data analysis techniques to allow the discovery of new fraud schemes.

Although it borrows underlying principles found in statistical models, the main focus of machine learning is producing predictions. These predictions are based on the analysis of known outcomes, known as “ground truth.” Machine learning also can search for fraud in unstructured and semi-structured data such as claims notes and documents.

Furthermore, machine learning can prevent fraud by detecting suspicious patterns in claims processing and customer background checks, which can potentially save insurers a lot of money. Since investing in a fraud prevention system, this Turkish insurer saved $5.7 million and recorded a 210% increase in ROI.

TheInsurance Fraud Detection Dataset

The ground truth provides a label that identifies the outcome of each claim based on a historical dataset of insurance claim information and patterns.While there are varying outcomesbetween insurance claims, thelabels are generally divided into “valid” claimsor “fraudulent” claims.

HealthInsurance Fraud Detection Dataset

In thiscase study, there are close to amillion claims records with more than 20 variables.Claims have been assessed and labelledas normal and flagged for possible fraud.Claims that were flagged showed signs ofsuspicious policy profiles or malicious agencies, claims, or hospital-related fraudulentbehavior.A machine learning model was created, a so-called binary classifier, to detect the two labels as accurately as possible. A supervised learning approachwasapplied since the data was already labelled.

Auto InsuranceDetection Dataset

Thisprojecthighlights the challenge ofbuilding a model that can detect fraud,wherelegitimate insurance claims far outweigh the fraudulent ones. This problem is known as imbalanced class classification.The data set consists of 1,000 auto incidents and insurance claims which had a total of 39 variables before any cleaning or feature engineering.Specific types of machine learning models, such as neural networks, natural language processing, and network graph analyticswere also utilized in this dataset.

Anomaly DetectioninInsurance Fraud

Deep anomaly detection isapopular form of machine learningthat can be utilized by the insurance industry to detect fraud.In claims processes, anomaly detection will analyze genuine claims by consumers. It then forms a model of what a typical claim looks like which isthenapplied to larger data sets.Insurers can also use anomaly detectionto identify the suspicious behavior of users on an insurer’s network.In addition,deep anomaly detectioncanbe combined with other AI applicationssuch as predictive analysis to further automate the fraud detection process.

Insurance Fraud Detection Using Big Data Analytics

The Digital Insurer recommendsa 10-step approachto implement analytics in frauddetection:

  1. Perform SWOT–A SWOTanalysis of existing fraud detection frameworks and processes to identify gapsmust be conducted.
  1. Build a dedicated fraud management team– It is important to have a team, not an individual, handling fraud claims.
  1. Whether to build or buy–Companies must evaluate whether they have the capacity and resources to build their own analytics framework or whether they need to engage an external vendor.
  1. Clean data–Remove inefficiencies andredundancies andintegrate siloed databases.
  1. Come up with relevant business rules–Companies should leverage existingdomain expertise and experienced resources.
  1. Come up with pre-determined anomaly prediction thresholds–Companies should provide inputs for thresholdvalues for different anomalies.
  1. Use predictive modelling–An effective fraud detection method is one that uses data mining tools to build models that produce fraud propensity scores linked to unidentified metrics.
  1. Use of SNA–Effective identification of fraud activities by modelling relationships between various entities involved in the claim.
  1. Build an integrated case management system leveraging social media–This allowsinvestigators to capture all key findings that are relevant to an organizationincluding claims data and social media data.
  1. Forward thinking analytics solutions– Insurers should always be on the huntforadditional sources of data to improve existing fraud detection systems.

An insurance company’s efficacy in distinguishing between valid and fraudulent claims plays a big part in determining its financial strength, allowing optimal compensation and supportforits customers.

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