Detecting Healthcare Fraud with Terno AI: A No-Code Data Science Journey

Introduction: Fighting Fraud with Data Science

Healthcare fraud costs billions globally and undermines trust in medical systems. Detecting such fraud isn't easy—it requires analyzing vast amounts of claim data, patient records, reimbursements, and provider behavior.

This is where data science comes in. By leveraging machine learning, we can surface unusual patterns and outliers that might indicate fraudulent behavior. But what if we could do this entire workflow—from raw data to fraud prediction—without writing a single line of code manually?

That’s the power of Terno AI, an AI assistant that turns plain English prompts into full-fledged machine learning pipelines.

Getting Started: Uploading Data and Understanding the Challenge

The dataset used for this project came from Kaggle as a ZIP archive containing four interlinked CSV files:

Once all this information is gathered, it’s fed into several machine learning models. Some models are simple and quick, like logistic regression, while others are more powerful and complex, like Random Forest and XGBoost.

Together, these models estimate how likely it is that a claim is fraudulent. For example, the system might say:

• Train.csv- Labels whether a provider is potentially fraudulent
• Train_Beneficiarydata.csv- Demographic details of beneficiaries
• Train_Inpatientdata.csv- Inpatient claim records
• Train_Outpatientdata.csv- Outpatient claim records

After uploading healthcarefraud.zip to Terno AI, I prompted:

Prompt: Perform exploratory data analysis on the Healthcare Provider Fraud Detection Analysis Dataset, including summary statistics, missing value analysis, duplicate value analysis, and visualizations for key features.

Terno AI unpacked the archive, loaded all four files, and automatically inspected their structure, readying them for full-scale EDA.

Exploratory Data Analysis (EDA)

Terno AI performed the following tasks:

Summary Statistics

A comprehensive breakdown of means, medians, standard deviations, and value ranges for key features across all datasets was generated.

Missing & Duplicate Value Analysis

Terno AI detected:

• A few missing values in certain claim columns
• Multiple duplicate rows, which were flagged for review

Visualizations for Key Insights

Terno AI created insightful plots, including:

Feature Engineering and Label Merging

To build predictive models, I prompted:

Prompt: Perform the best models on this data and address missing values, duplicates, and encoding. Then, evaluate the models using all applicable metrics. Finally, show the inference visualisation and conclude how someone can detect and catch healthcare provider fraud, including the major signs.

Terno Response:

1. Understanding the insights:

Terno handled the data preparation very systematically. Here’s what it did — and why each step matters:

• Removed exact duplicate rows: Duplicate entries can skew the analysis or confuse the model, so removing them helps improve data quality.
• Dropped Claim_ID and Claim_Description: Claim_ID is just a unique identifier (not useful for prediction), and Claim_Description is free-text, which needs special natural language processing — so it was set aside for now.
• Parsed the date columns and calculated Policy_Duration_Days, Instead of using raw dates, Terno extracted a useful numerical feature showing how long a policy was active. Then it removed the original date columns to avoid redundancy.
• Removed rows where Fraud_Label (our target) was missing, Since we can’t train a model without knowing the true label, these rows were safely discarded.
• Handled missing values: For numeric columns, missing values were filled using the median (which is robust to outliers); for categorical columns, the mode (most frequent value) was used.
• Converted all categorical columns into one-hot encoded format: This transforms text-based data into a numerical format the model can understand.
• Standardized numeric features: This scales all numeric values to have zero mean and unit variance, which helps many models perform better.
• Checked final data types and verified no missing values remain — A final sanity check to ensure the dataset is clean and ready.

Terno then saved the cleaned dataset, which will be used in the next steps of the machine learning pipeline. This response gave me confidence that the data is now well-prepared and ready for deeper exploration and modeling.

2. Exploratory Data Analysis(EDA)

What it means:

Now that our data is clean and organized, EDA is like being a detective. We dig into the data to find patterns, spot relationships between different variables, and uncover any hidden secrets. The goal is to understand the story the data is telling us, especially about what might indicate a fraudulent claim. This understanding helps us build a more intelligent model.

What I did:

With the cleaned dataset from the previous step, I was ready to have Terno AI perform the analysis. I didn't need to write any code for plotting graphs or calculating correlations; I just needed to ask the right questions.

Here is the prompt I gave to Terno AI:

Prompt:

Now that the dataset is cleaned and prepared, let’s move on to

Step 2: Exploratory Data Analysis (EDA).

Please help me understand the key patterns, distributions, and relationships in the data. Here’s what I’d like you to include (but feel free to add anything else useful):

i) Summary statistics (mean, median, min, max, std) for the numerical columns like Customer_Age, Claim_Amount, Claim_Frequency, etc.

ii) Value counts and distribution plots for categorical features like Policy_Type, Incident_Severity, Education Level, etc.

iii) A correlation matrix (including correlation of features with the target column Fraud_Label)

iv) Class imbalance check for Fraud_Label — how skewed is the data? v) Identify any outliers in key numerical columns (e.g., Claim_Amount, Income Level, etc.)
vi) Highlight any unusual trends or patterns related to fraud cases (e.g., are certain incident types more likely to be fraudulent?)

vii) Add visualizations where relevant — histograms, bar charts, box plots, or heatmaps — to make the insights easier to understand. Please explain the insights you find in simple language. We’ll move to feature selection, engineering, and model building after this step.

Terno Response:

Understanding the Insights

Terno's analysis gave us a clear picture of the data.

• Fraud is Rare, But Present: Only about 15% of the claims in our dataset are fraudulent. This is a classic "imbalanced dataset," and it's a crucial piece of information. It means our model will need to be smart enough to find the few "bad apples" in a large barrel of good ones.
• "Severe" Incidents are a Red Flag: Claims marked as "Severe" have a slightly higher chance of being fraudulent (about 16%) compared to "Minor" or "Moderate" ones. This is a great clue for our model.
• No Single "Smoking Gun": When we look at the numbers, no single feature like Customer_Age, Income Level, or Claim_Amount screams "fraud" on its own. The correlation with fraud is very low for all of them. This tells us that fraud is likely a complex pattern involving multiple factors working together.
• Well-Behaved Data: The data is quite clean and doesn't have wild, extreme outliers. This is great news because it means we can trust the data and won't need to do complex cleaning to remove crazy values.
• Good Variety in Data: The dataset has a balanced mix of customers, policy types (Auto, Home, Health), and education levels. This diversity is good because it means our model will learn from a wide range of scenarios.

In short, while there's no single easy predictor, we've uncovered important patterns (like incident severity) and confirmed that we need to handle the class imbalance. We are now set up for the exciting part: Feature Engineering and Selection.