Cleaning Messy Claims Data: An Actuarial Perspective

Claims data forms the foundation of actuarial analysis in property and casualty insurance. However, raw claims data often arrives in a state that’s far from ideal for analysis. In this comprehensive guide, we’ll explore how to transform messy claims data into a clean, reliable dataset that can support accurate actuarial analysis.

Contents

Understanding the Nature of Claims Data

Before diving into cleaning techniques, we must understand why claims data often arrives in a messy state. Insurance claims pass through multiple hands and systems before reaching actuaries. Claims adjusters focus on serving policyholders, not creating perfect datasets. Different departments might use varying conventions for recording information. Legacy systems may store data in inconsistent formats. Understanding these realities helps us approach data cleaning with appropriate strategies.

Common Sources of Data Issues

Claims data typically encounters several challenges before reaching actuarial analysis:

System Transitions: When companies upgrade or change claims systems, data often needs translation between different formats and structures. During these transitions, coding schemes might change, leading to inconsistencies in historical records.

Manual Entry Errors: Claims adjusters, focused on serving policyholders during stressful situations, might make typographical errors or use inconsistent formatting when entering data. For example, a claim amount might appear as “1000”, “1,000”, “$1000”, or “1000.00”.

Multiple Recording Systems: Large insurance companies often operate multiple claims systems, perhaps due to mergers or regional differences. Each system might use different conventions for recording similar information.

Evolving Business Practices: As companies change their claims handling procedures, the meaning and recording of certain data fields might change over time. What was once recorded in one field might later be split across multiple fields.

Establishing a Data Cleaning Framework

Initial Assessment Phase

Before beginning any cleaning operations, we need to thoroughly understand our data’s current state. This assessment phase involves several key steps:

Data Profiling: Examine the basic properties of each field in your dataset. Calculate the percentage of missing values, identify unique values, and understand the distribution of values. This step often reveals immediate red flags, such as unexpected values or patterns.

Business Rules Verification: Consult with claims departments to understand the business rules that should govern your data. For instance, certain claim types should never have values in particular fields, or specific combinations of values should never occur together.

Historical Context Research: Investigate any system changes, company mergers, or policy changes that might have affected how data was recorded over time. This historical context often explains patterns or inconsistencies in the data.

Creating Data Quality Metrics

Develop quantitative measures to assess data quality:

Completeness Metrics: Calculate the percentage of missing or null values in key fields. Track these metrics over time to identify any systematic issues in data recording.

Consistency Metrics: Measure how well the data adheres to expected patterns and relationships. For example, check if claim payment dates always follow claim report dates.

Accuracy Metrics: Compare values against known benchmarks or logical bounds. Claim amounts should fall within reasonable ranges for their line of business.

Practical Cleaning Techniques

Standardizing Formats

One of the first cleaning tasks involves standardizing how information is recorded:

Date Standardization: Claims data often contains dates in various formats. Convert all dates to a consistent format, being mindful of international date conventions. Remember that Excel might interpret dates differently from your statistical software.

Example of Date Standardization Code in Python:

def standardize_dates(df):
    """
    Standardize dates in a claims dataset, handling multiple input formats.
    Returns dates in YYYY-MM-DD format.

    Parameters:
    df (pandas.DataFrame): Claims data with date columns

    Returns:
    pandas.DataFrame: DataFrame with standardized dates
    """
    date_columns = ['report_date', 'loss_date', 'payment_date']

    for col in date_columns:
        # Handle multiple date formats
        df[col] = pd.to_datetime(df[col], format='mixed', errors='coerce')

        # Flag suspicious dates for review
        future_dates = df[col] > pd.Timestamp.now()
        ancient_dates = df[col] < pd.Timestamp('1990-01-01')

        print(f"Found {future_dates.sum()} future dates in {col}")
        print(f"Found {ancient_dates.sum()} dates before 1990 in {col}")

    return df

Monetary Value Standardization: Remove currency symbols, standardize decimal places, and ensure consistent handling of negative values. Be particularly careful with international claims that might involve different currencies.

Text Field Standardization: Create consistent formats for fields like policy numbers, claim numbers, and postal codes. This might involve padding numbers with leading zeros or removing special characters.

Handling Missing Values

Missing values require careful consideration in claims data:

Understanding Missing Value Patterns: Missing values might indicate different scenarios. A missing payment amount might mean no payment was made, or it might mean the payment information wasn’t recorded properly.

Imputation Strategies: When appropriate, develop strategies for imputing missing values. This might involve:

Using business rules (e.g., if claim status is “Closed” but final payment is missing, investigate similar claims)
Statistical methods (e.g., using regression models to estimate missing values based on other claim characteristics)
Conservative approaches (e.g., using worst-case scenarios for missing severity values in reserving calculations)

Documentation: Maintain clear documentation of how missing values are handled. This becomes crucial when other actuaries need to understand or audit your analysis.

Detecting and Handling Outliers

Outliers in claims data require special attention:

Identification Methods: Use both statistical and business-knowledge approaches to identify outliers:

Statistical methods: Z-scores, Interquartile Range rules, or more robust methods like Modified Z-scores
Business rules: Claims that violate known policy limits or typical patterns
Temporal analysis: Values that deviate significantly from historical patterns

Investigation Process: Develop a systematic process for investigating outliers:

Flag potential outliers using automated rules
Review flagged cases manually or with subject matter experts
Document decisions about keeping, modifying, or excluding outliers
Create an audit trail of all changes made

Example Outlier Detection Code:

def detect_payment_outliers(df):
    """
    Detect outliers in claim payments using multiple methods.

    Parameters:
    df (pandas.DataFrame): Claims data with payment information

    Returns:
    pandas.DataFrame: Original data with outlier flags
    """
    # Calculate statistics by line of business
    for lob in df['line_of_business'].unique():
        lob_data = df[df['line_of_business'] == lob]['payment_amount']

        # Statistical outlier detection
        Q1 = lob_data.quantile(0.25)
        Q3 = lob_data.quantile(0.75)
        IQR = Q3 - Q1

        # Flag statistical outliers
        df.loc[df['line_of_business'] == lob, 'statistical_outlier'] = (
            (lob_data < (Q1 - 1.5 * IQR)) | 
            (lob_data > (Q3 + 1.5 * IQR))
        )

        # Business rule outliers
        df.loc[df['line_of_business'] == lob, 'business_rule_outlier'] = (
            lob_data > df['policy_limit']
        )

    return df

Ensuring Data Consistency

Maintaining consistency across related fields is crucial:

Logical Relationship Validation: Check that relationships between fields make sense:

Claim closure dates should not precede opening dates
Total payments should not exceed policy limits
Claim status should align with payment history

Cross-Reference Validation: Compare data across related systems:

Policy information should match between claims and policy administration systems
Payment information should reconcile with accounting systems
Claim counts should align with regulatory filings

Advanced Cleaning Techniques

Natural Language Processing for Text Fields

Claims often include valuable information in free-text fields:

Text Standardization: Apply techniques to standardize text descriptions:

Remove special characters and extra spaces
Standardize common abbreviations
Correct common misspellings
Extract structured information from text

Example Text Cleaning Code:

def clean_claim_description(df):
    """
    Clean and standardize claim description text fields.

    Parameters:
    df (pandas.DataFrame): Claims data with description field

    Returns:
    pandas.DataFrame: Data with cleaned descriptions
    """
    # Create standard abbreviation mapping
    abbrev_map = {
        'MVA': 'motor vehicle accident',
        'WC': 'workers compensation',
        'PI': 'personal injury'
        # Add more mappings as needed
    }

    def standardize_text(text):
        if pd.isna(text):
            return text

        # Convert to lowercase
        text = text.lower()

        # Replace abbreviations
        for abbrev, full in abbrev_map.items():
            text = text.replace(abbrev.lower(), full)

        # Remove special characters
        text = re.sub(r'[^\w\s]', ' ', text)

        # Remove extra spaces
        text = ' '.join(text.split())

        return text

    df['clean_description'] = df['claim_description'].apply(standardize_text)
    return df

Time Series Consistency

Claims data often spans multiple years, requiring special attention to temporal consistency:

Temporal Pattern Analysis: Look for unexpected changes in patterns over time:

Sudden changes in claim frequency
Shifts in average claim severity
Changes in the distribution of claim types

Seasonal Adjustment: Account for known seasonal patterns:

Adjust for seasonal claim patterns
Consider the impact of catastrophic events
Account for changes in business volume

Documentation and Quality Control

Creating Data Quality Reports

Regular monitoring of data quality helps identify issues early:

Quality Metrics Tracking: Develop reports that track key quality metrics over time:

Missing value rates by field
Outlier frequencies
Data consistency measures
Error rates in key calculations

Automated Alerts: Set up automated alerts for potential data quality issues:

Unusual patterns in claim frequencies
Unexpected changes in average claim amounts
Spikes in data entry errors

Maintaining Clean Data

Establishing processes to maintain data quality over time:

Data Governance: Implement strong data governance practices:

Clear ownership of data quality
Regular quality reviews
Documentation of cleaning procedures
Training for data entry staff

Change Management: Develop processes for handling changes:

System updates
Business rule changes
New data fields
Modified procedures

Conclusion

Cleaning claims data is a crucial step in actuarial analysis. While it can be time-consuming, proper data cleaning ensures more accurate analysis and more reliable conclusions. Remember that data cleaning is not a one-time task but an ongoing process that requires regular attention and updates.

The techniques and approaches outlined in this guide provide a framework for developing robust data cleaning procedures. As you apply these methods to your own data, remember to:

Document your cleaning procedures thoroughly
Maintain clear audit trails of changes
Regularly review and update your cleaning processes
Collaborate with other departments to understand data issues
Stay current with new tools and techniques for data cleaning

By following these guidelines and continuously refining your approach, you can develop effective data cleaning procedures that support reliable actuarial analysis.

Additional Resources

To further develop your data cleaning skills:

Statistical software documentation
Data quality management frameworks
Industry working groups on data quality
Professional development courses
Programming resources for data analysis

Remember that effective data cleaning requires both technical skills and business knowledge. Continue to develop both aspects to improve your ability to handle messy claims data effectively.