AAA Quality Data

Six steps to accurate, adequate, and actionable insurance data

Data Quality Management (DQM) is critical for insurance predictive analytics projects to ensure that the data used in models is accurate, complete, and relevant.

This process enhances the reliability of predictions regarding risk assessment, pricing, customer segmentation, fraud detection, and other applications.

At Nippotica, we employ a structured six-step approach tailored for DQM, as outlined below.

1. Extraction

Data extraction involves retrieving data from various sources, which could include structured databases (like SQL databases), unstructured data (such as emails or text files), and semi-structured data sources (like JSON or XML files). The goal is to aggregate this data into a consistent format for further analysis.

\[ \text{Data Extraction:} \quad D_{\text{extracted}} = \bigcup_{i=1}^{n} D_i \]

where \(D_{\text{extracted}}\) represents the aggregated dataset and \(D_i\) represents each individual data source.

For an insurance company, this could involve pulling historical claim data, customer interaction logs from CRM systems, and policy details from a database.

2. Profiling

Data profiling assesses the data for accuracy, completeness, and consistency. This step involves statistical analysis to identify anomalies, outliers, or patterns that may indicate data quality issues.

\[ \text{Data Profiling:} \quad P(D_{\text{extracted}}) \rightarrow { \text{Statistics}, \text{Anomalies}, \text{Patterns} } \]

Profiling might reveal that 5% of claim records are missing values for the claim amount, or that there are unusual spikes in claim frequencies during certain periods, indicating potential data entry errors or fraud.

3. Cleansing

Data cleansing involves correcting or removing inaccurate, incomplete, or irrelevant data. This process is guided by the insights gained during data profiling.

\[ \text{Data Cleansing:} \quad D_{\text{clean}} = f(D_{\text{extracted}}, \text{Errors}) \]

where \(D_{\text{clean}}\) represents the cleansed dataset, and \(f\) is a function that corrects or removes errors identified in the profiling step.

Cleansing could involve imputing missing values for the claim amount based on historical averages or removing duplicate records.

4. Standardization

Data standardization converts data from various sources into a common format, applying uniform formats for dates, currencies, and other variable types.

\[ \text{Data Standardization:} \quad D_{\text{standard}} = g(D_{\text{clean}}) \]

where \(D_{\text{standard}}\) is the standardized dataset, and \(g\) is a function that applies standardization rules.

Converting all date formats to ISO 8601 (YYYY-MM-DD) or ensuring all currency values are in USD.

5. Enhancement

Data enhancement involves augmenting the dataset with additional data from external sources or derived variables to improve the predictive power of the analytics models.

\[ \text{Data Enhancement:} \quad D_{\text{enhanced}} = D_{\text{standard}} \oplus D_{\text{external}} \]

where \(D_{\text{enhanced}}\) is the enhanced dataset, \(D_{\text{standard}}\) is the standardized dataset, and \(D_{\text{external}}\) represents external data sources.

Integrating weather data to model the impact of natural disasters on claims or adding customer demographic data from third-party providers.

6. Approvals and Monitoring

The final datasets undergo a review process for approval by stakeholders, ensuring adherence to data quality standards. Continuous monitoring is established to maintain data quality over time, with metrics and thresholds defined for ongoing assessment.

\[ \text{Data Monitoring:} \quad M(D_{\text{enhanced}}) \rightarrow \text{Quality Metrics} \]

Implementing dashboards to monitor data quality metrics such as completeness, uniqueness, and timeliness, alerting the data management team to potential issues.

These above six steps, executed meticulously, form the backbone of effective Data Quality Management in insurance predictive analytics, ensuring that the data used in is accurate, adequate, and actionable.