In the insurance industry, like in any other sector, data is the lifeblood. But not all data is created equal.
In modern insurance companies data lives in different databases, IT systems and across various teams and business units. In order to manage data quality and integrations, many enterprise organizations turn to master data management.
So what exactly is this master data, why is it so important, and how is it managed? Let's dive into the world of insurance master data!
What is master data?
Master data refers to the critical business information that is shared across multiple departments, teams, and systems within an enterprise. It represents the 'single source of truth' that defines the key entities or objects around which business activities revolve.
Insurance master data includes details about policyholders (like name, address, contact details, and demographic data), policy details (like policy number, coverage, terms and conditions), product data (like product type, premiums, and benefits), and claims data (like claim number, claim amount, and claim status).
Four types of master data
Typically, master data can be categorized into four primary domains in enterprise organizations or businesses:
- Party data: This includes data about the individuals, organizations, or groups that the enterprise interacts with. For example, customers, employees, suppliers, and partners.
- Product data: This pertains to the products or services that the enterprise offers. It includes information like product descriptions, specifications, price, and availability.
- Location data: This includes details about the geographical locations related to the enterprise. It could be the locations of the company's offices, warehouses, retail outlets, or even the regions where its customers are based.
- Asset data: This refers to the tangible or intangible assets owned by the enterprise. It could be the company's physical assets like machinery and equipment, or intangible assets like intellectual property, software, or brand value.
What insurance data should you manage as master data?
Insurance companies share the same benefits and pains of master data management as many other knowledge-intensive businesses, but there are some unique flavors.
Here's a look at the key categories of master data that should be managed by insurance companies:
1. Policyholder data
Policyholder data is perhaps the most crucial aspect of master data in the insurance sector. It includes information about the insured individuals or entities, such as their names, contact details, demographic information (like age, gender, occupation), and risk profiles. This data is vital for underwriting, policy management, claim processing, and customer service.
2. Policy data
Policy data encompasses all details related to the insurance policies issued by the company. It includes the policy number, policy type (like life, health, auto, home), coverage details, terms and conditions, premium amounts, policy start and end dates, and more. Managing this data accurately is essential for servicing policies, calculating liabilities, and managing renewals.
3. Product data
Product data refers to information about the insurance products offered by the company. This includes the product name, description, benefits, exclusions, pricing details, and any special conditions. Product data is particularly important for marketing, sales, underwriting, and compliance.
4. Claim data
Claim data is the information related to insurance claims made by policyholders. It includes the claim number, claimant details, claim amount, cause of loss, claim status, and payout details. Accurate and timely management of claim data is crucial for efficient claim processing, fraud detection, and customer satisfaction.
5. Agent/broker data
This category includes data about the insurance agents or brokers who sell the company's products. It involves their names, contact details, license information, commission structures, and performance metrics. This data is vital for managing the distribution network, calculating commissions, and monitoring agent performance.
6. Regulatory data
Insurance companies are heavily regulated, and they need to manage specific data to comply with these regulations. Regulatory data includes information required for statutory reporting, risk-based capital calculations, Solvency II compliance, and other regulatory requirements.
Benefits of data management in the insurance industry
The importance of master data in an enterprise cannot be overstated. It plays a crucial role in various aspects:
Operational efficiency: Accurate and consistent master data ensures seamless operations across different departments. It eliminates redundancies, reduces errors, and improves the overall efficiency of business processes.
Strategic decision-making: Master data provides the foundation for generating insights and business intelligence. It helps enterprises make informed strategic decisions, identify growth opportunities, and stay competitive.
Regulatory compliance: For many businesses, maintaining accurate and consistent master data is not just beneficial - it's mandatory. It aids in regulatory reporting and helps ensure compliance with legal and industry-specific requirements.
Customer experience: Master data contributes to a better understanding of customer needs and preferences, enabling businesses to tailor their offerings and deliver a superior customer experience.
Data governance: Master data forms the backbone of any data governance initiative, ensuring data quality, integrity, and security across the enterprise.
Accelerate automation: a final benefit of well-maintained master data is the ability to automate repetitive tasks and data flows. For more information, see related article on claims processing automation tools.
How to improve insurance master data quality
Improving master data quality in insurance companies requires a systematic approach and continuous effort. Below are some strategies that can help:
Implement a Master Data Management (MDM) solution
Implementing an MDM solution can centralize data management and ensure consistency across different departments and systems. These solutions typically provide features for data validation, de-duplication, and standardization, which can significantly enhance data quality.
Develop a data governance framework
A robust data governance framework provides the guidelines and standards for data management. It helps define the roles and responsibilities for data quality management, establishes data standards and policies, and ensures adherence to these standards.
Regular data auditing
Conducting regular data audits can help identify errors, inconsistencies, or gaps in the data. Once identified, these issues can be rectified, ensuring the accuracy and completeness of the data.
Looking for a way to uncover and fix master data challenges? Workfellow's Insurance Process Intelligence helps you uncover the reality of IT systems and data flows across your tasks, teams and processes.
Enhance data entry processes
Improving the processes and tools used for data entry can help reduce errors at the source. This may involve training staff on data entry standards, implementing automatic validation checks, or using tools that streamline data entry.
Data cleansing
Data cleansing involves identifying and rectifying errors, inconsistencies, or inaccuracies in the data. This process can be automated with data cleansing tools, which can identify and fix issues such as duplicate records, missing data, or incorrect entries.
Foster a data quality culture
Finally, improving data quality requires a shift in the organization's culture. Employees at all levels should understand the importance of data quality and their role in maintaining it. This can be achieved through regular training and communication.
Improving master data quality in insurance companies is a multi-faceted task. It requires a blend of technology, processes, and people to ensure that the data is accurate, complete, and reliable.
Top tips for maintaining insurance data quality
Maintaining the quality of customer master data in insurance companies is a vital undertaking that can significantly enhance operational efficiency, customer service, and strategic decision making. Here are some strategies that insurance companies can adopt to achieve this:
Prioritize customer data
Quality customer master data empowers insurance companies to make informed decisions in areas like policy underwriting, premium setting, and claim settlement. It allows companies to understand each customer's risk profile better, resulting in more accurate and fair decisions.
The ability to personalize services depends on understanding the customer deeply. Quality customer master data provides the insights necessary to create personalized insurance products and deliver bespoke customer experiences.
Align data management and risk management
Master data management plays a crucial role in mitigating risks in insurance companies. It aids in creating a single, unified view of each customer by aggregating data from various sources, which significantly enhances the accuracy and completeness of the customer profile. This comprehensive understanding helps insurers in precise risk assessment, accurate premium calculation, and informed decision-making in policy underwriting and claim settlements.
Maintain an audit trail in your data
Additionally, MDM supports regulatory compliance by ensuring accurate customer identification and by maintaining a reliable audit trail. It also reduces operational risks by minimizing errors and redundancies in data-driven processes, thereby enhancing process efficiency and reliability. Furthermore, MDM systems, through data security measures, help in mitigating data breach risks, protecting both the insurer and the customers. Therefore, MDM effectively mitigates multiple risks in insurance operations.
Visualize your data, teams and processes
When you're mapping your master data landscape remember to align your data flows with your work and process flows. This helps you prioritize the data that is directly aligned with your core insurance processes and value drivers. Insurance Process Intelligence is advanced software that can automatically give you end-to-end transparency into how work-related data flows across your key tasks, processes and workflows.
Bottom line
In conclusion, the management of master data is a critical function in insurance companies. It requires a structured approach and robust technology support in the form of Master Data Management (MDM) systems and various data management best practices. These systems and techniques help ensure that the master data is accurate, consistent, and readily accessible for all stakeholders, thereby driving the company's efficiency, compliance, and success.
Next step: read about an effortlessly intelligent approach to mapping insurance processes and IT systems. Read the new Object-Centric Task Mining ebook.
