How insurance companies benefit from overcoming data challenges with MongoDB

As any organization in the insurance industry will know, there is a growing need to effectively manage and store huge amounts of information, including claims data, customer information and policy records. 

Poor data quality can result in wasted resources, negative impacts to reputation and damage to the reliability of analytics. Research by Precisely found 70% of data and analytics professionals who struggle to trust their data say data quality is the biggest issue.

Getting this right is particularly important to the insurance sector, particularly as the UK Business Data Survey 2024 found 31% of businesses in the finance and insurance sector analyze data to generate new insights or knowledge. That’s a greater percentage than any other sector.

Insurance companies who do this successfully have an opportunity to identify patterns and trends, which can help reduce fraud, mitigate risk and improve data-driven decision making. But it also presents challenges, from data consistency and security to managing complex queries and regulatory compliance. 

Dealing with data makes NoSQL/non-relational databases, like MongoDB, a popular option for businesses needing to deliver data quality and insights at scale. They offer insurers flexibility, scalability and an efficient way to manage large databases of semi-structured and unstructured data.

In this article, I cover some of the most common challenges the insurance industry faces when dealing with data and best practices for MongoDB optimization.

What are the benefits of effective data management for insurance companies? 

MongoDB’s strengths in flexibility, performance, and security translate into tangible benefits for insurers. 

Greater efficiency 

By addressing the technical challenges outlined below, decision-makers can significantly improve the overall efficiency of their operations, MongoDB’s flexible schema allows insurers to adapt quickly to changing regulations and product offerings, reducing the time and effort required to implement changes. The ability to handle complex queries with efficient indexing and aggregation frameworks accelerates decision-making processes such as fraud detection, claims assessment, and risk profiling. This leads to faster processing times, reducing operational bottlenecks and freeing up resources for higher-value tasks.

Faster decision making 

MongoDB’s flexible schema allows insurers to adapt quickly to changing regulations and product offerings, reducing the time and effort required to implement changes. The ability to handle complex queries with efficient indexing and aggregation frameworks accelerates decision-making processes such as fraud detection, claims assessment, and risk profiling. This leads to faster processing times, reducing operational bottlenecks and freeing up resources for higher-value tasks.

Cost savings

MongoDB’s scalability reduces the need for expensive, large-scale infrastructure investments. By optimizing data storage and access patterns through indexing and schema design, MongoDB lowers the total cost of ownership by minimizing hardware requirements and improving query performance. 

The ability to manage evolving data structures without costly migrations or significant downtime further reduces operational expenses. Automating security and compliance processes also cuts down on costly manual interventions, while mitigating the financial risks of non-compliance or data breaches.

Improved customer experience 

A faster, more responsive database directly improves customer-facing services. MongoDB’s efficient data handling supports real-time updates, allowing customers to access accurate policy details and claim statuses instantly. This improves transparency and reduces the likelihood of errors or delays in critical processes like claims approval, underwriting, or premium adjustments. By delivering timely, consistent information, insurers build trust and enhance customer satisfaction, which is key to long-term loyalty and customer retention.

Common data challenges for the insurance sector

Insurance companies typically need to address some common challenges in order to reap the benefits of their data. Let’s take a closer look at the business implications of each and what you can do to overcome the issues.

1. Data consistency

Insurance systems require high data consistency for critical processes such as claims processing, customer information updates, and payments. MongoDB’s default design, focused on high availability and partition tolerance (CAP theorem), can result in “eventual consistency,” which may be insufficient for real-time insurance operations.

Business implications

Customer experience: Delayed or inconsistent claims processing negatively impacts customer trust and satisfaction. A customer might see outdated data on their policy or claim status, leading to frustration and lost loyalty.

Operational efficiency: Inconsistent data may lead to more manual checks and corrections, increasing the workload for staff and reducing efficiency.

Cost savings: Inefficiencies in claims processing can lead to higher operational costs. Moreover, inconsistencies can result in costly compliance issues or the need for reconciliation processes.

What you can do about it

MongoDB offers fine-grained control over data consistency with features like “Read Concern” and “Write Concern.” These tools can be configured to ensure that insurance-critical operations prioritize consistency without sacrificing performance. 

Read concern: Ensures data is read from a replica that reflects the majority of recent updates, reducing the risk of outdated data. For insurance applications, using readConcern: “majority” ensures that data is read from a node where writes have been replicated to the majority of nodes, ensuring higher consistency.

Write concern: Guarantees that a write operation is confirmed only after being replicated to a specified number of replica nodes, preventing data loss during transactions. For critical insurance data such as claims, you can set the writeConcern: { w: “majority” } to guarantee that writes are replicated before confirming success.

Ready to get technical? Here’s an example of how the code might look:

// Ensuring writes are confirmed by the majority

db.policies.update(

    { policyId: “POL12345” },

    { $set: { status: “active” } },

    { writeConcern: { w: “majority”, wtimeout: 5000 } }

);

// Reading with strong consistency for critical operations

db.claims.find(

    { claimId: “CLAIM12345” },

    { readConcern: { level: “majority” } }

);

2. Security and regulatory compliance

Insurance companies handle sensitive data such as personal health information and financial details, which are subject to stringent regulations (e.g., HIPAA, GDPR). Without robust security controls, insurers are vulnerable to data breaches and compliance violations.

Business implications

Risk mitigation: A robust data security strategy prevents data breaches, which could result in hefty fines, litigation, and reputational damage.

Customer trust: Secure handling of customer data reinforces trust, which is critical for customer retention and long-term business growth.

Operational cost efficiency: Automated auditing and security processes reduce the need for manual oversight, freeing up resources and reducing the risk of human error.

What you can do about it

MongoDB offers multiple security features to meet insurance data privacy and regulatory requirements. It supports encryption at rest and in transit, ensuring sensitive data is protected from unauthorized access

It also offers Role-Based Access Control, which allows insurers to define strict access controls, ensuring only authorized personnel can view or manipulate sensitive claims or customer information.

MongoDB Enterprise provides auditing capabilities that track access to and modifications of sensitive data, ensuring compliance with industry regulations.

The code might look like this:

// Role-Based Access Control for claims handlers

db.createRole({

    role: “claimsHandler”,

    privileges: [

        { resource: { db: “insuranceDB”, collection: “claims” }, actions: [“find”, “update”] }

    ],

    roles: []

});

// Enabling encryption at rest

security:

  enableEncryption: true

  encryptionKeyFile: /etc/mongodb/keyfile

3. Query performance and complex queries

Insurers rely on complex queries for tasks like risk assessment, fraud detection, and customer profiling. MongoDB’s flexible schema, while ideal for handling large datasets, can lead to performance issues when running complex aggregations or queries on massive datasets.

Business implications

Customer experience: Faster query performance translates into quicker claim processing, real-time risk assessments, and timely customer service, all of which enhance the customer experience.

Operational efficiency: Optimized queries reduce the load on infrastructure and minimize the need for additional hardware, lowering IT costs.

Cost savings: Faster query performance reduces server costs by minimizing processing times and infrastructure needs. It also enables more timely insights for underwriting or fraud detection, reducing losses.

What you can do about it

Creating indexes on frequently queried fields significantly improves query speed and efficiency. MongoDB’s powerful aggregation framework allows insurers to perform sophisticated data processing on the server side, reducing the load on the client and speeding up analysis tasks. 

Vector search, which uses advanced mathematical models to understand the context and meaning of queries, is another tool that is transforming enterprise data retrieval. 

Code example:

// Indexing key fields for faster queries

db.policies.createIndex({ customerId: 1 });

db.claims.createIndex({ status: 1, claimDate: -1 });

// Using aggregation for claim analysis

db.claims.aggregate([

    { $match: { status: “approved”, claimAmount: { $gte: 10000 } } },

    { $group: { _id: “$policyId”, totalClaims: { $sum: “$claimAmount” } } },

    { $sort: { totalClaims: -1 } }

]);

4. Managing schema evolution

As insurers introduce new products, adjust regulations, or change business requirements, the underlying data structures must adapt accordingly. MongoDB’s schema-less design provides flexibility but can lead to difficulties in managing schema changes over time, especially as documents in a collection evolve.

Business implications

Future-proofing data infrastructure: Flexible schema management allows businesses to adapt quickly to new product lines, regulatory changes, and customer needs without costly database redesigns.

Operational efficiency: Managing schema changes efficiently reduces the time required to onboard new products or implement regulatory updates, accelerating time-to-market.

Cost control: A flexible schema reduces the need for frequent database migrations, saving costs on development and downtime.

What you can do about it

MongoDB allows insurers to handle schema changes more gracefully through Schema Versioning. Adding a version field to documents helps manage different versions of a schema within the same collection, ensuring backward compatibility as documents evolve. MongoDB also allows validation rules at the collection level, ensuring that new or updated documents conform to expected structures.

Code example:

// Implementing schema versioning

db.policies.update(

    { policyId: “POL54321” },

    { $set: { version: 2, coverageDetails: { newField: “value” } } }

);

// Enforcing validation rules for the ‘policies’ collection

db.createCollection(“policies”, {

    validator: { $jsonSchema: {

        bsonType: “object”,

        required: [ “policyId”, “customerId”, “status” ],

        properties: {

            policyId: { bsonType: “int” },

            customerId: { bsonType: “int” },

            status: { enum: [ “active”, “expired”, “pending” ] }

        }

    }}

});

Overcoming data challenges to future-proof your organization

By addressing these technical and business challenges, IT directors, Heads of Engineering, Heads of Customer Experience and other business leaders can significantly improve the overall efficiency of their operations, reduce costs, and improve customer experience. For insurance companies looking to future-proof their data infrastructure, MongoDB presents a powerful, adaptable solution that aligns with the evolving demands of the industry.