The Importance of Data Ethics in Insurance

4 minutes, 38 seconds read

Published on May 9, 2020

Updated on Oct 1, 2020

In a world where digitization is rapidly making its way into our everyday life, challenges come as an add on package. Amongst many others, Data and Privacy are the most raised concerns. Be it any sector, consumers need assurance that their data is safe with the company. Insurance is one of the sectors that banks highly sensitive data of its customers. Data breaches, wrongful processing of customer data, using the personal information of customers without consent, etc. puts a dent in the company’s image. We have seen the scandal caused by the data breach at Facebook.

In September 2018, Facebook announced that an attack on its computer network exposed the personal data of over 50 million users. According to Facebook, hackers were able to gain access to the system by exploiting a vulnerability in the code used for the ‘View as’ feature. The attackers stole the ‘access tokens’, which took over the user’s accounts and got access to other services.

The need for data protection in Insurance

‘Trust’ is an essential part of the Insurance industry, failure of which can lead to loss of customer loyalty and subsequently loss of business. Insurance companies need to process customer data for calculating premiums, customized policies, claims, etc.

In India, The Information Technology Act, 2000 (IT Act) and the Information Technology (Reasonable Security Practices and Procedures and Sensitive Personal Data or Information) Rules, 2011 (SPDI Rules) set out the general framework for data protection. However, given the nature of the Insurance business and intermediaries, the Insurance Regulatory and Development Authority of India (IRDAI) has prescribed an additional framework for the protection of policyholder information and data, which Insurers need to follow in addition to the general framework under the IT Act.

As India moves towards digitization, the IRDAI and IT Act are not enough to ensure proper compliance of data. The nation needs a comprehensive Data Protection law along with a governing body to oversee the implementation of the law. A draft of the Data Protection Bill was introduced in July 2018 which later was tabled on 11th December 2019 by the Indian Parliament. However, the Bill is being analyzed by a Joint Parliamentary Committee (JPC) in consultation with various groups. Indeed a groundbreaking step for our country, but it might have dangerous implications. The bill gives power to the government to access customers’ private data or government agency data on grounds of sovereignty or public order.

The question is that will the government adhere to data ethics while processing this private data? The answer is unknown, but this step puts Insurance companies and TPAs under pressure to take steps towards data protection.

How can Insurers ensure data ethics

To ensure the privacy of customers and use data effectively, Insurers and intermediaries can adhere to the following measures-

Implementing risk management and IT security policies

Insurance is the most targeted industry by hackers. Also, with a lot of mobile workforce handling portable devices, monitoring data can be challenging. Companies need to protect data on the endpoint. The software should be installed on the systems directly and encrypting the data on portable devices such as USBs and hard drives. Growing risks in cybersecurity increased demand for Cyber Insurance policies. Cyber Insurance products are another such medium which helps in mitigating risks in the event of a cyber attack or a breach.

According to a report by Data Security Council of India on Cyber Insurance in India, the Cyber Global Insurance market is prone to grow from a CAGR of 27% from 4.2 Bn to 22.8 Bn from 2017 to 2024. Insurers can also take measures such as setting-up internal policies and regular audits to keep a check on the data compliance.

Consent mechanism for using policy holder’s data

A company might need data for internal purposes such as upgrading services for its customers. In such cases, companies should mention the purpose and set-up a proper mechanism for taking consent. Insurers can also give a status update on the project for which they used the customer data to keep the trust factor intact.

Using data-centric technologies

Human errors are unavoidable. But a second step validation can be set-up using disruptive technologies such as quantum computing, blockchain, Artificial Intelligence. These technologies not only ensure data security but also help in utilizing the customer data most efficiently.

Ensuring transparency with customers.

In the event of a data breach, the company must inform the customers and take steps to contain the damage. In 2014, Anthem Healthcare was attacked which led to a data breach. They immediately sent out alerts to their customers informing of the possibility of their data leak. Subsequently, they also informed the media after 8 days. Furthermore, they contacted the FBI regarding the attack and hired Mandiant, a cybersecurity firm to assess the level of damage. As an essential part of data ethics, it is equally important to own the mistake and take appropriate measures.

Merits of the case: data ethics in Insurance

Data breaches can occur due to superficial monitoring of data flow; lack of accurate privacy design; poor internal audits; failure in conducting resistance tests; use of outdated security systems.

The present crisis of COVID-19 has made data all the more vulnerable. As many employees are working from home, data security compliance has been an issue. Data protection bills and authority can act as watchdogs in the Insurance sector to avoid breaches. The Insurance sector should not see the law as a burden for additional compliance but rather an opportunity for long term customer trust.

If you want to know more about the importance of data, and how to prevent data loss in other organizations that provide financial services, do read Financial services businesses must protect PII. DLP can help.

Generic AI is no longer enough. Domain-specific AI is the new enterprise advantage.

From hospitals to factories to insurance carriers, organizations are learning the hard way: horizontal AI platforms might be impressive, but they’re often blind to the realities of your industry.

Here’s the new playbook: intelligence that’s narrow, not general. Context-rich, not context-blind.
Welcome to the age of domain-specific AI agents— from underwriting co-pilots in insurance to care journey managers in hospitals.

Why Generalist LLMs Miss the Mark in Enterprise Use

Large language models (LLMs) like GPT or Claude are trained on the internet. That means they’re fluent in Wikipedia, Reddit, and research papers; basically, they are a jack-of-all-trades. But in high-stakes industries, that’s not good enough because they don’t speak insurance policy logic, ICD-10 coding, or assembly line telemetry.

This can lead to:

Hallucinations in compliance-heavy contexts
Poor integration with existing workflows
Generic insights instead of actionable outcomes

Generalist LLMs may misunderstand specific needs and lead to inefficiencies or even compliance risks. A generic co-pilot might just summarize emails or generate content. Whereas, a domain-trained AI agent can triage claims, recommend treatments, or optimize machine uptime. That’s a different league altogether.

What Makes an AI Agent “Domain-Specific”?

A domain-specific AI agent doesn’t just speak your language, it thinks in your logic—whether it’s insurance, healthcare, or manufacturing.

Here’s how:

Context-awareness: It understands what “premium waiver rider”, “policy terms,” or “legal regulations” mean in your world—not just the internet’s.
Structured vocabularies: It’s trained on your industry’s specific terms—using taxonomies, ontologies, and glossaries that a generic model wouldn’t know.
Domain data models: Instead of just web data, it learns from your labeled, often proprietary datasets. It can reason over industry-specific schemas, codes (like ICD in healthcare), or even sensor data in manufacturing.
Reinforcement feedback: It improves over time using real feedback—fine-tuned with user corrections, and audit logs.

Think of it as moving from a generalist intern to a veteran team member—one who’s trained just for your business.

Industry Examples: Domain Intelligence in Action

Insurance

AI agents are now co-pilots in underwriting, claims triage, and customer servicing. They:

Analyze complex policy documents
Apply rider logic across state-specific compliance rules
Highlight any inconsistencies or missing declarations

Healthcare

Clinical agents can:

Interpret clinical notes, ICD/CPT codes, and patient-specific test results.
Generate draft discharge summaries
Assist in care journey mapping or prior authorization

Manufacturing

Domain-trained models:

Translate sensor data into predictive maintenance alerts
Spot defects in supply chain inputs
Optimize plant floor workflows using real-time operational data

How to Build Domain Intelligence (And Not Just Buy It)

Domain-specific agents aren’t just “plug and play.” Here’s what it takes to build them right:

Domain-focused training datasets: Clean, labeled, proprietary documents, case logs.

Taxonomies & ontologies: Codify your internal knowledge systems and define relationships between domain concepts (e.g., policy → coverage → rider).
Reinforcement loops: Capture feedback from users (engineers, doctors, underwriters) and reinforce learning to refine output.
Control & Clarity: Ensure outputs are auditable and safe for decision-making

Choosing the Right Architecture: Wrapper or Ground-Up?

Not every use case needs to reinvent the wheel. Here’s how to evaluate your stack:

LLM Wrappers (e.g., LangChain, semantic RAG): Fast to prototype, good for lightweight tasks
Fine-tuned LLMs: Needed when the generic model misses nuance or accuracy
Custom-built frameworks: When performance, safety, and integration are mission-critical

Use Case	Reasoning
Customer-facing chatbot	Often low-stakes, fast-to-deploy use cases. Pre-trained LLMs with a wrapper (e.g., RAG, LangChain) usually suffice. No need for deep fine-tuning or custom infra.
Claims co-pilot (Insurance)	Requires understanding domain-specific logic and terminology, so fine-tuning improves reliability. Wrappers can help with speed.
Treatment recommendation (Healthcare)	High risk, domain-heavy use case. Needs fine-tuned clinical models and explainable custom frameworks (e.g., for FDA compliance).
Predictive maintenance (Manufacturing)	Relies on structured telemetry data. Requires specialized data pipelines, model monitoring, and custom ML frameworks. Not text-heavy, so general LLMs don’t help much.

Strategic Roadmap: From Pilot to Platform

Enterprises typically start with a pilot project—usually an internal tool. But scaling requires more than a PoC.

Here’s a simplified maturity model that most enterprises follow:

Start Small (Pilot Agent): Use AI for a standalone, low-stakes use case—like summarizing documents or answering FAQs.

Make It Useful (Departmental Agent): Integrate the agent into real team workflows. Example: triaging insurance claims or reviewing clinical notes.
Scale It Up (Enterprise Platform): Connect AI to your key systems—like CRMs, EHRs, or ERPs—so it can automate across more processes.

Think Big (Federated Intelligence): Link agents across departments to share insights, reduce duplication, and make smarter decisions faster.

What to measure: Track how many tasks are completed with AI assistance versus manually. This shows real-world impact beyond just accuracy.

Closing Thoughts: Domain is the Differentiator

The next phase of AI isn’t about building smarter agents. It’s about building agents that know your world.

Whether you’re designing for underwriting or diagnostics, compliance or production—your agents need to understand your data, your language, and your context.

Ready to Build Your Domain-Native AI Agent?

Talk to our platform engineering team about building custom-trained, domain-specific AI agents.

Further Reading: AI Code Assistants: Revolution Unveiled

The Importance of Data Ethics in Insurance

The need for data protection in Insurance

How can Insurers ensure data ethics

Implementing risk management and IT security policies