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.

There was a time when people truly believed that humans only used 10% of their brains, so much so that it fueled Hollywood Movies and self-help personas promising untapped genius. The truth? Neuroscientists have long debunked this myth, proving that nearly all parts of our brain are active, even when we’re at rest. Now, imagine AI doing the same, providing information that is untrue, except unlike us, it doesn’t have a moment of self-doubt. That’s the bizarre and sometimes dangerous world of AI hallucinations.

AI hallucinations aren’t just funny errors; they’re a real and growing issue in AI-generated misinformation. So why do they happen, and how do we build reliable AI systems that don’t confidently mislead us? Let’s dive in.

Why Do AI Hallucinations Happen?

AI hallucinations happen when models generate errors due to incomplete, biased, or conflicting data. Other reasons include:

Human oversight: AI mirrors human biases and errors in training data, leading to AI’s false information
Lack of reasoning: Unlike humans, AI doesn’t “think” critically—it generates predictions based on patterns.

But beyond these, what if AI is too creative for its own good?

‘Creativity Gone Rogue’: When AI’s Imagination Runs Wild

AI doesn’t dream, but sometimes it gets ‘too creative’—spinning plausible-sounding stories that are basically AI-generated fake data with zero factual basis. Take the case of Meta’s Galactica, an AI model designed to generate scientific papers. It confidently fabricated entire studies with fake references, leading Meta to shut it down in three days.

This raises the question: Should AI be designed to be ‘less creative’ when AI trustworthiness matters?

The Overconfidence Problem

Ever heard the phrase, “Be confident, but not overconfident”? AI definitely hasn’t.

AI hallucinations happen because AI lacks self-doubt. When it doesn’t know something, it doesn’t hesitate—it just generates the most statistically probable answer. In one bizarre case, ChatGPT falsely accused a law professor of sexual harassment and even cited fake legal documents as proof.

Take the now-infamous case of Google’s Bard, which confidently claimed that the James Webb Space Telescope took the first-ever image of an exoplanet, a factually incorrect statement that went viral before Google had to step in and correct it.

There are more such multiple instances where AI hallucinations have led to Human hallucinations. Here are a few instances we faced.

When we tried the prompt of “Padmavaat according to the description of Malik Muhammad Jayasi-the writer ”

When we tried the prompt of “monkey to man evolution”

Now, if this is making you question your AI’s ability to get things right, then you should probably start looking have a checklist to check if your AI is reliable.

Before diving into solutions. Question your AI. If it can do these, maybe these will solve a bit of issues:

Can AI recognize its own mistakes?
What would “self-awareness” look like in AI without consciousness?
Are there techniques to make AI second-guess itself?
Can AI “consult an expert” before answering?

That might be just a checklist, but here are the strategies that make AI more reliable:

Strategies for Building Reliable AI

1. Neurosymbolic AI

It is a hybrid approach combining symbolic reasoning (logical rules) with deep learning to improve factual accuracy. IBM is pioneering this approach to build trustworthy AI systems that reason more like humans. For example, RAAPID’s solutions utilize this approach to transform clinical data into compliant, profitable risk adjustment, improving contextual understanding and reducing misdiagnoses.

2. Human-in-the-Loop Verification

Instead of random checks, AI can be trained to request human validation in critical areas. Companies like OpenAI and Google DeepMind are implementing real-time feedback loops where AI flags uncertain responses for review. A notable AI hallucination prevention use case is in medical AI, where human radiologists verify AI-detected anomalies in scans, improving diagnostic accuracy.

3. Truth Scoring Mechanism

IBM’s FactSheets AI assigns credibility scores to AI-generated content, ensuring more fact-based responses. This approach is already being used in financial risk assessment models, where AI outputs are ranked by reliability before human analysts review them.

4. AI ‘Memory’ for Context Awareness

Retrieval-Augmented Generation (RAG) allows AI to access verified sources before responding. This method is already being used by platforms like Bing AI, which cites sources instead of generating standalone answers. In legal tech, RAG-based models ensure AI-generated contracts reference actual legal precedents, reducing AI accuracy problems.

5. Red Teaming & Adversarial Testing

Companies like OpenAI and Google regularly use “red teaming”—pitting AI against expert testers who try to break its logic and expose weaknesses. This helps fine-tune AI models before public release. A practical AI reliability example is cybersecurity AI, where red teams simulate hacking attempts to uncover vulnerabilities before systems go live

The Future: AI That Knows When to Say, “I Don’t Know”

One of the most important steps toward reliable AI is training models to recognize uncertainty. Instead of making up answers, AI should be able to respond with “I’m unsure” or direct users to validated sources. Google DeepMind’s Socratic AI model is experimenting with ways to embed self-doubt into AI.

Conclusion:

AI hallucinations aren’t just quirky mistakes—they’re a major roadblock in creating trustworthy AI systems. By blending techniques like neurosymbolic AI, human-in-the-loop verification, and retrieval-augmented generation, we can push AI toward greater accuracy and reliability.

But here’s the big question: Should AI always strive to be 100% factual, or does some level of ‘creative hallucination’ have its place? After all, some of the best innovations come from thinking outside the box—even if that box is built from AI-generated data and machine learning algorithms.

At Mantra Labs, we specialize in data-driven AI solutions designed to minimize hallucinations and maximize trust. Whether you’re developing AI-powered products or enhancing decision-making with machine learning, our expertise ensures your models provide accurate information, making life easier for humans

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