COVID-19 Effects: Motor Insurance Practical Challenges

5 minutes, 36 seconds read

Published on Jun 16, 2020

The outbreak of COVID-19 shook the very foundation of many industries. It is probably the first time that a pandemic created a dent in the world economy. Statista estimates that COVID-19 will bring down the global real GDP growth by 0.5 percent in 2020 compared to the previous year.

Consumers have become conscious of their expenditure. Due to disruptions in supply chains, many small and medium businesses have suffered huge losses. A dip in international trade has created a ripple effect across all industries including travel, hospitality, insurance, and manufacturing.

The pandemic has different effects on the life and non-life segments of Insurance. While the rising concern for health has led to a spike in life and health insurance demands, the general insurance sector is suffering a setback due to restrained expenditure.

Motor insurance is no different from being severely hit by the pandemic. Amidst this crisis, people are not keen on purchasing cars, bikes, which is directly affecting the insurance sector as well. Re-negotiation on premiums is another big challenge for Insurers. Let’s delve deeper into the impact of Covid-19 on motor insurance and practical challenges for Insurers.

The Real Picture

Till a cure is available in the market, there will be travel restrictions to a certain extent. People will hesitate to commute daily and avoid long-distance travel. The significant drop in the usage of motor vehicles is impacting claims and sales differently.

Claims and Premium

In the initial lockdown period, many people were not able to drive their vehicles. The domino effect of this was a reduced number of motor insurance claims.

At first, it sounds profitable for Insurers. But, for policyholders, continued premiums on policies they can’t use seems an additional burden. So most customers are either asking for bailouts or reduced premiums or refunds on premiums.

Some major Motor insurance companies in the US and UK have already refunded 10-15% of annual premiums back to customers. In India, the finance ministry has extended the validity of the third-party insurance policies which were up for renewal during the lockdown.

Sales

Moody’s Investors Service, expects a 20% drop in global auto unit sales as compared to its earlier projection of 14%. In many countries, Motor Insurance is compulsory. However, if people won’t use vehicles, there’ll be a significant dip in the requirement for Motor Insurance policies.

In the wake of the current situation, IRDAI decided to withdraw its earlier policy of long-term third party vehicle insurance coverage from August 1, 2020. Earlier, the third party insurance was mandatory (three years for new cars and five-year policies for two-wheelers).

The IRDAI’s decision is a result of concerns over the implementation of a long-term insurance cover package which made buying new vehicles an expensive affair. This will reduce the price of vehicles, which, in turn, will boost the automobile and motor insurance sectors.

Prevailing Challenges for Motor Insurance Companies

Motor Claims Process

Vehicles can still suffer damage due to theft, natural calamities, non-usage, etc. Moreover, once people start traveling, accidents are prone to occur. It will be difficult for claims investigators to assess the damage through an in-person visit.

Some insurance companies are accepting claims and renewing premiums through online inspection and vehicle photograph assessment. This procedure, however, is still in a nascent stage. Despite high-resolution cameras, it is possible to overlook a dent due to deflection caused by sunlight.

Sales and Marketing

Even though automobile sales dropped in the short-term, it is expected to pick-up in the early quarter of 2021.

On one hand, marketing & selling policies at the original price will be difficult for motor insurers, and on the other hand, people will avoid public transport and prefer personal vehicles for commuting.

Insurers, thus, have a challenge for positioning their product that suits both — customer requirements and their profit margins amidst fierce competition with InsurTechs.

Policy Changes due to Volatile Consumer Behaviour

Since there were no clauses or policies for the pandemic in place earlier, some immediate mitigation measures had to be taken such as refunds on premiums to safeguard customers’ interests.

Going forward, till there is a conclusive solution to this crisis it will be difficult for Insurers to formulate policies that preserve both – their and customers’ interests.

Business Continuity

With lockdowns, major workforce resorted to working-from-home. In the beginning, some companies faced issues in making sure whether their employees had the means to work remotely.

Even though the lockdowns have been eased a bit and the workforce is getting used to collaborating online, the situation is here to stay. Smooth operations with a major part of the workforce working remotely is still a challenge, especially for call-centers, surveyors, and field investigators.

Lack of Historical Data

During the SARS and Ebola outbreaks, only some countries like Singapore, Thailand, China, the African continent were affected. To a certain extent, businesses were cognizant of the effects which COVID-19 would have on their businesses.

Therefore, insurers had come out with new policies and clauses on pandemics. However, the outbreak of a pandemic of this scale where the entire world felt the effects had not happened earlier. Lack of historical data for motor insurance is making it difficult to come up with mitigation strategies and business models for a sustainable and profitable business.

Mitigation Measures and The Way Forward

“Claims” is one of the most important aspects of motor insurance and will now witness automation more than ever. Coupling Machine Vision technology with panoramic/360° pictures can give insurers a holistic view of the extent of the damage.

Car rental services have an extensive guide to click pictures of the car rented before driving which makes the process very tedious. This can be simplified through apps having pre-shot pictures of the car before renting it out. AI can also help assess the accuracy of the images.

In the short run, finance ministries in many countries have taken steps to lessen the burden of the insurance premiums. But in the long run, insurers will have to come up with policies that are more viable for the insurance buyers. ‘Pay-as-you-use’ policies will see more demand because of their small ticket size.

Technologies such as IoT can help gather data through sensors that could help underwrite insurance premiums for vehicles. The data gathered can help understand consumer behavior and profile them for creating future strategies.

We’re an InsurTech100 firm, building AI-First Solutions for the new age Digital Insurer across the entire Insurance Lifecycle. For your specific requirements and Machine Vision for motor claims, please feel free to write to us at hello@mantralabsglobal.com.

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

Impact of COVID-19 on Motor Insurance & Practical Challenges for Insurers