The Big Insurance Shake Up
In today’s data-driven world, embracing AI is not an option; it’s a necessity for any business seeking to remain competitive.
The insurance sector is no exception.
For years, insurance companies have relied on legacy technologies, which have hindered their ability to innovate and scale. However, the tide is turning, and hesitations to upgrade technologies are increasingly outweighed by fears of falling behind in an era when customer-centricity rules every product in every industry.
Statistics indicate that AI has started to permeate the global insurance industry and that by 2032, the industry is projected to reach nearly $80 billion with a compound annual growth rate of 33.06%. Leading the pack in AI adoption is the Property and Casualty (P/7C) insurance vertical. In contrast, life and health insurance are lagging but catching up.
1. Impact of AI on the 12 Stages of the Insurance Value Chain1. Licensing and Reinsurance: Automate license verification process; predictive analytics for optimal reinsurance contracts; and AI-driven due diligence for reinsurance partners.
2. Compliance and Regulatory Affairs: Auto-validate policies against current laws; flag potential compliance risks; automate submission of regulatory filings and compliance reports.
3. Product Development: Automated data analysis for market trends; A/B testing of policy features; machine learning (ML) for product recommendation engines.
4. Distribution and Sales: Virtual training programmes for agents; automated customer segmentation and personalised marketing; predictive analytics for lead scoring and prioritisation.
5. Submissions and Data Collection: Standardise submission process; automate data parsing and validation; ML-based document recognition and sorting.
6. Underwriting and Rating: Highlight key factors for risk assessment; validate analysis; provide go/no-go recommendations; optimise premiums using ML algorithms.
7. Quoting and Binding: Auto-generate quotes with factor analysis; checklists for negotiation points; real-time adjustments to quotes based on market dynamics.
8. Policy Insurance: Automate final policy checks and approvals; automatic issuance of digital policies; real-time validation against compliance databases.
9. Premium Collection: Automate invoice generation; implement fraud detection in the payment process; use predictive analytics to identify late-payment risks.
10. Claims Processing and Settlement: Automated initial assessment via image recognition; AI-assisted calculation of settlement amounts; predictive analytics for claims prioritisation.
11. Customer Service and Support: Chatbots for 24/7 customer service; sentiment analysis for improved customer interactions; AI-based FAQ generation based on customer behaviour.
12. Data Analytics and Business Intelligence: Real-time risk monitoring; advanced predictive analytics for future trends; AI-powered scenario analysis; and stress testing.
2. AI Applications and Impact
There are four key sub-categories of AI technology used in the insurance sector: ML, natural language processing (NLP), computer vision, and robotic process automation (RPA).
ML is used for fraud detection, risk assessment and pricing. NLP is being deployed for extracting information from text documents, understanding customer queries and generating chatbots. Computer vision is used for fraud detection, claim processing and underwriting. RPA is used in data entry, customer service and claim processing.
3. AI in Health Insurance
Health insurance is starting to realise the potential of AI compared to other industries, highlighting the need for substantial investment in technology and talent. To close the gap, insurtechs are fast taking over, using AI tools to streamline processes, improve member satisfaction, reduce costs and enhance the overall experience. For example, AI is being used for claims processing to reduce the time spent settling claims, aid in fraud detection, and improve member satisfaction and business operations.
With AI, insurers can predict health trends and assess the probability of certain medical conditions resulting in insureds making smart decisions in relation to pricing and plan coverage. Wearable health monitoring devices such as smart watches, smartphone apps, and other intelligent devices are becoming integral in insurance as they provide real-time data for more accurate risk assessment, preventive measures and tailored insurance products.
5G-enabled sensors provide real-time data on risks like health vitals, allowing insurers to offer tailored coverage and preventive measures, leading to advanced AI applications for insurance. Remote monitoring and telemedicine is another area that has been significantly impacted by AI.
Here, insurers are able to integrate telemedicine and remote monitoring solutions into their benefits packages to provide on-demand care for policyholders and offer targeted clinical recommendations.
4. Case Study
Blue Cross Blue Shield, one of the largest health insurance companies in the US, was able to expand without driving up any complexity with the help of AI. Through a 12-person team and the AI engine they deployed in 2024, the company was able to deliver services in an unprecedented manner.
This was the result of effective data use by harnessing Gen AI. Blue Cross Blue Shield successfully created a virtual data schema in taxonomies, allowing them to tap into unstructured data, extract relevant information and apply it to entirely different human interfaces.
5. Rise of Insurtechs
The one-size-fits-all approach to insurance has been challenged by the emergence of insurtechs.
The majority of the industry players can be grouped into eight main categories: Digital Insurance Brokerage, D2C (direct-to-consumer), B2B (business-to-business), P2P (peer-to-peer), Tech Infrastructure Providers, Balance-Sheet-as-a-Service, Embedded Insurance and Embedded Insurance Orchestration companies.
Instabase, a US-based insurtech that automates the processing of complex documents and unstructured data content through applied AI technology, is a unicorn, now valued at two billion dollars.
In Saudi Arabia, Rasan’s Tameeni disrupted the insurance industry by redefining the process of buying and issuing insurance. Listed in Forbes Top 50 Most Funded Startups of 2022, the company announced its successful exit through its IPO in June 2024. Bayzat is one of the most resounding success stories in the Gulf. Lying at the cusp of a work-life platform and health insurance, it has raised $60 million over a series of seven funding rounds since its inception in 2012.
6. Risks Associated with AI in Insurtech
Although AI comes with a plethora of benefits, it also brings some risks and ethical considerations for insurtechs.
Two of the most prevalent are the ‘black box’ nature of some algorithms that produce results without explanation and ‘hallucinations’ – misleading results caused by insufficient training data, incorrect assumptions made by the model, or biases in the data used to train the model.
To overcome this, companies need to understand where the data is being trained from and whether there is an in-built bias or not. AI applications can also make insurance companies prone to cyberattacks, as perpetrators can use Gen AI tools, such as audio synthesis and deepfake, to produce believable yet fraudulent insurance claims.
7. The Future of AI in Insurtech
The future is brimming with potential. As technologies mature, more ingenious applications will emerge that will alter the landscape of the insurance business. Large-scale deployment of chatbots has already become mainstream and the following areas will see a massive thought-through AI deployment in the near future:
• Specialisation in different processes along the insurance valuechain, such as claims management and underwriting, leads tosignificant gains in efficiency, accuracy, personalisation andautomation.
• Premium pricing behaviour where data from the haptics and sensorswill replace proxy data with sourced data.
• Fraud detection and minimising of false positives – a big issue inthe area of claims settlement.
• Statistics suggest that 80% of the claims will be simple claims,settled rapidly through AI.
To capture the full value of AI, insurers must reinvent their end-to-end processes.
Where the key to successful implementation of the technology lies in starting by digitising data collection and ensuring holistic digital journeys, the focus should be on developing a collaborative approach where AI ‘augments’ human capabilities rather than replacing them entirely.
It is this approach that will lead to significant competitive advantages for the industry’s early adopters.
Nabeel Qadeer is Deputy Group CEO, BenchMatrix.nabeel.akmal@gmail.com
Read Comments
1. Licensing and Reinsurance: Automate license verification process; predictive analytics for optimal reinsurance contracts; and AI-driven due diligence for reinsurance partners.
2. Compliance and Regulatory Affairs: Auto-validate policies against current laws; flag potential compliance risks; automate submission of regulatory filings and compliance reports.
3. Product Development: Automated data analysis for market trends; A/B testing of policy features; machine learning (ML) for product recommendation engines.
4. Distribution and Sales: Virtual training programmes for agents; automated customer segmentation and personalised marketing; predictive analytics for lead scoring and prioritisation.
5. Submissions and Data Collection: Standardise submission process; automate data parsing and validation; ML-based document recognition and sorting.
6. Underwriting and Rating: Highlight key factors for risk assessment; validate analysis; provide go/no-go recommendations; optimise premiums using ML algorithms.
7. Quoting and Binding: Auto-generate quotes with factor analysis; checklists for negotiation points; real-time adjustments to quotes based on market dynamics.
8. Policy Insurance: Automate final policy checks and approvals; automatic issuance of digital policies; real-time validation against compliance databases.
9. Premium Collection: Automate invoice generation; implement fraud detection in the payment process; use predictive analytics to identify late-payment risks.
10. Claims Processing and Settlement: Automated initial assessment via image recognition; AI-assisted calculation of settlement amounts; predictive analytics for claims prioritisation.
11. Customer Service and Support: Chatbots for 24/7 customer service; sentiment analysis for improved customer interactions; AI-based FAQ generation based on customer behaviour.
12. Data Analytics and Business Intelligence: Real-time risk monitoring; advanced predictive analytics for future trends; AI-powered scenario analysis; and stress testing.
2. AI Applications and Impact
There are four key sub-categories of AI technology used in the insurance sector: ML, natural language processing (NLP), computer vision, and robotic process automation (RPA).
ML is used for fraud detection, risk assessment and pricing. NLP is being deployed for extracting information from text documents, understanding customer queries and generating chatbots. Computer vision is used for fraud detection, claim processing and underwriting. RPA is used in data entry, customer service and claim processing.
3. AI in Health Insurance
Health insurance is starting to realise the potential of AI compared to other industries, highlighting the need for substantial investment in technology and talent. To close the gap, insurtechs are fast taking over, using AI tools to streamline processes, improve member satisfaction, reduce costs and enhance the overall experience. For example, AI is being used for claims processing to reduce the time spent settling claims, aid in fraud detection, and improve member satisfaction and business operations.
With AI, insurers can predict health trends and assess the probability of certain medical conditions resulting in insureds making smart decisions in relation to pricing and plan coverage. Wearable health monitoring devices such as smart watches, smartphone apps, and other intelligent devices are becoming integral in insurance as they provide real-time data for more accurate risk assessment, preventive measures and tailored insurance products.
5G-enabled sensors provide real-time data on risks like health vitals, allowing insurers to offer tailored coverage and preventive measures, leading to advanced AI applications for insurance. Remote monitoring and telemedicine is another area that has been significantly impacted by AI.
Here, insurers are able to integrate telemedicine and remote monitoring solutions into their benefits packages to provide on-demand care for policyholders and offer targeted clinical recommendations.
4. Case Study
Blue Cross Blue Shield, one of the largest health insurance companies in the US, was able to expand without driving up any complexity with the help of AI. Through a 12-person team and the AI engine they deployed in 2024, the company was able to deliver services in an unprecedented manner.
This was the result of effective data use by harnessing Gen AI. Blue Cross Blue Shield successfully created a virtual data schema in taxonomies, allowing them to tap into unstructured data, extract relevant information and apply it to entirely different human interfaces.
5. Rise of Insurtechs
The one-size-fits-all approach to insurance has been challenged by the emergence of insurtechs.
The majority of the industry players can be grouped into eight main categories: Digital Insurance Brokerage, D2C (direct-to-consumer), B2B (business-to-business), P2P (peer-to-peer), Tech Infrastructure Providers, Balance-Sheet-as-a-Service, Embedded Insurance and Embedded Insurance Orchestration companies.
Instabase, a US-based insurtech that automates the processing of complex documents and unstructured data content through applied AI technology, is a unicorn, now valued at two billion dollars.
In Saudi Arabia, Rasan’s Tameeni disrupted the insurance industry by redefining the process of buying and issuing insurance. Listed in Forbes Top 50 Most Funded Startups of 2022, the company announced its successful exit through its IPO in June 2024. Bayzat is one of the most resounding success stories in the Gulf. Lying at the cusp of a work-life platform and health insurance, it has raised $60 million over a series of seven funding rounds since its inception in 2012.
6. Risks Associated with AI in Insurtech
Although AI comes with a plethora of benefits, it also brings some risks and ethical considerations for insurtechs.
Two of the most prevalent are the ‘black box’ nature of some algorithms that produce results without explanation and ‘hallucinations’ – misleading results caused by insufficient training data, incorrect assumptions made by the model, or biases in the data used to train the model.
To overcome this, companies need to understand where the data is being trained from and whether there is an in-built bias or not. AI applications can also make insurance companies prone to cyberattacks, as perpetrators can use Gen AI tools, such as audio synthesis and deepfake, to produce believable yet fraudulent insurance claims.
7. The Future of AI in Insurtech
The future is brimming with potential. As technologies mature, more ingenious applications will emerge that will alter the landscape of the insurance business. Large-scale deployment of chatbots has already become mainstream and the following areas will see a massive thought-through AI deployment in the near future:
• Specialisation in different processes along the insurance valuechain, such as claims management and underwriting, leads tosignificant gains in efficiency, accuracy, personalisation andautomation.
• Premium pricing behaviour where data from the haptics and sensorswill replace proxy data with sourced data.
• Fraud detection and minimising of false positives – a big issue inthe area of claims settlement.
• Statistics suggest that 80% of the claims will be simple claims,settled rapidly through AI.
To capture the full value of AI, insurers must reinvent their end-to-end processes.
Where the key to successful implementation of the technology lies in starting by digitising data collection and ensuring holistic digital journeys, the focus should be on developing a collaborative approach where AI ‘augments’ human capabilities rather than replacing them entirely.
It is this approach that will lead to significant competitive advantages for the industry’s early adopters.
Nabeel Qadeer is Deputy Group CEO, BenchMatrix.nabeel.akmal@gmail.com
Read Comments
There are four key sub-categories of AI technology used in the insurance sector: ML, natural language processing (NLP), computer vision, and robotic process automation (RPA).
ML is used for fraud detection, risk assessment and pricing. NLP is being deployed for extracting information from text documents, understanding customer queries and generating chatbots. Computer vision is used for fraud detection, claim processing and underwriting. RPA is used in data entry, customer service and claim processing.
3. AI in Health Insurance
Health insurance is starting to realise the potential of AI compared to other industries, highlighting the need for substantial investment in technology and talent. To close the gap, insurtechs are fast taking over, using AI tools to streamline processes, improve member satisfaction, reduce costs and enhance the overall experience. For example, AI is being used for claims processing to reduce the time spent settling claims, aid in fraud detection, and improve member satisfaction and business operations.
With AI, insurers can predict health trends and assess the probability of certain medical conditions resulting in insureds making smart decisions in relation to pricing and plan coverage. Wearable health monitoring devices such as smart watches, smartphone apps, and other intelligent devices are becoming integral in insurance as they provide real-time data for more accurate risk assessment, preventive measures and tailored insurance products.
5G-enabled sensors provide real-time data on risks like health vitals, allowing insurers to offer tailored coverage and preventive measures, leading to advanced AI applications for insurance. Remote monitoring and telemedicine is another area that has been significantly impacted by AI.
Here, insurers are able to integrate telemedicine and remote monitoring solutions into their benefits packages to provide on-demand care for policyholders and offer targeted clinical recommendations.
4. Case Study
Blue Cross Blue Shield, one of the largest health insurance companies in the US, was able to expand without driving up any complexity with the help of AI. Through a 12-person team and the AI engine they deployed in 2024, the company was able to deliver services in an unprecedented manner.
This was the result of effective data use by harnessing Gen AI. Blue Cross Blue Shield successfully created a virtual data schema in taxonomies, allowing them to tap into unstructured data, extract relevant information and apply it to entirely different human interfaces.
5. Rise of Insurtechs
The one-size-fits-all approach to insurance has been challenged by the emergence of insurtechs.
The majority of the industry players can be grouped into eight main categories: Digital Insurance Brokerage, D2C (direct-to-consumer), B2B (business-to-business), P2P (peer-to-peer), Tech Infrastructure Providers, Balance-Sheet-as-a-Service, Embedded Insurance and Embedded Insurance Orchestration companies.
Instabase, a US-based insurtech that automates the processing of complex documents and unstructured data content through applied AI technology, is a unicorn, now valued at two billion dollars.
In Saudi Arabia, Rasan’s Tameeni disrupted the insurance industry by redefining the process of buying and issuing insurance. Listed in Forbes Top 50 Most Funded Startups of 2022, the company announced its successful exit through its IPO in June 2024. Bayzat is one of the most resounding success stories in the Gulf. Lying at the cusp of a work-life platform and health insurance, it has raised $60 million over a series of seven funding rounds since its inception in 2012.
6. Risks Associated with AI in Insurtech
Although AI comes with a plethora of benefits, it also brings some risks and ethical considerations for insurtechs.
Two of the most prevalent are the ‘black box’ nature of some algorithms that produce results without explanation and ‘hallucinations’ – misleading results caused by insufficient training data, incorrect assumptions made by the model, or biases in the data used to train the model.
To overcome this, companies need to understand where the data is being trained from and whether there is an in-built bias or not. AI applications can also make insurance companies prone to cyberattacks, as perpetrators can use Gen AI tools, such as audio synthesis and deepfake, to produce believable yet fraudulent insurance claims.
7. The Future of AI in Insurtech
The future is brimming with potential. As technologies mature, more ingenious applications will emerge that will alter the landscape of the insurance business. Large-scale deployment of chatbots has already become mainstream and the following areas will see a massive thought-through AI deployment in the near future:
• Specialisation in different processes along the insurance valuechain, such as claims management and underwriting, leads tosignificant gains in efficiency, accuracy, personalisation andautomation.
• Premium pricing behaviour where data from the haptics and sensorswill replace proxy data with sourced data.
• Fraud detection and minimising of false positives – a big issue inthe area of claims settlement.
• Statistics suggest that 80% of the claims will be simple claims,settled rapidly through AI.
To capture the full value of AI, insurers must reinvent their end-to-end processes.
Where the key to successful implementation of the technology lies in starting by digitising data collection and ensuring holistic digital journeys, the focus should be on developing a collaborative approach where AI ‘augments’ human capabilities rather than replacing them entirely.
It is this approach that will lead to significant competitive advantages for the industry’s early adopters.
Nabeel Qadeer is Deputy Group CEO, BenchMatrix.nabeel.akmal@gmail.com
Read Comments
Health insurance is starting to realise the potential of AI compared to other industries, highlighting the need for substantial investment in technology and talent. To close the gap, insurtechs are fast taking over, using AI tools to streamline processes, improve member satisfaction, reduce costs and enhance the overall experience. For example, AI is being used for claims processing to reduce the time spent settling claims, aid in fraud detection, and improve member satisfaction and business operations.
With AI, insurers can predict health trends and assess the probability of certain medical conditions resulting in insureds making smart decisions in relation to pricing and plan coverage. Wearable health monitoring devices such as smart watches, smartphone apps, and other intelligent devices are becoming integral in insurance as they provide real-time data for more accurate risk assessment, preventive measures and tailored insurance products.
5G-enabled sensors provide real-time data on risks like health vitals, allowing insurers to offer tailored coverage and preventive measures, leading to advanced AI applications for insurance. Remote monitoring and telemedicine is another area that has been significantly impacted by AI.
Here, insurers are able to integrate telemedicine and remote monitoring solutions into their benefits packages to provide on-demand care for policyholders and offer targeted clinical recommendations.
4. Case Study
Blue Cross Blue Shield, one of the largest health insurance companies in the US, was able to expand without driving up any complexity with the help of AI. Through a 12-person team and the AI engine they deployed in 2024, the company was able to deliver services in an unprecedented manner.
This was the result of effective data use by harnessing Gen AI. Blue Cross Blue Shield successfully created a virtual data schema in taxonomies, allowing them to tap into unstructured data, extract relevant information and apply it to entirely different human interfaces.
5. Rise of Insurtechs
The one-size-fits-all approach to insurance has been challenged by the emergence of insurtechs.
The majority of the industry players can be grouped into eight main categories: Digital Insurance Brokerage, D2C (direct-to-consumer), B2B (business-to-business), P2P (peer-to-peer), Tech Infrastructure Providers, Balance-Sheet-as-a-Service, Embedded Insurance and Embedded Insurance Orchestration companies.
Instabase, a US-based insurtech that automates the processing of complex documents and unstructured data content through applied AI technology, is a unicorn, now valued at two billion dollars.
In Saudi Arabia, Rasan’s Tameeni disrupted the insurance industry by redefining the process of buying and issuing insurance. Listed in Forbes Top 50 Most Funded Startups of 2022, the company announced its successful exit through its IPO in June 2024. Bayzat is one of the most resounding success stories in the Gulf. Lying at the cusp of a work-life platform and health insurance, it has raised $60 million over a series of seven funding rounds since its inception in 2012.
6. Risks Associated with AI in Insurtech
Although AI comes with a plethora of benefits, it also brings some risks and ethical considerations for insurtechs.
Two of the most prevalent are the ‘black box’ nature of some algorithms that produce results without explanation and ‘hallucinations’ – misleading results caused by insufficient training data, incorrect assumptions made by the model, or biases in the data used to train the model.
To overcome this, companies need to understand where the data is being trained from and whether there is an in-built bias or not. AI applications can also make insurance companies prone to cyberattacks, as perpetrators can use Gen AI tools, such as audio synthesis and deepfake, to produce believable yet fraudulent insurance claims.
7. The Future of AI in Insurtech
The future is brimming with potential. As technologies mature, more ingenious applications will emerge that will alter the landscape of the insurance business. Large-scale deployment of chatbots has already become mainstream and the following areas will see a massive thought-through AI deployment in the near future:
• Specialisation in different processes along the insurance valuechain, such as claims management and underwriting, leads tosignificant gains in efficiency, accuracy, personalisation andautomation.
• Premium pricing behaviour where data from the haptics and sensorswill replace proxy data with sourced data.
• Fraud detection and minimising of false positives – a big issue inthe area of claims settlement.
• Statistics suggest that 80% of the claims will be simple claims,settled rapidly through AI.
To capture the full value of AI, insurers must reinvent their end-to-end processes.
Where the key to successful implementation of the technology lies in starting by digitising data collection and ensuring holistic digital journeys, the focus should be on developing a collaborative approach where AI ‘augments’ human capabilities rather than replacing them entirely.
It is this approach that will lead to significant competitive advantages for the industry’s early adopters.
Nabeel Qadeer is Deputy Group CEO, BenchMatrix.nabeel.akmal@gmail.com
Read Comments
Blue Cross Blue Shield, one of the largest health insurance companies in the US, was able to expand without driving up any complexity with the help of AI. Through a 12-person team and the AI engine they deployed in 2024, the company was able to deliver services in an unprecedented manner.
This was the result of effective data use by harnessing Gen AI. Blue Cross Blue Shield successfully created a virtual data schema in taxonomies, allowing them to tap into unstructured data, extract relevant information and apply it to entirely different human interfaces.
5. Rise of Insurtechs
The one-size-fits-all approach to insurance has been challenged by the emergence of insurtechs.
The majority of the industry players can be grouped into eight main categories: Digital Insurance Brokerage, D2C (direct-to-consumer), B2B (business-to-business), P2P (peer-to-peer), Tech Infrastructure Providers, Balance-Sheet-as-a-Service, Embedded Insurance and Embedded Insurance Orchestration companies.
Instabase, a US-based insurtech that automates the processing of complex documents and unstructured data content through applied AI technology, is a unicorn, now valued at two billion dollars.
In Saudi Arabia, Rasan’s Tameeni disrupted the insurance industry by redefining the process of buying and issuing insurance. Listed in Forbes Top 50 Most Funded Startups of 2022, the company announced its successful exit through its IPO in June 2024. Bayzat is one of the most resounding success stories in the Gulf. Lying at the cusp of a work-life platform and health insurance, it has raised $60 million over a series of seven funding rounds since its inception in 2012.
6. Risks Associated with AI in Insurtech
Although AI comes with a plethora of benefits, it also brings some risks and ethical considerations for insurtechs.
Two of the most prevalent are the ‘black box’ nature of some algorithms that produce results without explanation and ‘hallucinations’ – misleading results caused by insufficient training data, incorrect assumptions made by the model, or biases in the data used to train the model.
To overcome this, companies need to understand where the data is being trained from and whether there is an in-built bias or not. AI applications can also make insurance companies prone to cyberattacks, as perpetrators can use Gen AI tools, such as audio synthesis and deepfake, to produce believable yet fraudulent insurance claims.
7. The Future of AI in Insurtech
The future is brimming with potential. As technologies mature, more ingenious applications will emerge that will alter the landscape of the insurance business. Large-scale deployment of chatbots has already become mainstream and the following areas will see a massive thought-through AI deployment in the near future:
• Specialisation in different processes along the insurance valuechain, such as claims management and underwriting, leads tosignificant gains in efficiency, accuracy, personalisation andautomation.
• Premium pricing behaviour where data from the haptics and sensorswill replace proxy data with sourced data.
• Fraud detection and minimising of false positives – a big issue inthe area of claims settlement.
• Statistics suggest that 80% of the claims will be simple claims,settled rapidly through AI.
To capture the full value of AI, insurers must reinvent their end-to-end processes.
Where the key to successful implementation of the technology lies in starting by digitising data collection and ensuring holistic digital journeys, the focus should be on developing a collaborative approach where AI ‘augments’ human capabilities rather than replacing them entirely.
It is this approach that will lead to significant competitive advantages for the industry’s early adopters.
Nabeel Qadeer is Deputy Group CEO, BenchMatrix.nabeel.akmal@gmail.com
Read Comments
The one-size-fits-all approach to insurance has been challenged by the emergence of insurtechs.
The majority of the industry players can be grouped into eight main categories: Digital Insurance Brokerage, D2C (direct-to-consumer), B2B (business-to-business), P2P (peer-to-peer), Tech Infrastructure Providers, Balance-Sheet-as-a-Service, Embedded Insurance and Embedded Insurance Orchestration companies.
Instabase, a US-based insurtech that automates the processing of complex documents and unstructured data content through applied AI technology, is a unicorn, now valued at two billion dollars.
In Saudi Arabia, Rasan’s Tameeni disrupted the insurance industry by redefining the process of buying and issuing insurance. Listed in Forbes Top 50 Most Funded Startups of 2022, the company announced its successful exit through its IPO in June 2024. Bayzat is one of the most resounding success stories in the Gulf. Lying at the cusp of a work-life platform and health insurance, it has raised $60 million over a series of seven funding rounds since its inception in 2012.
6. Risks Associated with AI in Insurtech
Although AI comes with a plethora of benefits, it also brings some risks and ethical considerations for insurtechs.
Two of the most prevalent are the ‘black box’ nature of some algorithms that produce results without explanation and ‘hallucinations’ – misleading results caused by insufficient training data, incorrect assumptions made by the model, or biases in the data used to train the model.
To overcome this, companies need to understand where the data is being trained from and whether there is an in-built bias or not. AI applications can also make insurance companies prone to cyberattacks, as perpetrators can use Gen AI tools, such as audio synthesis and deepfake, to produce believable yet fraudulent insurance claims.
7. The Future of AI in Insurtech
The future is brimming with potential. As technologies mature, more ingenious applications will emerge that will alter the landscape of the insurance business. Large-scale deployment of chatbots has already become mainstream and the following areas will see a massive thought-through AI deployment in the near future:
• Specialisation in different processes along the insurance valuechain, such as claims management and underwriting, leads tosignificant gains in efficiency, accuracy, personalisation andautomation.
• Premium pricing behaviour where data from the haptics and sensorswill replace proxy data with sourced data.
• Fraud detection and minimising of false positives – a big issue inthe area of claims settlement.
• Statistics suggest that 80% of the claims will be simple claims,settled rapidly through AI.
To capture the full value of AI, insurers must reinvent their end-to-end processes.
Where the key to successful implementation of the technology lies in starting by digitising data collection and ensuring holistic digital journeys, the focus should be on developing a collaborative approach where AI ‘augments’ human capabilities rather than replacing them entirely.
It is this approach that will lead to significant competitive advantages for the industry’s early adopters.
Nabeel Qadeer is Deputy Group CEO, BenchMatrix.nabeel.akmal@gmail.com
Read Comments
Although AI comes with a plethora of benefits, it also brings some risks and ethical considerations for insurtechs.
Two of the most prevalent are the ‘black box’ nature of some algorithms that produce results without explanation and ‘hallucinations’ – misleading results caused by insufficient training data, incorrect assumptions made by the model, or biases in the data used to train the model.
To overcome this, companies need to understand where the data is being trained from and whether there is an in-built bias or not. AI applications can also make insurance companies prone to cyberattacks, as perpetrators can use Gen AI tools, such as audio synthesis and deepfake, to produce believable yet fraudulent insurance claims.
7. The Future of AI in Insurtech
The future is brimming with potential. As technologies mature, more ingenious applications will emerge that will alter the landscape of the insurance business. Large-scale deployment of chatbots has already become mainstream and the following areas will see a massive thought-through AI deployment in the near future:
• Specialisation in different processes along the insurance valuechain, such as claims management and underwriting, leads tosignificant gains in efficiency, accuracy, personalisation andautomation.
• Premium pricing behaviour where data from the haptics and sensorswill replace proxy data with sourced data.
• Fraud detection and minimising of false positives – a big issue inthe area of claims settlement.
• Statistics suggest that 80% of the claims will be simple claims,settled rapidly through AI.
To capture the full value of AI, insurers must reinvent their end-to-end processes.
Where the key to successful implementation of the technology lies in starting by digitising data collection and ensuring holistic digital journeys, the focus should be on developing a collaborative approach where AI ‘augments’ human capabilities rather than replacing them entirely.
It is this approach that will lead to significant competitive advantages for the industry’s early adopters.
Nabeel Qadeer is Deputy Group CEO, BenchMatrix.nabeel.akmal@gmail.com
Read Comments
The future is brimming with potential. As technologies mature, more ingenious applications will emerge that will alter the landscape of the insurance business. Large-scale deployment of chatbots has already become mainstream and the following areas will see a massive thought-through AI deployment in the near future:
• Specialisation in different processes along the insurance valuechain, such as claims management and underwriting, leads tosignificant gains in efficiency, accuracy, personalisation andautomation.
• Premium pricing behaviour where data from the haptics and sensorswill replace proxy data with sourced data.
• Fraud detection and minimising of false positives – a big issue inthe area of claims settlement.
• Statistics suggest that 80% of the claims will be simple claims,settled rapidly through AI.
To capture the full value of AI, insurers must reinvent their end-to-end processes.
Where the key to successful implementation of the technology lies in starting by digitising data collection and ensuring holistic digital journeys, the focus should be on developing a collaborative approach where AI ‘augments’ human capabilities rather than replacing them entirely.
It is this approach that will lead to significant competitive advantages for the industry’s early adopters.
Nabeel Qadeer is Deputy Group CEO, BenchMatrix.nabeel.akmal@gmail.com