By deploying AI-based multimodal technologies to detect fraudulent behaviors throughout the claims lifecycle.Property and casualty insurers can help tackle multibillion-dollar consumer drain.
Insurance fraud remains the second costliest white collar crime in the United States after tax evasion.The Coalition Against Insurance Fraud reports that 78% of American consumers are concerned about insurance fraud, most likely because they know.Fraud doesn't just affect insurers, because losses are passed on to insureds through higher premiums to cover costs.The FBI reports that insurance fraud costs the average American family $400 to $700 a year.This is due to the increased premiums that companies charge to compensate for the financial damage caused by insurance fraud.
Meanwhile, many property and casualty insurers are facing a growing churn of customers due to recent inflation-driven rate hikes.In this environment, continuing to raise premiums to offset fraud losses is likely not a viable strategy for long-term profitability and market share growth.
Instead, insurers can prepare to fight back and help tackle fraud before incidents occur.They can move from relying on traditional, rules-based fraud detection methods to investing in more advanced detection and prevention techniques.In a recent Deloitte survey of insurance executives, 35% of respondents selected fraud detection as one of the top five areas for developing or implementing generative AI applications in the next 12 months.
Continuing to raise premiums to compensate for fraud losses is probably not a viable strategy for long-term profitability and market share growth.
Deloitte predicts that by deploying AI-based technologies throughout the claim lifecycle and integrating real-time analytics from multiple modalities, property and casualty insurers could reduce fraudulent claims and save between $80 billion and $160 billion by 2032.
Multimodal AI-based technologies refer to advanced systems that use AI to process and integrate data from multiple modalities or sources.These modalities can include text, images, audio, ...
By deploying AI-based multimodal technologies to detect fraudulent behaviors throughout the claims lifecycle.Property and casualty insurers can help tackle multibillion-dollar consumer drain.
Insurance fraud remains the second costliest white collar crime in the United States after tax evasion.The Coalition Against Insurance Fraud reports that 78% of American consumers are concerned about insurance fraud, most likely because they know.Fraud doesn't just affect insurers, because losses are passed on to insureds through higher premiums to cover costs.The FBI reports that insurance fraud costs the average American family $400 to $700 a year.This is due to the increased premiums that companies charge to compensate for the financial damage caused by insurance fraud.
Meanwhile, many property and casualty insurers are facing a growing churn of customers due to recent inflation-driven rate hikes.In this environment, continuing to raise premiums to offset fraud losses is likely not a viable strategy for long-term profitability and market share growth.
Instead, insurers can prepare to fight back and help tackle fraud before incidents occur.They can move from relying on traditional, rules-based fraud detection methods to investing in more advanced detection and prevention techniques.In a recent Deloitte survey of insurance executives, 35% of respondents selected fraud detection as one of the top five areas for developing or implementing generative AI applications in the next 12 months.
Continuing to raise premiums to compensate for fraud losses is probably not a viable strategy for long-term profitability and market share growth.
Deloitte predicts that by deploying AI-based technologies throughout the claim lifecycle and integrating real-time analytics from multiple modalities, property and casualty insurers could reduce fraudulent claims and save between $80 billion and $160 billion by 2032.
Multimodal AI-based technologies refer to advanced systems that use AI to process and integrate data from multiple modalities or sources.These modalities can include text, images, audio, video, and sensory data, among others.By combining and analyzing different types of data, these technologies can generate more comprehensive and accurate insights than single-modal systems.
Why is detecting fraud in property and casualty insurance so difficult?
It is estimated that 10% of property and casualty insurance claims are fraudulent, resulting in a loss of $122 billion annually.This represents about 40% of the total fraud losses in the insurance industry according to recent analysis and expert estimates.One of the reasons that fraud is so common is that insured individuals rarely interact with their insurance providers.They do so mainly when they pay premiums annually or when they have to file personal injury or property damage claims.This rare interaction limits insurers' ability to monitor their customers' activities and facilitates fraudulent activity.
Frauds are generally divided into two main types - "soft" and "hard" incidents according to the nature of the act performed."Soft" fraud involves inflating a perfectly legitimate claim by overstating costs or exaggerating damages and injuries.For example, an insured may inflate the cost of a repair or state an injury is more serious than it actually is."Hard" fraud is a premeditated act designed to create a completely false claim and defraud the insurer.This could include faking an accident, arson, faking a theft or using one photo for multiple insurances."Soft" fraud is significantly more common because it is difficult to prove and accounts for approximately 60% of all reported cases.
The increasing pressure is driving the demand for advanced detection tools
The onset of the pandemic accelerated the digitization process and opened up new opportunities for fraudsters and insurers alike.This has led to the emergence of numerous innovative technological solutions aimed at preventing and detecting insurance fraud.Fraud detection technology has become one of the fastest growing industries in the insurance sector globally.The market is expected to grow eightfold, from $4 billion in 2023 to $32 billion by 2032.At the same time, regulators such as the National Association of Insurance Commissioners are stepping up the push for modern systems.
How can artificial intelligence help detect and prevent fraud?
Artificial intelligence provides insurers with innovative models for fraud detection and optimization of claims processing processes.These technologies allow investigators to focus on more complex cases instead of routine checks and data analysis.Combining AI-based solutions with advanced data analytics improves the accuracy and speed of fraud detection.Depending on the law in each jurisdiction, these technologies may be integrated throughout the claims lifecycle.They are particularly useful in property claims and motor insurance where the volume of data and complexity is significant.Such an approach results in lower operational costs, faster processing and increased effectiveness in preventing insurance fraud.
Techniques
Multiple techniques such as automated business rules, artificial intelligence and machine learning can process millions of claims in real time.These technologies include text mining, anomaly detection, and network connection analysis for more accurate fraud detection.Combining data from different sources – text, images, audio and video – improves the identification of patterns and suspicious activity.Such an approach reduces the number of false positives and increases the rate of successful detection of fraudulent claims.It also results in significant cost savings related to insurance fraud investigations and processing.All of these techniques must be implemented under strict human supervision and in accordance with the laws of each jurisdiction.
Here are some areas where artificial intelligence can be used:
Text analysis.Natural language processing analyzes text data from claim forms, emails and social media posts to identify keywords and objects.While claims with suspicious language or inconsistent details can be flagged for further investigation, regulations such as the Colorado Artificial Intelligence Act require models based on artificial intelligence algorithms to avoid discrimination and bias in risk flagging.
Audio-Image-Video Analysis.Speech recognition and sentiment analysis can examine customer calls for signs of coercion or unnatural behavior.These methods are permitted under the European Union Artificial Intelligence Act when used for security-related purposes.Analyzing photos allows detection of irregularities in metadata, signs of manipulation or repeated use of the same images.Causal analysis can determine whether the described injuries correspond to the actual nature of the accident that occurred.Video analysis makes it possible to confirm the extent of the damage and the authenticity of the visual evidence provided by the insured persons.Through it, potential signs of manipulation, staging or other fraudulent actions aimed at wrongful compensation can be highlighted.
Geospatial analysis.Satellite imagery and detailed 3D footage from drones can verify the extent and location of damage that may not be clearly visible on physical inspections.It could also reduce the risk of injury to damage personnel, especially at natural disaster sites.
Data from the Internet of Things.Real-time monitoring devices such as vehicle telematics can reconstruct accidents and verify the legitimacy of claims.Smart home sensors, such as water leak detectors and security cameras, can help gather evidence that can be used to verify claims and detect fraudulent or staged activity.
Simulation models.Reproducing the behavior of medical providers, repair shops and others that people may work with in different scenarios in a controlled virtual environment can identify patterns and deviations from standard industry practices and detect cases such as overcharging, unnecessary services and coordinated activities or potential conflicts between organizations.
By combining AI-based anti-fraud technologies with advanced data analytics (depending on each jurisdiction's law), insurers can improve their fraud detection and prevention capabilities.
Combining artificial intelligence and human foresight may be the way forward
Over the past two decades, insurers have created specialized units to investigate and prevent insurance fraud.These departments play a key role in identifying fraudulent practices and reducing financial losses to the industry.Looking to the future, anti-fraud leaders face challenges related to managing costs and staffing shortages.Insurers who combine innovative technology with human expertise can detect fraud more effectively and optimize the entire process.Integrating artificial intelligence with professional expertise could result in billions of dollars in savings for policyholders.Attracting and retaining skilled professionals, along with investments in automation, will be critical to long-term success.
Learn more on the Insurance.bg website!
Frequently asked questions
Why is insurance fraud such a serious problem?
Answer: Insurance frauds lead to huge financial losses which are ultimately borne by the honest customers through higher premiums.In the US, the average family pays between $400 and $700 more per year precisely because of fraud.They also undermine confidence in the insurance system.Pressure on companies to compensate for losses leads to more expensive policies and limited coverage.
What are "soft" and "hard" insurance fraud?
Answer: "Soft" fraud involves exaggerating actual damage or costs, such as an inflated repair invoice."Hard" fraud is deliberately organized and involves staged incidents, fake thefts or forging documents.Although "soft" fraud may seem more minor, it is much more common.It is they who carry the greatest cumulative financial risk for the insurance system.
How does artificial intelligence detect insurance fraud?
Answer: AI analyzes massive data sets to detect patterns, anomalies, and inconsistencies in claims and documents.It can process text, images, video and sensor data simultaneously.This enables faster recognition of suspicious behavior and reduction of human error.Investigative experts focus on complex cases instead of routine inspection.
What does “multimodal AI” mean in insurance?
Answer: Multimodal AI combines different sources of information such as text, images, audio and geodata.For example, it can compare a photo of an accident with data from the car's telematics.Thus, a more accurate assessment of the reality of the damage is achieved.This greatly reduces the likelihood of false claims.
What is the effect of AI on the cost of insurance?
Answer: Better fraud detection allows insurers to limit financial losses.This reduces the need for constant premium increases.In the long run, customers can benefit from more stable and fair prices.In addition, claim processing becomes faster and more transparent.
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By deploying AI-based multimodal technologies to detect fraudulent behaviors throughout the claims lifecycle.Property and casualty insurers can help tackle multibillion-dollar consumer drain.
Insurance fraud remains the second costliest white collar crime in the United States after tax evasion.The Coalition Against Insurance Fraud reports that 78% of American consumers are concerned about insurance fraud, most likely because they know.Fraud doesn't just affect insurers, because losses are passed on to insureds through higher premiums to cover costs.The FBI reports that insurance fraud costs the average American family $400 to $700 a year.This is due to the increased premiums that companies charge to compensate for the financial damage caused by insurance fraud.
Meanwhile, many property and casualty insurers are facing a growing churn of customers due to recent inflation-driven rate hikes.In this environment, continuing to raise premiums to offset fraud losses is likely not a viable strategy for long-term profitability and market share growth.
Instead, insurers can prepare to fight back and help tackle fraud before incidents occur.They can move from relying on traditional, rules-based fraud detection methods to investing in more advanced detection and prevention techniques.In a recent Deloitte survey of insurance executives, 35% of respondents selected fraud detection as one of the top five areas for developing or implementing generative AI applications in the next 12 months.
Continuing to raise premiums to compensate for fraud losses is probably not a viable strategy for long-term profitability and market share growth.
Deloitte predicts that by deploying AI-based technologies throughout the claim lifecycle and integrating real-time analytics from multiple modalities, property and casualty insurers could reduce fraudulent claims and save between $80 billion and $160 billion by 2032.
Multimodal AI-based technologies refer to advanced systems that use AI to process and integrate data from multiple modalities or sources.These modalities can include text, images, audio, ...
By deploying AI-based multimodal technologies to detect fraudulent behaviors throughout the claims lifecycle.Property and casualty insurers can help tackle multibillion-dollar consumer drain.
Insurance fraud remains the second costliest white collar crime in the United States after tax evasion.The Coalition Against Insurance Fraud reports that 78% of American consumers are concerned about insurance fraud, most likely because they know.Fraud doesn't just affect insurers, because losses are passed on to insureds through higher premiums to cover costs.The FBI reports that insurance fraud costs the average American family $400 to $700 a year.This is due to the increased premiums that companies charge to compensate for the financial damage caused by insurance fraud.
Meanwhile, many property and casualty insurers are facing a growing churn of customers due to recent inflation-driven rate hikes.In this environment, continuing to raise premiums to offset fraud losses is likely not a viable strategy for long-term profitability and market share growth.
Instead, insurers can prepare to fight back and help tackle fraud before incidents occur.They can move from relying on traditional, rules-based fraud detection methods to investing in more advanced detection and prevention techniques.In a recent Deloitte survey of insurance executives, 35% of respondents selected fraud detection as one of the top five areas for developing or implementing generative AI applications in the next 12 months.
Continuing to raise premiums to compensate for fraud losses is probably not a viable strategy for long-term profitability and market share growth.
Deloitte predicts that by deploying AI-based technologies throughout the claim lifecycle and integrating real-time analytics from multiple modalities, property and casualty insurers could reduce fraudulent claims and save between $80 billion and $160 billion by 2032.
Multimodal AI-based technologies refer to advanced systems that use AI to process and integrate data from multiple modalities or sources.These modalities can include text, images, audio, video, and sensory data, among others.By combining and analyzing different types of data, these technologies can generate more comprehensive and accurate insights than single-modal systems.
Why is detecting fraud in property and casualty insurance so difficult?
It is estimated that 10% of property and casualty insurance claims are fraudulent, resulting in a loss of $122 billion annually.This represents about 40% of the total fraud losses in the insurance industry according to recent analysis and expert estimates.One of the reasons that fraud is so common is that insured individuals rarely interact with their insurance providers.They do so mainly when they pay premiums annually or when they have to file personal injury or property damage claims.This rare interaction limits insurers' ability to monitor their customers' activities and facilitates fraudulent activity.
Frauds are generally divided into two main types - "soft" and "hard" incidents according to the nature of the act performed."Soft" fraud involves inflating a perfectly legitimate claim by overstating costs or exaggerating damages and injuries.For example, an insured may inflate the cost of a repair or state an injury is more serious than it actually is."Hard" fraud is a premeditated act designed to create a completely false claim and defraud the insurer.This could include faking an accident, arson, faking a theft or using one photo for multiple insurances."Soft" fraud is significantly more common because it is difficult to prove and accounts for approximately 60% of all reported cases.
The increasing pressure is driving the demand for advanced detection tools
The onset of the pandemic accelerated the digitization process and opened up new opportunities for fraudsters and insurers alike.This has led to the emergence of numerous innovative technological solutions aimed at preventing and detecting insurance fraud.Fraud detection technology has become one of the fastest growing industries in the insurance sector globally.The market is expected to grow eightfold, from $4 billion in 2023 to $32 billion by 2032.At the same time, regulators such as the National Association of Insurance Commissioners are stepping up the push for modern systems.
How can artificial intelligence help detect and prevent fraud?
Artificial intelligence provides insurers with innovative models for fraud detection and optimization of claims processing processes.These technologies allow investigators to focus on more complex cases instead of routine checks and data analysis.Combining AI-based solutions with advanced data analytics improves the accuracy and speed of fraud detection.Depending on the law in each jurisdiction, these technologies may be integrated throughout the claims lifecycle.They are particularly useful in property claims and motor insurance where the volume of data and complexity is significant.Such an approach results in lower operational costs, faster processing and increased effectiveness in preventing insurance fraud.
Techniques
Multiple techniques such as automated business rules, artificial intelligence and machine learning can process millions of claims in real time.These technologies include text mining, anomaly detection, and network connection analysis for more accurate fraud detection.Combining data from different sources – text, images, audio and video – improves the identification of patterns and suspicious activity.Such an approach reduces the number of false positives and increases the rate of successful detection of fraudulent claims.It also results in significant cost savings related to insurance fraud investigations and processing.All of these techniques must be implemented under strict human supervision and in accordance with the laws of each jurisdiction.
Here are some areas where artificial intelligence can be used:
Text analysis.Natural language processing analyzes text data from claim forms, emails and social media posts to identify keywords and objects.While claims with suspicious language or inconsistent details can be flagged for further investigation, regulations such as the Colorado Artificial Intelligence Act require models based on artificial intelligence algorithms to avoid discrimination and bias in risk flagging.
Audio-Image-Video Analysis.Speech recognition and sentiment analysis can examine customer calls for signs of coercion or unnatural behavior.These methods are permitted under the European Union Artificial Intelligence Act when used for security-related purposes.Analyzing photos allows detection of irregularities in metadata, signs of manipulation or repeated use of the same images.Causal analysis can determine whether the described injuries correspond to the actual nature of the accident that occurred.Video analysis makes it possible to confirm the extent of the damage and the authenticity of the visual evidence provided by the insured persons.Through it, potential signs of manipulation, staging or other fraudulent actions aimed at wrongful compensation can be highlighted.
Geospatial analysis.Satellite imagery and detailed 3D footage from drones can verify the extent and location of damage that may not be clearly visible on physical inspections.It could also reduce the risk of injury to damage personnel, especially at natural disaster sites.
Data from the Internet of Things.Real-time monitoring devices such as vehicle telematics can reconstruct accidents and verify the legitimacy of claims.Smart home sensors, such as water leak detectors and security cameras, can help gather evidence that can be used to verify claims and detect fraudulent or staged activity.
Simulation models.Reproducing the behavior of medical providers, repair shops and others that people may work with in different scenarios in a controlled virtual environment can identify patterns and deviations from standard industry practices and detect cases such as overcharging, unnecessary services and coordinated activities or potential conflicts between organizations.
By combining AI-based anti-fraud technologies with advanced data analytics (depending on each jurisdiction's law), insurers can improve their fraud detection and prevention capabilities.
Combining artificial intelligence and human foresight may be the way forward
Over the past two decades, insurers have created specialized units to investigate and prevent insurance fraud.These departments play a key role in identifying fraudulent practices and reducing financial losses to the industry.Looking to the future, anti-fraud leaders face challenges related to managing costs and staffing shortages.Insurers who combine innovative technology with human expertise can detect fraud more effectively and optimize the entire process.Integrating artificial intelligence with professional expertise could result in billions of dollars in savings for policyholders.Attracting and retaining skilled professionals, along with investments in automation, will be critical to long-term success.
Learn more on the Insurance.bg website!
Frequently asked questions
Why is insurance fraud such a serious problem?
Answer: Insurance frauds lead to huge financial losses which are ultimately borne by the honest customers through higher premiums.In the US, the average family pays between $400 and $700 more per year precisely because of fraud.They also undermine confidence in the insurance system.Pressure on companies to compensate for losses leads to more expensive policies and limited coverage.
What are "soft" and "hard" insurance fraud?
Answer: "Soft" fraud involves exaggerating actual damage or costs, such as an inflated repair invoice."Hard" fraud is deliberately organized and involves staged incidents, fake thefts or forging documents.Although "soft" fraud may seem more minor, it is much more common.It is they who carry the greatest cumulative financial risk for the insurance system.
How does artificial intelligence detect insurance fraud?
Answer: AI analyzes massive data sets to detect patterns, anomalies, and inconsistencies in claims and documents.It can process text, images, video and sensor data simultaneously.This enables faster recognition of suspicious behavior and reduction of human error.Investigative experts focus on complex cases instead of routine inspection.
What does “multimodal AI” mean in insurance?
Answer: Multimodal AI combines different sources of information such as text, images, audio and geodata.For example, it can compare a photo of an accident with data from the car's telematics.Thus, a more accurate assessment of the reality of the damage is achieved.This greatly reduces the likelihood of false claims.
What is the effect of AI on the cost of insurance?
Answer: Better fraud detection allows insurers to limit financial losses.This reduces the need for constant premium increases.In the long run, customers can benefit from more stable and fair prices.In addition, claim processing becomes faster and more transparent.