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Will Bachman에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 Will Bachman 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.
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559. Paul Gaspar: AI Project Case Study

19:21
 
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Manage episode 401806853 series 1433158
Will Bachman에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 Will Bachman 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.

Show Notes:

In this episode of Unleashed, Paul Gaspar discusses his experience working with artificial intelligence at a major global insurance conglomerate in Japan. The company faced pressure to streamline operations and reduce costs within its auto business. Paul, who was in a role leading the data science function, suspected that the claims area in insurance was a target-rich environment for delivering value with advanced analytics and technology. He found that similar processes were being utilized on claims regardless of the size, leading to the opportunity to put analytical rigor behind the claims estimation process.

AI Use for Processing Insurance Claims

Paul and his team looked at information flows at various points in the process, specifically evaluating how information collected at the time of the accident could be used to provide insight on losses. Using this information, they built predictive models using AI techniques that would allow them to predict the ultimate value of these claims from a $1 perspective, using a subset of the initial information collected at the time of loss. By building models that could do this quickly and accurately, they were able to set thresholds that would allow for automated processing and payment of claims amounts on about a quarter of the total claims volume. This reduced the workload for the team handling claims and sped responsiveness to customers with smaller claim amounts.

The Process of Assessing Information

Paul explains the process of assessing the quality, consistency, and reliability of information for a client. This involves assessing the types of information, blending them with data analysts experienced with using different modeling techniques and programming languages. Paul and his team used Python to investigate particular approaches, and testing results to identify useful data elements for creating meaningful insights. This process is not necessarily feasible for a data analyst with minimal data science knowledge. Instead, a step-by-step approach involves evaluating the data, considering viable modeling techniques, and experimenting with them to ensure accuracy, speed, and processing power. A team of experienced data scientists can help guide the technical approach and modeling techniques used in the case. This approach is essential for evaluating claims and determining the appropriateness of claims based on the available data. To ensure precision across various claim types, it is crucial to segment claims by value and look at the ones with the lowest value. This helps identify potential risks and minimizes leakage, which is the risk of overpaying for claims relative to processing costs.

Predictive analytics is a complex art and science, and it is essential to be careful about how and where to use it, ensuring that risks are well understood and balanced against the benefits of the process.To turn a scalable business process into a working scalable business process, Paul states that change management work must be done across various functional areas. This includes ensuring that information is passed into payment systems, how automation impacts existing processes, and how to contact customers and inform them of potential benefits.

Building AI Algorithms to Prevent Human Errors

In the claims process, Paul states that human errors can be a significant issue, as they can lead to false positives and false negatives. To prevent human errors, AI algorithms should be trained to match human judgments and set error tolerance thresholds. This is a time-consuming part of the process, and it is essential to work with claim handling professionals to assess the performance of the models and identify errors. He also mentions that risk management is crucial in ensuring that systems make accurate decisions and avoid making mistakes. Machine learning operations (ML ops) have emerged as a concept that accounts for model performance over time, and it is crucial to continually monitor and adjust models as needed. To ensure that the model does not become overly sympathetic to human errors, it is essential to conduct testing and monitoring over time. Companies that excel in this field have developed software programs that allow for systematic monitoring of decisions. By setting thresholds and balancing processing time and error, companies can set acceptable thresholds and auto-process claims at risk-acceptable levels.

The Evolution of Predictive AI

Paul discusses the evolution of predictive AI, specifically generative AI, which uses existing knowledge bases and training models to generate content that is most likely to be related to an end user's query. This is the basis of foundational models used by open AI and Perplexity to create a new paradigm and use case for predictive AI. The accessibility, power, and intuitive nature of these models make them exciting for experimentation. Generative AI tools have become multimodal, allowing them to take textual, voice, image, or video inputs and respond to queries about that type of content. This allows for an incredible range of possibilities, even in the mobile first world. For example, in the case of auto claims, the estimation process could change from a low value subset to a higher value and sophistication of claims.

The multimodal input, the ease of interaction with providing information to these tools, and the ability to access from both practitioner and end user perspectives are key game changers in the future of predictive AI. Paul emphasizes the importance of change management in implementing AI tools in corporations.

Timestamps:

01:04 Implementing AI in claims handling at an insurance company

08:34 Using predictive analytics in claims processing

13:41 AI-powered claims processing and error management

18:25 Generative AI's transformative potential in various industries

Links:

LinkedIn: https://www.linkedin.com/in/paulmgaspar/ Unleashed is produced by Umbrex, which has a mission of connecting independent management consultants with one another, creating opportunities for members to meet, build relationships, and share lessons learned. Learn more at www.umbrex.com.

  continue reading

567 에피소드

Artwork
icon공유
 
Manage episode 401806853 series 1433158
Will Bachman에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 Will Bachman 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.

Show Notes:

In this episode of Unleashed, Paul Gaspar discusses his experience working with artificial intelligence at a major global insurance conglomerate in Japan. The company faced pressure to streamline operations and reduce costs within its auto business. Paul, who was in a role leading the data science function, suspected that the claims area in insurance was a target-rich environment for delivering value with advanced analytics and technology. He found that similar processes were being utilized on claims regardless of the size, leading to the opportunity to put analytical rigor behind the claims estimation process.

AI Use for Processing Insurance Claims

Paul and his team looked at information flows at various points in the process, specifically evaluating how information collected at the time of the accident could be used to provide insight on losses. Using this information, they built predictive models using AI techniques that would allow them to predict the ultimate value of these claims from a $1 perspective, using a subset of the initial information collected at the time of loss. By building models that could do this quickly and accurately, they were able to set thresholds that would allow for automated processing and payment of claims amounts on about a quarter of the total claims volume. This reduced the workload for the team handling claims and sped responsiveness to customers with smaller claim amounts.

The Process of Assessing Information

Paul explains the process of assessing the quality, consistency, and reliability of information for a client. This involves assessing the types of information, blending them with data analysts experienced with using different modeling techniques and programming languages. Paul and his team used Python to investigate particular approaches, and testing results to identify useful data elements for creating meaningful insights. This process is not necessarily feasible for a data analyst with minimal data science knowledge. Instead, a step-by-step approach involves evaluating the data, considering viable modeling techniques, and experimenting with them to ensure accuracy, speed, and processing power. A team of experienced data scientists can help guide the technical approach and modeling techniques used in the case. This approach is essential for evaluating claims and determining the appropriateness of claims based on the available data. To ensure precision across various claim types, it is crucial to segment claims by value and look at the ones with the lowest value. This helps identify potential risks and minimizes leakage, which is the risk of overpaying for claims relative to processing costs.

Predictive analytics is a complex art and science, and it is essential to be careful about how and where to use it, ensuring that risks are well understood and balanced against the benefits of the process.To turn a scalable business process into a working scalable business process, Paul states that change management work must be done across various functional areas. This includes ensuring that information is passed into payment systems, how automation impacts existing processes, and how to contact customers and inform them of potential benefits.

Building AI Algorithms to Prevent Human Errors

In the claims process, Paul states that human errors can be a significant issue, as they can lead to false positives and false negatives. To prevent human errors, AI algorithms should be trained to match human judgments and set error tolerance thresholds. This is a time-consuming part of the process, and it is essential to work with claim handling professionals to assess the performance of the models and identify errors. He also mentions that risk management is crucial in ensuring that systems make accurate decisions and avoid making mistakes. Machine learning operations (ML ops) have emerged as a concept that accounts for model performance over time, and it is crucial to continually monitor and adjust models as needed. To ensure that the model does not become overly sympathetic to human errors, it is essential to conduct testing and monitoring over time. Companies that excel in this field have developed software programs that allow for systematic monitoring of decisions. By setting thresholds and balancing processing time and error, companies can set acceptable thresholds and auto-process claims at risk-acceptable levels.

The Evolution of Predictive AI

Paul discusses the evolution of predictive AI, specifically generative AI, which uses existing knowledge bases and training models to generate content that is most likely to be related to an end user's query. This is the basis of foundational models used by open AI and Perplexity to create a new paradigm and use case for predictive AI. The accessibility, power, and intuitive nature of these models make them exciting for experimentation. Generative AI tools have become multimodal, allowing them to take textual, voice, image, or video inputs and respond to queries about that type of content. This allows for an incredible range of possibilities, even in the mobile first world. For example, in the case of auto claims, the estimation process could change from a low value subset to a higher value and sophistication of claims.

The multimodal input, the ease of interaction with providing information to these tools, and the ability to access from both practitioner and end user perspectives are key game changers in the future of predictive AI. Paul emphasizes the importance of change management in implementing AI tools in corporations.

Timestamps:

01:04 Implementing AI in claims handling at an insurance company

08:34 Using predictive analytics in claims processing

13:41 AI-powered claims processing and error management

18:25 Generative AI's transformative potential in various industries

Links:

LinkedIn: https://www.linkedin.com/in/paulmgaspar/ Unleashed is produced by Umbrex, which has a mission of connecting independent management consultants with one another, creating opportunities for members to meet, build relationships, and share lessons learned. Learn more at www.umbrex.com.

  continue reading

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