Insurance is a $1.2 trillion industry in the U.S. alone, employing 2.9 million people.
Historically, the insurance industry hasn’t felt the effects of digital disruption, due to a strict regulatory environment, the scale required to create a risk portfolio, and the time needed to establish trust with customers. But in a recent IBM Institute for Business Value (IBV) survey, insurance executives identified changing market forces (such as increased competition and changing customer preferences) as the top driver affecting their enterprise.
The core function of the insurance industry, risk management, has gotten more complex as customer data continues to compound. Insurance companies collect data scattered across siloed business units in paper or various unstructured digital formats. In this data-rich environment, underwriting and claims management workers don’t have immediate access to the information needed for informed internal and external decision-making, leading to burnout and costly mistakes.
In fact, knowledge workers spend 30% of their time finding information required to complete their work. Due to the volume and complexity of unstructured data, manual analysis is tedious, time-consuming and expensive. This information disconnect makes maintaining compliance, avoiding fines and preserving brand reputation even more difficult for insurance companies in an ever-changing regulatory environment.
In the insurance industry, manual processes simply can’t scale alongside the scope and speed of business growth. So to get smarter about their knowledge management and big data processing approaches, insurers are implementing artificial intelligence (AI) across their operations to create better, personalized experiences for their employees and clients.
AI is the future of insurance: Smarter risk assessment, smarter operations
In the coming years, automation and AI solutions will roll out across every domain in the insurance industry. Some insurance providers empower knowledge workers to make better and faster data-driven decisions using AI-powered text-analytics platforms. A high-ROI application of AI, a text-analytics platform uses natural language processing to help improve worker productivity by revealing hidden insights in existing data sets and automating simple decisions or search queries in near real time. These gains in efficiency free workers to focus on more complex tasks and be more responsive, resulting in better customer experiences and higher customer satisfaction.
Let’s look at how AI-powered text-analytics platforms can digitally transform two typical use cases for insurance companies:
Underwriting
AI technology can increase efficiency and automate workflows by accelerating underwriting processes, delegating tasks for human attention, offering better data-informed insurance policies faster, and improving customer experiences. With AI, underwriters can accurately pinpoint optimal rates based on the individual customer for optimal risk management. AI-based pricing models also help reduce the time it takes to introduce new pricing frameworks across the underwriting lifecycle.
Underwriters can also use AI solutions to get answers fast and pass that information along to clients. For example, insurance companies can deploy chatbots externally and internally to help knowledge workers quickly deliver relevant insights through remote digital experiences during the risk assessment process. Employees can also use machine learning algorithms to augment traditional actuarial models, providing more accurate and consistent risk management. Knowledge workers can then focus more on “human touch” tasks that make customers feel valued.
Claims processing
From the moment a customer opens an insurance claim, AI technology can streamline the administrative process through process automation. Employees can use data science and AI solutions to analyze numerical and natural language data, referencing relevant insurance policy information, healthcare forms and other input documents along the way. AI can provide insightful recommendations based on claims management data analysis, helping knowledge workers determine eligible claims and what percentage of claims should be consistently paid out. These insights greatly accelerate decision-making, which can lead to better employee and customer experiences.
Machine learning algorithms can spot “red flags” more easily in fraudulent claims and risk management data, giving workers more time to spend on unique cases. The overall result is that high-volume, low-cost insurance claims like broken windshields can be quickly resolved, and knowledge workers can spend more time on more complex claims and fraud detection.
The end goal: Personalized client-centric experiences with artificial intelligence
With AI solutions, insurers can obtain a single view of the customer in near real time. While customer data exists across many traditional and siloed systems, a modernized AI-infused architecture can unify datasets and gather insights from various documents to make insights easily accessible to anyone across the claims processing or underwriting lifecycle.
This transparency creates an opportunity for insurers to provide client-centric customer journeys. With AI, the underwriters, claims processing teams, and insurance agents have access to deeper insights into each customer’s specific life circumstances and preferences, so every touchpoint in the customer life cycle becomes an opportunity to provide highly personalized advertisements and policy recommendations, quotes and more.
By fully integrating AI solutions into their business models and operations, insurers can empower knowledge workers with the information they need to reinforce existing customer relationships and take customer experiences to the next level.
Institutional insurers already have the longstanding trust and scale that earned their current business. If they can quickly integrate AI and other new technologies across their operations, especially in augmenting knowledge worker capabilities, they will be able to face today’s uncertainties and compete with threats coming from “insurtech” startups and other new market entrants.
Insurance is a $1.2 trillion industry in the U.S. alone, employing 2.9 million people.
Historically, the insurance industry hasn’t felt the effects of digital disruption, due to a strict regulatory environment, the scale required to create a risk portfolio, and the time needed to establish trust with customers. But in a recent IBM Institute for Business Value (IBV) survey, insurance executives identified changing market forces (such as increased competition and changing customer preferences) as the top driver affecting their enterprise.
The core function of the insurance industry, risk management, has gotten more complex as customer data continues to compound. Insurance companies collect data scattered across siloed business units in paper or various unstructured digital formats. In this data-rich environment, underwriting and claims management workers don’t have immediate access to the information needed for informed internal and external decision-making, leading to burnout and costly mistakes.
In fact, knowledge workers spend 30% of their time finding information required to complete their work. Due to the volume and complexity of unstructured data, manual analysis is tedious, time-consuming and expensive. This information disconnect makes maintaining compliance, avoiding fines and preserving brand reputation even more difficult for insurance companies in an ever-changing regulatory environment.
In the insurance industry, manual processes simply can’t scale alongside the scope and speed of business growth. So to get smarter about their knowledge management and big data processing approaches, insurers are implementing artificial intelligence (AI) across their operations to create better, personalized experiences for their employees and clients.
AI is the future of insurance: Smarter risk assessment, smarter operations
In the coming years, automation and AI solutions will roll out across every domain in the insurance industry. Some insurance providers empower knowledge workers to make better and faster data-driven decisions using AI-powered text-analytics platforms. A high-ROI application of AI, a text-analytics platform uses natural language processing to help improve worker productivity by revealing hidden insights in existing data sets and automating simple decisions or search queries in near real time. These gains in efficiency free workers to focus on more complex tasks and be more responsive, resulting in better customer experiences and higher customer satisfaction.
Let’s look at how AI-powered text-analytics platforms can digitally transform two typical use cases for insurance companies:
Underwriting
AI technology can increase efficiency and automate workflows by accelerating underwriting processes, delegating tasks for human attention, offering better data-informed insurance policies faster, and improving customer experiences. With AI, underwriters can accurately pinpoint optimal rates based on the individual customer for optimal risk management. AI-based pricing models also help reduce the time it takes to introduce new pricing frameworks across the underwriting lifecycle.
Underwriters can also use AI solutions to get answers fast and pass that information along to clients. For example, insurance companies can deploy chatbots externally and internally to help knowledge workers quickly deliver relevant insights through remote digital experiences during the risk assessment process. Employees can also use machine learning algorithms to augment traditional actuarial models, providing more accurate and consistent risk management. Knowledge workers can then focus more on “human touch” tasks that make customers feel valued.
Claims processing
From the moment a customer opens an insurance claim, AI technology can streamline the administrative process through process automation. Employees can use data science and AI solutions to analyze numerical and natural language data, referencing relevant insurance policy information, healthcare forms and other input documents along the way. AI can provide insightful recommendations based on claims management data analysis, helping knowledge workers determine eligible claims and what percentage of claims should be consistently paid out. These insights greatly accelerate decision-making, which can lead to better employee and customer experiences.
Machine learning algorithms can spot “red flags” more easily in fraudulent claims and risk management data, giving workers more time to spend on unique cases. The overall result is that high-volume, low-cost insurance claims like broken windshields can be quickly resolved, and knowledge workers can spend more time on more complex claims and fraud detection.
The end goal: Personalized client-centric experiences with artificial intelligence
With AI solutions, insurers can obtain a single view of the customer in near real time. While customer data exists across many traditional and siloed systems, a modernized AI-infused architecture can unify datasets and gather insights from various documents to make insights easily accessible to anyone across the claims processing or underwriting lifecycle.
This transparency creates an opportunity for insurers to provide client-centric customer journeys. With AI, the underwriters, claims processing teams, and insurance agents have access to deeper insights into each customer’s specific life circumstances and preferences, so every touchpoint in the customer life cycle becomes an opportunity to provide highly personalized advertisements and policy recommendations, quotes and more.
By fully integrating AI solutions into their business models and operations, insurers can empower knowledge workers with the information they need to reinforce existing customer relationships and take customer experiences to the next level.
Institutional insurers already have the longstanding trust and scale that earned their current business. If they can quickly integrate AI and other new technologies across their operations, especially in augmenting knowledge worker capabilities, they will be able to face today’s uncertainties and compete with threats coming from “insurtech” startups and other new market entrants.
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