AI is one of the most exciting developments in human-machine interaction. Enterprise leaders are keenly monitoring advancements in their fields related to AI. Although most people are convinced of the revolutionary potential of AI, organizations vary widely in their adoption of the technology, primarily due to the type of business, possible hazards, and degree of impact. Thus, it becomes a top-of-mind concern for executives in all industries including the insurance sector.
AI is transforming the way insurance companies operate, making them more efficient, customer centric and capable of managing risks effectively. Customers can derive advantage from AI's equitable pricing and individual solutions, while the workforce can benefit from enhanced productivity and the delegation of routine tasks.
Our primary objective here is to furnish valuable insights and perspectives concerning the significance and influence of Artificial Intelligence (AI) within the insurance sector. We accomplish this by drawing upon Coforge’s own examples and case studies. Additionally, we delve into the challenges and constraints encountered during the integration of AI in insurance processes. Subsequently, we outline a strategic path forward, offering a preview of forthcoming trends in this dynamic landscape.
Dimensions of Analytics maturity
According to a recent report from McKinsey,Insurance companies in Europe, the Middle East, and Africa have made substantial investments in advanced analytics. However, they have not fully realized the potential value of these investments. The survey also revealed that the top-performing companies leveraged advanced analytics to achieve a remarkable 25% increase in profitability. Following are the dimensions of analytics maturity:
Proliferation of AI use cases in Insurance functions
In recent times, the insurance industry has witnessed an expansion of use cases for Artificial Intelligence across various functional domains. These emerging AI applications are reshaping the way insurers operate and deliver services and hence it becomes imperative for every insurer to venture into the realm of AI. Here are some prospective use case scenarios within different stages of the insurance lifecycle:
Coforge Quasar AI: Reshaping Industries operating in diverse real world scenarios
We assist our clients in a diverse range of insurance sectors, including personal and commercial, specialty, large commercial, life and annuities, retirement, supplemental, and reinsurance. The insurance domain accounts for 23% of our annual revenue, with an extensive client base of over 100 active customers and a written premium exceeding $100 billion. Having been honored with the Duck Creek Standard Excellence Award at Formation'23, we have established strategic partnerships with leading companies such as Google Cloud, Microsoft Azure, Oracle, Snowflake, AWS, among others.
The latest addition in this journey is our own platform, Quasar AI, designed to build Enterprise AI capabilities to help our clients in realizing the use cases listed above.
The Quasar suite encompasses a range of advanced AI solutions tailored to address diverse data processing and analysis needs. These sophisticated offerings are designed to cater to various aspects of data analytics, utilizing cutting-edge technology to deliver valuable insights. Below, we elaborate on each component of the Quasar suite with their industrial applications.
Quasar Responsible AI
Our proprietary Responsible AI Engine and framework plays a pivotal role in identifying and explaining biases within datasets. Quasar Responsible AI uncovers potential risks and compliance challenges, providing options to govern, mitigate, and remediate third-party risks where necessary. In a world where anti-discrimination and privacy laws are becoming increasingly stringent, our Quasar Responsible AI Platform provides a robust framework for ethical AI integration. Here are some of our Responsible AI solutions:
Our success stories shaped by innovative solutions and transformative experiences
For each customer challenge, we follow a consistent approach of assessing the customer requirements for AI solutions. We assess the current state and required state to ensure adequate pre-deployment planning and sizing. We have a demonstrated approach for helping the client understand the clear AI requirements in relation to their business goals, product fit and gaps to identify which product will best serve the needs, solution performance requirements and data requirements, thus moving to the design phase.
Case study 1:
Problem statement: An American supplemental insurance company revolved around the escalating instances of fraudulent activities within their claims processing procedures. Traditionally, the insurer had been countering these fraudulent cases through a rule-based methodology that relied heavily on manual processing and investigative efforts. These investigations were primarily driven by the expertise and judgment of agents, investigators, and auditors. However, the company sought a more systematic and data-driven approach, signaling the need for the development of an advanced Fraud Analytics System for the next generation.
Solution: To address this challenge, we helped them with the development of a machine learning-based predictive model. The model being trained on a dataset including both normal and fraudulent instances learned to recognize the patterns and features associated with fraudulent behavior. On being presented with new, unseen data, the model used its learned knowledge to predict whether the case is likely to be normal or fraudulent. Based on the client’s need a threshold was set to influence the tradeoffs between false positives and negatives, depending on the system’s requirements and risk tolerance. The model’s performance was monitored, which included updating the model with new data and potentially adjusting features or hyperparameters.
Result: This yielded significant results, including a remarkable 500 basis point improvement in fraud detection rates. Notably, the transition from the rule-based manual identification of fraudulent activities to a fully automated system marked a pivotal shift in their anti-fraud measures.
Case study 2:
Problem statement: A leading insurance company in the UK encountered a challenge concerning the negotiation emails they received. These emails contained unstructured text along with various attachments in formats such as Excel, Word, or PDF. This presented a significant hurdle for the underwriting team, as they had to invest substantial effort in manually reviewing and processing these incoming requests.
Solution: In response to this challenge, we devised a comprehensive solution. Our approach involved the implementation of training for a diverse set of email templates. This solution leveraged Natural Language Processing (NLP) techniques to comprehend the nuances of natural language and extract the underlying intent of the emails by performing various steps such as tokenization, parts of speech tagging, named entity recognition, sentiment analysis, keyword extraction and thus classify them based on their content, and transform the incoming data into a structured format using both NLP and Machine Learning (ML) methodologies. The extracted intent can then be used for further classification or to trigger specific actions based on the purpose of the communication.
Result: The outcomes of this implementation were truly transformative. Notably, the cases handling speed increased by 300% compared to its original state. Additionally, the turnaround time for handling these emails significantly improved. This streamlined process culminated in expedited decision-making, allowing the company to respond more swiftly and effectively to negotiation requests.
Case study 3:
Problem statement: An insurance company in the United States grappled with a significant challenge: the need to manually verify a substantial volume of insurance documents daily. This task was not only cumbersome but also prone to errors, demanding considerable time and effort.
Solution: To address this challenge, we implemented a solution that streamlined the validation process. Instead of scrutinizing the entirety of each document, we helped them extract only the relevant content from the unstructured data using IDP/Document AI. This strategic approach resulted in substantial benefits for the company. The IDP system ingests various types of documents such as invoices, contracts, forms, or emails containing unstructured data. The system utilized NLP and ML techniques to understand the structure and content of the documents and transformed it into structured and actionable information. The extracted information was validated for accuracy, and quality assurance processes were applied to ensure reliable results.
Result: The processing time for each policy release saw a remarkable reduction of up to 75%. This transformation not only enhanced operational efficiency but also allowed the company to allocate resources more effectively, ultimately leading to improved outcomes in their document verification processes.
Unveiling Challenges and Limitations in the realm of AI
To attain optimal outcomes, several key factors act as pivotal contributors:
1. Technological and Economic feasibility
Technical viability comprises of technology’s accuracy and performance as well as the infrastructure and resources needed to create solutions. Thus, it becomes critical in the AI journey for any organization. The financial viability of a solution is the return on investment (ROI) of creating and implementing solutions compared to the financial gains from higher quality and efficiency. This also considers the labor market dynamics, including the relative costs of automation and human labor. The pace of adoption of Advanced analytics depends highly on the economic feasibility of a solution.
2. Regulatory Environment and Social acceptance
Another important factor is the social acceptance and the regulatory environment, both of which are influenced by how quickly important stakeholders and larger companies adopt new ideas. It can become a major hurdle in the path of any organization on the path of advanced analytics. It is crucial to prevent exploitation of vulnerable populations by measures such as Assessment of working conditions for labor force tasked with reinforcement learning.
3. Privacy concerns and Security Threats:
Measures to protect sensitive data and robust IP policy, practices and oversight have a major role to play in the adoption of advanced analytics. Thus, for any organization to succeed, specific controls become necessary such as diligence and vet data sources, secure necessary rights and licenses, monitor/filter training data. User access controls should be granted to appropriately restrict model and data access.
4. Investment and Impact Assessment:
Investment in advanced analytics is a fundamental step, but it is equally crucial to engage in regular assessments of the impact generated by such investments. This entails a continuous evaluation of the effectiveness and returns on investment (ROI) associated with advanced analytics initiatives. These assessments serve as crucial points, ensuring that resources are allocated judiciously and that the chosen analytics solutions align with the strategic objectives.
5. Speed of Execution of Use Cases:
The expeditious execution of use cases within the realm of advanced analytics is highly critical. Time is often critical, and delays in implementing analytics solutions can result in missed opportunities or less effective responses to evolving challenges. The pace at which use cases are conceptualized, developed, and deployed plays a vital role in achieving timely insights and outcomes that are responsive to dynamic market conditions.
6. Acquisition and Retention of Talent:
One of the cornerstones of success in advanced analytics is the recruitment and retention of the right talent. Identifying individuals with the requisite expertise in data science, machine learning, artificial intelligence, and related domains is essential. Establishment of strategies and environments that foster talent retention are also equally important. A cohesive team of skilled professionals can drive innovation, problem-solving, and the sustained development of advanced analytics capabilities.
According to a McKinsey report, despite ambitious hiring targets, most Europe, Middle East, and African insurers struggle to attract and retain talent.
Developing and supporting talent in AI
Attracting and retaining AI talents
AA capability building strategy
Navigating Tomorrow: Charting the path forward with AI
Some of the top insurance companies have already started leveraging AI leading to some innovative use cases:
AI-Driven Policy Creation and Claims Handling: Leading companies are harnessing the power of chatbots and AI assistants to streamline policy creation and handle user claims seamlessly across desktop and mobile platforms. This not only enhances customer convenience but also improves operational efficiency.
Giving Back Through Insurance: In a socially responsible shift, insurers are allowing customers to choose nonprofit organizations to receive underwriting profits as part of annual "Giveback" initiatives. This demonstrates a commitment to corporate social responsibility and aligns insurance with philanthropy.
Advanced Property Analysis: Insurers are leveraging AI to analyze a multitude of variables, including proximity to bodies of water and highways, in property underwriting. These data-driven insights enable more informed decisions, benefiting both insurers and those involved in property trading and investments.
Visual Risk Assessment with Smartphone Technology: Through the fusion of deep learning and computer vision techniques, companies are revolutionizing risk assessment. Customers can capture images and videos with their smartphones, allowing AI to swiftly analyze visual data and identify potential risks.
Personalized Customer Service: AI-powered systems are matching customers with the best-fitting customer representatives, enhancing the customer experience by providing tailored support and assistance.
Real-time Driver Safety Monitoring: Insurers are deploying AI-powered driver safety systems equipped with dual-facing cameras, computer vision, and proprietary algorithms. These systems monitor driver behavior in real time, identifying and preventing risky actions on the road.
Document Analysis: AI is being employed to analyze various forms of documents, including emails, PDFs, Excel files, and more. This technology streamlines document processing, improving accuracy and efficiency in insurance operations.
Estimating Vehicle Damage: AI-powered solutions are revolutionizing the process of estimating vehicle damage. By analyzing images and data, AI can accurately assess the extent of damage after accidents, enabling faster claims processing and reducing the need for manual inspections.
Within the insurance industry, there remains ample opportunity for the exploration of numerous additional use cases. Some of the potential use cases are:
Identifying Environmentally Responsible Clients for Underwriting Benefits: The possibility of identifying clients who adhere to sound environmental practices and protocols, thus warranting advantageous underwriting benefits.
Development of Personalized Healthcare Assistance for Employees: The exploration of personalized healthcare assistance programs tailored to the unique needs of employees, thereby enhancing their well-being.
Claim denials resolution: Gen-AI models can play a pivotal role in streamlining denials management. These models can summarize denial letters, consolidate denial codes, highlight relevant denial reasons, and provide contextualized next steps for managing denials.
Policyholder Communication: Chatbots and virtual assistants can provide policyholders with legal information, answer queries, and guide them through legal processes. AI-driven communications can ensure consistency and accuracy in policyholder interactions.
Training and Safety procedures: Training sessions can be conducted through simulation of hazardous scenarios thus enabling the client to minimize risk and take appropriate action in case of a hazardous event.
Smart qualification: This can be done on customer data to predict which customers are most likely to convert, for rapid qualification thus giving the customers the deserved attention by the agent.
Co-created products for newer ideas: Products can be co-created by humans and AI based on popular products in the past for different segments of customers, which can then be tested for a product market fit.
AI is a transformative force, empowering insurers to innovate, enhance customer experiences, and optimize operations. By embracing AI and exploring new use cases, insurers can thrive in the evolving insurance landscape. Coforge consistently strives to assist the clients in this endeavor.