Over the past decade, artificial intelligence (AI) has become the breakthrough technology in the insurance industry. In addition to data transformation, he has been instrumental in creating more efficient application and claims management systems, as well as increasing hyper-personal insurance products and services. But perhaps its most significant impact is in risk management, particularly in claims and underwriting, where it is used with other technologies, such as machine learning (ML), to identify and minimize risk, detect fraud and find a balance between risks. and opportunities.
Optimize risk selection
Insurers are leveraging AI to identify underwriting risks and optimize risk selection. Clever algorithms scour industry databases to select relevant customer data, effectively separating it into pre-determined price categories. AI-based risk detection is used to identify credit risk, governance and compliance risk, operational risk, market risk, liquidity risk, business risk, cyber risk and criminal risks, such as fraud or money laundering.
Embedded AI and real-time integration with industry databases have also helped make the underwriting process, including underwriting and pricing, faster and more efficient, significantly improving the customer experience. For insurance companies, these technologies are rapidly emerging as a key competitive tool for customer acquisition and retention. Considering the importance of IoT and tracking devices in our lives, and their access to accurate and critical data, AI-related technologies will gain importance in data analysis, selection of risks and pricing.
Intelligent complaint handling
From chatbots for quick resolutions to ML apps, smart tools have completely redesigned claims handling, making it more efficient while reducing risk. In risk management, data analytics has helped automate fraud detection, identify trends in claim volumes and further strengthen loss analysis.
One of the biggest concerns for an insurance company is fraudulent claims. Investigating each claim can take up valuable time and resources. Today, visual analytics, involving the analysis of images and videos, has accelerated processes. Insurance companies can conduct preliminary investigations with minimal resources while relying on highly accurate data, eliminating fraudulent claims.
Predictive risk management is a crucial aspect of any insurance business. While underwriters do proper risk selection when deciding on pricing, there is only so much data a human can process. With the massive amounts of data we have today, predictive analytics has necessarily been supported by AI-based technologies. Intelligent predictive algorithms can sift through the data to identify trends in outlier claims that lead to unexpected huge losses.
This allows insurance companies to plan their policies in such a way as to reduce the risk of outlier claims. Predictive analytics can also help identify common risk factors to encourage safe behavior, thereby reducing overall complaint volume. For example, healthcare insurtech looks at hospitalization data to identify high-risk lifestyles. Therefore, the insurance company can encourage its customers to adopt safe practices that reduce the risk of hospitalization.
One of the biggest challenges presented by AI-based solutions is fixing liabilities. The shift from human to technology in decision-making creates a gray area in decision-making that could eventually lead to governance and compliance issues. As embedded AI technologies become a critical part of the underwriting process, we need to be aware of unintended biases that may arise from their implementation. While algorithms are presented as fail-safe mechanisms to calculate risk, these need to be applied with certain socio-cultural factors in mind, and this is where machines can make mistakes.
Failure to consider these factors can result in two main liabilities: bias in claims settlement and discriminatory underwriting. Insurtech algorithms decide subscription prices based on factors such as gender, creditworthiness and social class. The model output can be biased against any factor even if the other variables meet the desired standard. Similarly, in the case of claims, it may reject claims based on fraud detection error.
Human-AI collaboration is not only important to ensure the “human” factor, it is also a necessary risk mitigation strategy. While machines can perform complex calculations, we need humans for emotional intelligence, for example to identify biases and to ensure human-centered results, creating added value in products and services.
An article published last year pointed out the vulnerability of AI-ML technologies to unintended and intentional risks, calling for human oversight of critical decisions. For example, human intervention is required to determine patterns of bias or discrimination in claims or underwriting. It also opens up the possibility of new business models addressing future liabilities that may arise from the use of AI, such as robo-boards.
AI and Blockchain
AI has also been instrumental in the transition to blockchain in the insurance industry. Blockchain’s distributed digital ledger format decentralizes data, facilitates access, and ensures transparency. AI automates Blockchain data collection, allowing adjusters to settle claims quickly with less risk of fraud. Similarly, IoT with blockchain can foster hyper-personalization while reducing the risk of discriminatory underwriting by providing the underwriter with highly accurate and relevant data. In this way, he can still take advantage of the hyper-personalization of insurance products.
More importantly, the transparent nature of the blockchain minimizes the risk of fraud, while optimizing risk selection. For example, blockchain-augmented AI-powered healthcare analytics ensures accurate health record keeping, helping to reduce the risks associated with underwriting or selecting claims.
With the growing importance of embedded AI-based technologies in our lives, insurers have access to unprecedented amounts of data. This has provided them with an unparalleled view of their consumers, enabling them to improve the customer experience, develop personalized products and add value to the insurance value chain. However, we must remain vigilant in the face of the risks posed through the use of these technologies. It is essential that insurance companies undertake frequent internal risk assessments to assess the security of AI systems and prepare for possible failures.
Written by Hersh Shah, CEO, India Affiliate, Institute of Risk Management and Rohit Boda, Managing Director, JB Boda Group
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