On the cold night of December 23rd, 2017, a world record was made for the fastest claim paid out - 3 seconds. A leading US InsurTech utilised Artificial Intelligence to pay out the quickest claim in the insurance industry’s history. Three seconds was all it took for the Artificial Intelligence (AI) to review, cross-reference, run anti-fraud algorithms, approve and instruct the bank to transfer the claim amount.
The seismic shift in how things are operated in the insurance industry is underway. Artificial Intelligence and Machine Learning (ML) are fusing with traditional insurance industry roles like actuarial sciences to revolutionise the landscape. Actuarial sciences, traditionally focused on risk assessment and pricing, find new dimensions through AI and ML, empowering InsurTechs with unparalleled insights, efficiency, and innovation.
Actuarial Sciences: The Foundation of Insurance
Actuarial sciences form the bedrock of insurance, utilising mathematical and statistical methods to assess risk and determine premiums. Historically, actuaries relied on historical data and mathematical models to predict future outcomes and set insurance rates. While this approach has been effective, it often needed more agility to adapt to rapidly changing risk landscapes.
AI & ML: Revolutionising Risk Assessment
Enter AI and ML, heralding a new risk assessment and management era. These technologies enable InsurTech companies to analyse vast amounts of data in real time, uncovering patterns and trends that traditional actuarial methods might overlook. By leveraging AI and ML algorithms, insurers can enhance underwriting accuracy, detect fraudulent claims, and personalise insurance offerings based on individual risk profiles.
.jpg)
Alignment of Actuarial Sciences with AI & ML
The synergy between actuarial sciences and AI & ML is reshaping the insurance industry in several key ways:
Enhanced Risk Assessment: AI & ML algorithms complement actuarial models by processing diverse data sources, including social media, IoT devices, and telematics. This holistic approach gives insurers a more comprehensive understanding of risk factors, leading to more accurate risk assessments and pricing.
Dynamic Underwriting: Traditional underwriting processes are often rigid and time-consuming. AI & ML-powered underwriting systems offer flexibility and speed by automating data analysis and decision-making. InsurTechs can tailor insurance policies in real-time based on evolving risk profiles, customer behaviour, and market trends.
Fraud Detection and Prevention: Fraudulent claims pose a significant challenge to insurers, resulting in substantial financial losses. AI & ML algorithms excel at identifying abnormal patterns indicative of fraud, enabling insurers to detect suspicious claims with greater precision and efficiency.
Customer Experience Optimisation: In the digital age, customer experience is paramount. By integrating AI-powered chatbots, virtual assistants, and personalised recommendation engines, InsurTechs can deliver seamless and personalised experiences to policyholders, enhancing customer satisfaction and loyalty.
Conclusion
InsurTechs relying on AI, machine learning, and related technologies as their primary tech source saw a consistent annual investment increase of 20%, as per CAGR from 2015 to 2020.
As AI and ML continue to advance, the possibilities for InsurTech innovation are limitless. From predictive analytics to autonomous underwriting and claims processing, the marriage of actuarial sciences with AI & ML promises to revolutionise every aspect of the insurance value chain. As the insurance industry embraces digital transformation, the synergy between traditional expertise and cutting-edge technology will drive unprecedented growth, efficiency, and value for insurers and policyholders.
