Voice AI Risk Signaling: Using Home Assistant Devices to Detect Undeclared Property Hazards

Аннотация

With the increase in smart home adoption, VOLUME Vol.06 Issue 01 2024 voice-enabled devices like Amazon Alexa, Google Home, CITATION and Apple Siri are becoming increasingly abundant. Rachit Jain. (2024). Voice AI Risk Signaling: Using Home Assistant Devices Most of the new homes use these smart devices, and the to Detect Undeclared Property Hazards. The American Journal of Engineering and Technology, 6(06), 43–48. old ones are upgrading to integrate these voice-enabled https://doi.org/10.37547/tajet/Volume06Issue05-06 assistants. This paper explores a novel study for using COPYRIGHT voice data, with user consent, to identify the undeclared © 2024 Original content from this work may be used under the terms of the creative commons attributes 4.0 License. or non-reported risks within residential properties. By analyzing the speech using the natural language patterns, complaint frequency, and targeted keywords signals, we propose an Artificial Intelligence-based model to measure underlying risks that is not available in the traditional underwriting models has a very high potential to translate risk profiling dynamically, which will lead to improved pricing accuracy, fair pricing and diminish claim leakage in the property insurance. Keywords: Voice AI, Property Insurance, Risk Detection, Smart Home, NLP, Underwriting, Risk Scoring, Digital Insurance, Behavioral Analytics, Privacy-Aware AI 1. Introduction The whole property and casualty insurance industry is experiencing digital transformation by leveraging the IoT sensor data, Artificial intelligence models, and real-time dynamic data to enhance insurers’ underwriting and the claim process. Though a critical loophole exists, which is underreported or unreported risks in the homes, like mold, plumbing issues, poor electricity infrastructure, etc. These issues frequently remain unnoticed till the time a claim is reported, which creates adverse selection and inefficient pricing. Most of the time, homeowners The American Journal of Engineering and Technology 43 https://www.theamericanjournals.com/index.php/tajet neglect these issues, imagining this is something that legal and ethical ramifications, showing how the cloud- can be dealt with easily, but that’s not the case. To fill based recording can be an important evidence in this gap in the home insurance space, we introduced a detecting domestic violence used to solve the cases novel concept in the paper on Voice AI risk signaling. The using the data. This helps the detectives in identifying voice-enabled home assistant devices behave as passive the victims of abuse.[4] The voice is captured from the risk identifiers through speech data analysis using devices and is sent to the cloud, where it is stored. natural language processing[1]. This paper aims to propose a new layer of proactivity and behavior-based Additionally, in a study, Kumar, Gupta, and Sapra underwriting. explained that integrating Natural language processing to convert speech to text is effective. NLP captures the 2. Literature Review user’s speech input and processes it into text based on The current smart home insurance workflow is fully vocal parameters like pitch, loudness, and intonation. dependent on the structured sensor data of the devices They have calculated the application's performance installed at the user’s home. These sensors detect water using hidden Markov models, showing strong results leaks, smoke detection, and electrical wiring issues, with 91.5% precision, 95.4% recall, 86.8% F1 score, and which offer only binary or threshold-based insights. In 89% accuracy. This exposes that the text-to-speech contrast, human voice is enriched with context, conversion is accurate and captures the correct emotions, and behavior patterns. Voice-based models information.[5] have proved in mental health, elder care, and sentiment detection applications, but their potential use the home Lastly, in one of the studies, it was highlighted that while or property insurance has not been explored yet. voice assistant awareness is high (90%) and usage is Previous work has also discovered acoustic anomaly widespread (72%), most users still rely on them for basic detection, such as glass break, fire alarms, but the tasks like playing music or checking the weather. Around elucidation of speech for insurance risk detection is an 50% of people purchased these devices for their regular emerging field. small work. However, trust remains the major challenge In a real-time use case, a murder case was solved by for most users, and this is the biggest challenge in voice Alexa, in which a husband killed his wife and was jailed commerce. [6] for 20 years. Voice recording on the Amazon Alexa helped bring the victim to justice. The detectives Based on the above literature study, it is evident that the discovered that the voice records recorded by Alexa at voice detected by the smart home devices is providing the time of the murder helped them to solve the case. evidenc