The American Journal of Engineering and Technology
43
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TYPE
Original Research
PAGE NO.
43-48
OPEN ACCESS
SUBMITED
19 March 2024
ACCEPTED
08 April 2024
PUBLISHED
30 May 2024
VOLUME
Vol.06 Issue 05 2024
CITATION
Rachit Jain. (2024). Voice AI Risk Signaling: Using Home Assistant Devices
to Detect Undeclared Property Hazards. The American Journal of
Engineering and Technology, 6(05), 43
–
48.
COPYRIGHT
© 2024 Original content from this work may be used under the terms
of the creative commons attributes 4.0 License.
Voice AI Risk Signaling:
Using Home Assistant
Devices to Detect
Undeclared Property
Hazards
Independent Researcher Downingtown, Pennsylvania, USA
Abstract:
With the increase in smart home adoption,
voice-enabled devices like Amazon Alexa, Google Home,
and Apple Siri are becoming increasingly abundant.
Most of the new homes use these smart devices, and the
old ones are upgrading to integrate these voice-enabled
assistants. This paper explores a novel study for using
voice data, with user consent, to identify the undeclared
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
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neglect these issues, imagining this is something that
can be dealt with easily, but that’s not the case. To fill
this gap in the home insurance space, we introduced a
novel concept in the paper on Voice AI risk signaling. The
voice-enabled home assistant devices behave as passive
risk identifiers through speech data analysis using
natural language processing[1]. This paper aims to
propose a new layer of proactivity and behavior-based
underwriting.
2.
Literature Review
The current smart home insurance workflow is fully
dependent on the structured sensor data of the devices
installed at the user’s home. These sensors detect water
leaks, smoke detection, and electrical wiring issues,
which offer only binary or threshold-based insights. In
contrast, human voice is enriched with context,
emotions, and behavior patterns. Voice-based models
have proved in mental health, elder care, and sentiment
detection applications, but their potential use the home
or property insurance has not been explored yet.
Previous work has also discovered acoustic anomaly
detection, such as glass break, fire alarms, but the
elucidation of speech for insurance risk detection is an
emerging field.
In a real-time use case, a murder case was solved by
Alexa, in which a husband killed his wife and was jailed
for 20 years. Voice recording on the Amazon Alexa
helped bring the victim to justice. The detectives
discovered that the voice records recorded by Alexa at
the time of the murder helped them to solve the case.
sounding 'out of breath' when saying 'Turn on - Alexa'
during the early hours of the morning, when the
murderer killed his wife. This shows how the speech was
able to resolve the case; otherwise, it could have
remained unnoticed. [2]
In another study, it is mentioned that the virtual
assistants played a key role in solving a mystery case
when voice recordings from an Amazon Echo device
were used as evidence in a murder investigation. In the
U.S. "Bates" case, police sought access to audio data
captured by the device to uncover details about the
crime, raising major legal and ethical questions. The case
highlighted how virtual assistants, while designed for
convenience, can also act as silent witnesses, potentially
aiding law enforcement but also challenging privacy
rights and data ownership[3].
Furthermore, Tabetha explained in his study about the
legal and ethical ramifications, showing how the cloud-
based recording can be an important evidence in
detecting domestic violence used to solve the cases
using the data. This helps the detectives in identifying
the victims of abuse.[4] The voice is captured from the
devices and is sent to the cloud, where it is stored.
Additionally, in a study, Kumar, Gupta, and Sapra
explained that integrating Natural language processing
to convert speech to text is effective. NLP captures the
user’s speech input and processes it into text based on
vocal parameters like pitch, loudness, and intonation.
They have calculated the application's performance
using hidden Markov models, showing strong results
with 91.5% precision, 95.4% recall, 86.8% F1 score, and
89% accuracy. This exposes that the text-to-speech
conversion is accurate and captures the correct
information.[5]
Lastly, in one of the studies, it was highlighted that while
voice assistant awareness is high (90%) and usage is
widespread (72%), most users still rely on them for basic
tasks like playing music or checking the weather. Around
50% of people purchased these devices for their regular
small work. However, trust remains the major challenge
for most users, and this is the biggest challenge in voice
commerce.
[6]
Based on the above literature study, it is evident that the
voice detected by the smart home devices is providing
evidence in the mystery cases where no one can be able
to trace the victim. The existing literature lacks the same
use case in insurance, and that gap we are going to cover
in this paper.
3. METHODOLOGY
•
Data Collection
:
The user will be asked to opt for the data
collection by the insurance company in return
for a discount and fair pricing. With user opt-in,
anonymized transcripts from the voice-enabled
home assistant interactions are collected over a
regular interval of time. These primarily include
everyday
conversations,
queries,
and
complaints about the home. The speech-to-text
workflow converts audio data into structured
text for the analysis. The data will be stored on
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the provider’s cloud. Below is the Python code
that can be utilized for the same.
import speech_recognition as sr
recognizer = sr.Recognizer()
with
sr.AudioFile("sample_audio.wav")
as
source:
audio = recognizer.record(source)
transcript
=
recognizer.recognize_google(audio)
print(transcript)
Risk Signal Dictionary
:
A curated set of keywords and phrases is developed
through expert consultation and historical claims
analysis. Examples include: "leaking pipe," "weird
smell," "breaker tripped again," "can’t sleep because of
the cold," and "sparks came out." The below Python
code shows how the keywords will be stored.
risk_keywords = ["leaking pipe", "weird smell", "sparks",
"breaker tripped", "water dripping"]
def detect_risk_phrases(transcript):
return [phrase for phrase in risk_keywords if phrase in
transcript.lower()]
Natural Language Processing (NLP)
:
The transformer-based models, such as BERT, RoBERTa
are fine-tuned on labeled data to classify segments of
speech as risk-related or neutral. These models would
be able to identify the speech that is useful for the
underwriting model. The multi-label classification
techniques are used to handle overlapping issues, e.g.,
electrical & humidity. This classification helps in the
segregation of the risk data. The tokenization is handled
using WordPiece embedding with positional encoding,
and finally, the training is performed using a weighted
binary cross-entropy loss to mitigate label imbalance.
Fig. 1 NLP Text to Speech converter
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Below is the Python code snippet for the same.
from transformers import BertTokenizer,
BertForSequenceClassification
import torch
tokenizer = BertTokenizer.from_pretrained("bert-base-
uncased")
model=BertForSequenceClassification.from_pretrained
("bert-base-uncased", num_labels=2)
inputs = tokenizer("There is a weird smell in the
kitchen", return_tensors="pt")
outputs = model(**inputs)
predictions = torch.argmax(outputs.logits, dim=-1)
Temporal and Sentiment Analysis
:
The risk-related remarks or comments are analyzed over
time for frequency, intensity, and sentiment polarity. An
increase in urgency or negativity may indicate
deteriorating home property conditions, which are very
sensitive. A sliding window approach with exponential
decay weights recent expressions more heavily in the
underwriting scoring models. Below is the Python code
snippet for the same.
from vaderSentiment.vaderSentiment import
SentimentIntensityAnalyzer
analyzer = SentimentIntensityAnalyzer()
score = analyzer.polarity_scores("I’m tired of the
dripping noise every night")
print(score)
Risk Scoring Engine
:
The homeowners will receive a composite risk score
derived from the volume, severity, and diversity of
detected issues at home, adjusted for demographic and
geographic factors. The score is updated weekly, using a
dynamic rolling average to stabilize transient anomalies.
Below is the Python code snippet for the same.
def calculate_risk_score(phrases_detected,
sentiment_scores, time_decay=0.85):
base_score = len(phrases_detected) * 10
sentiment_modifier = -
sentiment_scores['compound'] * 5
adjusted_score = (base_score + sentiment_modifier)
* time_decay
return round(adjusted_score, 2)
4. Case Study Simulation
We have generated a synthetic dataset of 10,000 voice
assistant transcripts using the generative language
models, as real-time data requires a lot of privacy and
safety considerations. We are incorporating both benign
and hazard-related content. Data augmentation
techniques like back translation and contextual
synonym replacement ensured linguistic variability. A
fine-tuned BERT model achieved:
•
Risk classification accuracy: 92.3%
•
Precision: 89.7%, Recall: 93.8%
•
Early warning detection: Identified 78% of
emerging risks at least 3.2 months before
physical inspection or sensor triggers.
An example insight: A simulated household showed
recurring complaints about "damp smell in the
basement" and "water dripping noise" weeks before a
major water damage claim. The model successfully
flagged this as a high-risk case.
5. Ethical and Privacy Considerations
This proposed framework highlights full user
transparency and ethical AI design. Key principles
include:
•
Opt-in Consent
: Users must explicitly agree to
share voice data for insurance analysis for the
insurer. They will get a document for their
consent approval.
•
Data Anonymization
: Personal identifiers are
stripped from transcripts before model
processing to make sure privacy is maintained.
•
Right to Opt-Out and Erasure
: Users can revoke
consent or request data deletion at any time if
they feel uncomfortable or useless. For those
customers, the traditional method will be used
to rate the policy, and they will give up their
discount.
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•
Transparency and Disclosure
: Insurers must
disclose how data will be used and not used,
e.g., not for automatic premium hikes without
review. The report should be shared with the
insured on what basis they think the premium
should go up.
•
Compliance
: This proposed framework will
adhere to GDPR, CCPA, and NAIC model privacy
regulations.
6. POTENTIAL APPLICATIONS
•
Dynamic Underwriting
: The Traditional risk
models are updated periodically based on the
claims recorded or insurance factor increase,
but the voice-based models offer dynamic
underwriting and adaptive pricing.
•
Proactive Risk Mitigation
: Regular alerts and
recommendations can be sent to insureds based
on voice-
detected issues, e.g., “Consider
inspecting your HVAC system”, “Check your
kitchen plumbing”.
•
Claims Validation
: The Claims adjusters can
verify whether the issue was previously
captured, helping them reduce fraud and
expedite payouts.
•
Customer Segmentation
: Behavioral data may
expose proactive versus reactive homeowners,
filtering engagement strategies.
7. LIMITATIONS AND FUTURE WORK
This is a conceptual model and has not yet been
implemented in a real-world insurance product.
Challenges include:
•
Data Access
: Acquisition of consent from home
smart device users for real-world pilot studies is
a challenge, as people might see this as a threat
to their privacy.
•
Bias and Misclassification
: A need to address
fairness and prevent overfitting on certain
demographic or linguistic groups. Every
language must be carefully verified.
•
Multimodal Fusion
: Future iterations of this
model should integrate voice data with visual
(CCTV), environmental (sensors), and geospatial
data for complete risk modeling.
•
Federated Learning Potential
: Future work
could implement privacy-preserving model
training directly on edge devices.
•
Regulatory Hurdles
: Insurance regulators will
need clear frameworks on the acceptable use of
unstructured data and what can’t be translated.
8. CONCLUSION
Voice AI Risk Signaling heads a revolutionary direction in
property insurance. Capturing the undeclared factors of
home maintenance and upcoming issues through
natural language processing, insurers gain a new
parameter for their insurance model to rate the policies.
By following the privacy rules and regulations, ethical
usage, this model promises to improve the safety, trust,
fairness, and actuarial accuracy. Future developments
can further implement this architecture into the smart
insurance workflow.
REFERENCES
1.
Kamath, U., Liu, J., & Whitaker, J. (2019).
Deep
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Man jailed for life after voice recordings on Amazon
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(2023, March 24).
Daily Mail
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Tabetha Soberdash, Domestic Violence in the Era of
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TECH., no. 1, 2020
5.
R. Kumar, M. Gupta and S. R. Sapra, "Speech to text
Community Application using Natural Language
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