AMERICAN ACADEMIC PUBLISHER
https://www.academicpublishers.org/journals/index.php/ijns
1
INTERNATIONAL JOURNAL OF NETWORKS AND SECURITY (ISSN: 2693-387X)
Volume 05, Issue 02, 2025, pages 01-29
Published Date: - 07-07-2025
Doi: -
https://doi.org/10.55640/ijns-05-02-01
Predictive Risk Modeling in P&C Insurance Using Guidewire
DataHub and Power BI Embedded Analytics
Kawaljeet Singh Chadha
University of the Cumberlands, Williamsburg, KY, USA
ABSTRACT
P&C insurers are increasingly pressured to identify and effectively predict risk. While traditional methods, such as
actuarial models and manual assessments, are effective for identifying patterns in large-scale policy and claims data,
they struggle to capture complex patterns, like resistance curves. This paper examines how predictive risk modelling
can be implemented in practice using Guidewire DataHub and Power BI Embedded Analytics. Power BI is used for
interactive visualization and real-time decision support, whereas Guidewire Data Hub is utilized as a centralized
platform for storing and managing structured insurance data. It utilized structured data from claim history,
underwriting attributes, policy details, and customer profiles to build a predictive model. Machine learning
algorithms, such as Random Forest and Logistic Regression, were then applied to classify policyholders as High,
Medium, or Low risk after preprocessing and feature selection. Standard metrics (accuracy, precision, recall, ROC-
AUC) were used to evaluate model performance. The Random Forest classifier achieves an accuracy of 84% and
identifies high-risk profiles most effectively. It then integrated these predictions with Power BI dashboards, allowing
underwriters and analysts to explore risk at both the individual and portfolio levels. The study illustrates how
building data platforms that integrate machine learning and embedded analytics facilitates more innovative
underwriting, fraud detection and pricing. In a competitive, data-driven insurance environment, the ability to turn
raw insurance data into actionable insights provides significant operational and strategic value.
KEYWORDS
Predictive
Risk Modeling, Property and Casualty Insurance, Guidewire DataHub, Machine Learning in Insurance,
Power BI Embedded Analytics
1.
INTRODUCTION
Risk evaluation is the backbone for all policy decisions, pricing strategies and operating mechanisms in the insurance
industry. Insurers within the Property and Casualty (P&C) segment grapple with a broad range of risk types, from
automobile collisions to property damage, natural disasters, and fraudulent claims. To effectively manage these
risks, one must understand how likely future events are and their potential impact. Historical data analysis,
underwriters’ professional judgment and actuarial models h
ave traditionally been the basis for assessing risk. While
these approaches remain useful, they are insufficient to address the complexity, scale, and velocity of modern
insurance data.
With readily available digital data and the attraction to cloud-based platforms, the entire insurance process has
AMERICAN ACADEMIC PUBLISHER
https://www.academicpublishers.org/journals/index.php/ijns
2
undergone a massive significant transformation. As insurers shift to data centric operations, attention is now
focused on moving from static reporting to dynamic, real-time analytics. It allows a proactive and more informed
decision process. However, data is meaningful only subsequently, and yet raw data alone is of little value; it is what
is made of it that counts. The capability to predict meaningfully has become a critical gap that predictive modelling
helps address. Predictive models learn from historical patterns to estimate the probability of future events
occurring, such as future policyholder claims, premium payment lapses, or potential fraud. Using these insights,
underwriting accuracy can be improved, pricing structures refined, claims triage can be enhanced, and operational
losses can be reduced. However, with the advent of modern technology solutions, it is now possible to integrate
predictive analytics directly into business processes.
One such platform is Guidewire DataHub, which is built specifically for the insurance industry. This unifies the
repository of data and brings it to a single place, capturing and organizing management, billing systems, and
customer touchpoints, reducing the need for researchers to gain access to clean and consolidated data necessary
for reliable analytics. When combined with other business intelligence tools, such as Microsoft Power BI, this data
serves as a basis for building interactive dashboards, visual risk monitoring, and enterprise reporting.
The construct of the present study aims to develop a predictive risk classification model based on structured data
from a simulated Guidewire DataHub environment. The idea behind the project is to categorize policyholders into
distinct risk categories (e.g., low, medium, high risk) by examining indicators related to their behavior and historical
data. The model is then trained using supervised machine learning algorithms aided by features extracted from
policy, claim and customer datasets. The model is then trained and validated, and the outputs are then integrated
into an embedded Power BI dashboard. Insurance professionals utilize this dashboard to visualize risk scores, claim
trends, and feature-level insights in real time, thereby eliminating the need for technical teams to be involved in
the process. To offer a scalable and practical solution for improving risk assessment in P&C insurance, this approach
combines advanced analytics, data warehousing, and interactive visualization. Greater accuracy and visibility in
classifying risk enhance operational efficiency and support strategic decision-making, all of which offer a
competitive advantage in a data-driven marketplace.
2. LITERATURE REVIEW
This section reviews current research and professional practices in predictive risk modelling for P&C insurance (
).
It covers developments in data platforms, analytics tools, and modelling techniques that enhance underwriting,
pricing, and fraud detection.
2.1 Insurance Predictive Risk Modelling
In modern insurance analytics, predictive modelling is a major player. Primarily, these models are used to estimate
claim likelihood, detect fraud, and provide a basis for pricing. Logistic regression, decision trees, and ensemble
models are widely used supervised machine learning algorithms for classifying risk using historical data. These
models can process more variables and identify more complex patterns more quickly than traditional actuarial
techniques while still acknowledging their limitations when compared with manual analysis. Risk assessments
increasingly utilize behavioral and transactional variables, such as previous claims, payment delays, and changes in
policy coverage. These variables provide insight into behavior and risk. The availability of larger and more detailed
datasets has enabled the achievement of better model performance and more precise segmentation.
As shown in the figure below, predictive analytics in insurance supports a wide range of objectives, including
improved risk assessment, fraud detection, cost reduction, enhanced customer experience, and regulatory
AMERICAN ACADEMIC PUBLISHER
https://www.academicpublishers.org/journals/index.php/ijns
3
compliance.
Figure 1: predictive-analytics-in-insurance-industry
2.2 Risk Modeling Driven by Data Platforms
Insurers can manage and structure information from multiple departments using modern data platforms. New
systems, such as Guidewire DataHub, provide a single location for consolidating policy, claims, billing, customer
service data, and more. This centralization improves consistency across applications and simplifies analytics by
eliminating data duplication. By creating a unified data environment, these platforms allow risk models to be built
using clean, reliable inputs. Moreover, structured and well-governed data makes it easier to comply with regulatory
standards and internal audit protocols. As noted by Goel, building resilient systems
—
whether in supply chain or
data management
—
depends on reducing fragmentation and enabling robust foundational infrastructures (
2.3 Embedded analytics tools
This provides non-technical users, such as underwriters and claims managers, with the ability to interact with model
results in real-time, using embedded analytics tools like Microsoft Power BI. Organizations can build dashboards
that depict essential insights, including claim trends, risk levels, and possible fraud indicators, using these tools.
Dashboards offer visual summaries that enable informed decisions and eliminate the need for static reports (
Embedded analytics combines real-time data feeds with user-friendly visuals to increase transparency and provide
alignment of decisions between departments. The added value lies in the ability to drill down into individual policy
details or gain insight into trends at a portfolio level.
2.4 Machine Learning Techniques for P&C Risk Assessment
In the insurance sector, a wide range of machine learning models is used. Ensemble methods, such as Random
Forest and XGBoost, are renowned for their high accuracy and their ability to work effectively with various types of
variables. Furthermore, they can determine which variables contribute the most to risk predictions (supporting the
explainability). Insurance has also been tested with advanced models, including neural networks, to determine
complex patterns such as fraud. These models, however, are less transparent and more difficult to explain to
AMERICAN ACADEMIC PUBLISHER
https://www.academicpublishers.org/journals/index.php/ijns
4
business users or regulators. Therefore, in many occasions, environments where transparency is essential, simpler
models with explainable logic are preferred. SHAP, an explainable AI tool, is used to explain complex models and
provide detailed feedback on individual predictions. These serve to maintain faith and ensure that, if necessary, an
export format for audits and reviews of model outputs remains possible.
As shown in the figure below, machine learning supports various underwriting tasks including creditworthiness
assessment, fraud detection, insurance risk assessment, pricing strategies, and portfolio management.
Figure 2: underwriting-machine-learning-models
2.5 Gaps in the Existing Literature
Predictive modelling is a proven area; however, some unexplored areas within this field also have significant gaps.
One notable gap is the lack of studies documenting how data platforms, modelling toolkits, and visualization
dashboards can be integrated cohesively into a unified system. The existing research is mainly focused on modelling
or data, but not on the combination of both. Ensuring consistency and real-time responsiveness is essential to
operationalizing analytics-driven systems, which parallels the need for integration in insurance predictive modelling
(
). Insurers also lack guidance on the use of predictive models in operational live environments, where they must
make their results accessible and understandable. This paper fills these gaps by presenting an end-to-end, practical
approach to risk modelling built on Guidewire DataHub and Power BI, aligning with the call for scalable,
performance-driven solutions in complex data infrastructures (
3. Theoretical Framework
3.1 Risk Modeling Principles in P&C Insurance
Property and Casualty (P&C) insurance risk modelling is used to estimate the likelihood and magnitude of future
losses that may occur due to a claim, policyholder behavior, or an external event (
). Vehicle collisions, property
damage, liability exposure and insurance fraud are common types of risk. These risks have traditionally been
handled by insurers using actuarial techniques. However, such methods require stable, linear relationships and are
mostly limited to low-dimensional, low-dimensional data. A predictive modelling approach provides a more
AMERICAN ACADEMIC PUBLISHER
https://www.academicpublishers.org/journals/index.php/ijns
5
adaptive solution, leveraging historical patterns, behavioral indicators, and real-time data inputs to forecast. These
models are key for accurate underwriting, customer segmentation and proactive claims management.
As illustrated in the figure below, the P&C insurance sector is rapidly evolving, driven by multiple industry trends.
These trends
—
including digital transformation, predictive analytics, and customer-centric innovations
—
are
reshaping traditional risk modeling strategies and highlighting the need for adaptable, data-driven approaches.
Figure 3: Property & Casualty Insurance Industry Trends
3.2 Predictive Analytics and CRISP-DM Process.
The Cross Industry Standard Process for Data Mining (CRISP-DM) provides a structured method for developing and
applying predictive models (
). The first step of this process involves understanding the business problem (in the
insurance case, this might mean improving the accuracy of underwriting or reducing the fraudulent claims rate).
Once the objective is defined, the next step is to understand and explore the dataset, then clean, transform and
prepare the data for modelling. Then, they select the algorithms needed to build the models and evaluate them
through metrics such as accuracy and error rate. The final stage is to provide a way for the models to be deployed
in a system that decision-makers can access and use. The CRISP-DM framework ensures that the modelling process
is aligned with business needs and that this process is repeatable within a disciplined workflow.
3.3 Feature engineering and dimensionality reduction.
Insurance data is often diverse and voluminous, consisting of structured information such as policy attributes,
customer demographics, vehicle specifications, and historical claim records. The choice of algorithm is not the sole
determinant of modelling quality; the quality of the input features also matters. Feature engineering involves
selecting, transforming, or creating variables that serve as proper signals for the prediction task. Encoding
categorical variables, normalizing numerical fields, and handling missing data may be involved in this process.
Dimensionality reduction techniques are employed to enhance model performance and interpretability further.
Using correlation-based feature selection, it selects and stores only variables that have a strong statistical
AMERICAN ACADEMIC PUBLISHER
https://www.academicpublishers.org/journals/index.php/ijns
6
relationship with the target variable, eliminating others that include noise or redundancy. Principal component
analysis is one ‘feature reduction’ algorithm that simplifies the dataset by transforming the original features into a
smaller set of derived components that capture the most variance in the data (
). Furthermore, these methods can
streamline the modelling process and reduce the risk of overfitting.
3.4 Risk Classification with Machine Learning
Several widely used supervised machine learning algorithms are employed in insurance risk classification. Logistic
regression is a linear, logically interpretable model for predicting an outcome as a function of multiple inputs.
Decision trees and random forests are flexible models that can convert numerical data with categorical data and
generate easily understandable decision rules. Gradient boosting and other methods using ensemble combine
several base models into a structured sequence, leading to higher prediction accuracy. Neural networks and deep
learning methods can model complex, non-linear relationships, but often in less transparent and more challenging
environments for regulatory applications. Both algorithms and their characteristics are a function of the data
(including its size and dimensionality), the desire for interpretability and the operational environment. Error
metrics, such as mean absolute error (MAE), root mean squared error (RMSE), and area under the receiver
operating characteristic curve (AUC), are used to assess models and ensure they are both accurate and reliable in
their predictions.
3.5 Business Intelligence Integration & Decision Support
Predictive risk modelling is the ultimate goal, helping to support decision-making within the insurance organization
(
38
). Model outputs must be presented in a clear and accessible format to be actionable. Interactive dashboards
created with Power BI Embedded are business intelligence tools that visually render model results, allowing users
to explore them. Directly connected to live data sources from Guidewire DataHub, these dashboards provide
current insights, including customer risk scores, claim frequency trends, and regional risk concentrations. These
insights can be used by users (such as underwriters, analysts, and claims managers) to select priority cases, adjust
prices or flag suspicious activity. Using a visual and data-driven approach speeds up and helps make more informed
and transparent decisions across sections. Analytics are integrated into daily operations, ensuring predictive models
deliver meaningful business value rather than remaining closed to technical teams, similar to how integrated
systems in healthcare and security pipelines enhance operational responsiveness (
4. System Architecture Overview
4.1 Overview of Architecture Design
Predictive risk modelling is designed to be supported by an architecture that enables seamless integration between
core insurance data systems, machine learning environments, and business intelligence tools. The five major
components include data extraction from Guidewire DataHub, data transformation and staging, centralized
storage, training machine learning and embedded analytics with Power BI. The modular design of the system
ensures that it can scale, be maintained, and be made accessible to data science and business user teams (
4.2 ETL Pipeline from Guidewire Data Hub
Data extraction begins with scheduled jobs or API-based connectors that extract structured data from Guidewire
DataHub. Policy records, claim transactions, billing activity, and customer profile information are all extracted
datasets. The transformation layer processes these datasets by imputing missing values, encoding categorical
AMERICAN ACADEMIC PUBLISHER
https://www.academicpublishers.org/journals/index.php/ijns
7
variables and deriving new ones. The transformed data are placed in a staging layer for use in validation and quality
control. Standardizing data in this step ensures that data aligns with enterprise data governance policies. The image
below illustrates a standard ETL (Extract, Transform, Load) architecture. Data from various sources
—
including
RDBMS, API endpoints, cloud services, and flat files
—
is first extracted and moved to a staging area.
The image below illustrates a standard ETL (Extract, Transform, and Load) architecture. Data from various sources
—
including RDBMS, API endpoints, cloud services, and flat files
—
is first extracted and moved to a staging area.
Figure 4: ETL data pipelines
4.3. Data Lake and Model Training Environment
Once pre-processed, data is loaded into a secure data lake environment. This is the central place where raw and
processed insurance data sit (
). It is designed to handle massive volumes of structured records and supports both
batch and incremental updates. Datasets are pulled from the data lake to the machine learning workspace, which
is populated with Python-based tools (Pandas, sci-kit-learn, XGBoost) specific to the case study at hand. Models are
trained for this environment using supervised learning algorithms ie. Random Forest, Logistic Regression.
4.4 Reporting Layer and Power BI Embedded Integration
Outputs of the model, such as risk scores, class labels, and feature importance’s, are pushed to a reporting database
or published as API endpoints once the model is trained. The outputs of these data pipelines are then consumed by
Power BI Embedded, which creates interactive dashboards for business users to view. Key risk indicators are
presented at the policy, customer, and portfolio levels through dashboards. Heat maps, claim trend lines,
classification summaries, and filterable reporting by geography, policy type, or time frame are visual elements.
Embedded within internal web-based applications or portals, these interfaces provide near real-time access to
insights for users, including risk analysts and underwriters, without requiring them to leave one platform (
4.5. Data Governance, Security and Compliance
Data governance measures are firmly integrated with the architecture. Access control is implemented via role-
AMERICAN ACADEMIC PUBLISHER
https://www.academicpublishers.org/journals/index.php/ijns
8
based permissions, thereby limiting a user's interaction to only what they should be interacting with based on their
role. Modelling and dashboard reporting mask personally identifiable information to protect our customers'
privacy. All activities related to data processing and model prediction are logged for traceability and auditability.
The architecture adheres to industry standards, such as GDPR and local insurance regulations, regarding data
handling, which means that predictive models can be deployed in an operationally and legally compliant manner.
5. Dataset Description
5.1 Overview of Data Source
The structured records used in this predictive risk modelling study consist of simulated Guidewire DataHub records
that conform to the structure of actual P&C insurance data systems (
). It contains anonymized and synthetic
records that represent the kind of data insurance companies collect during a policy lifecycle. The data spans multiple
years and includes records about customer profiles, policy issuance, endorsements, claims events, payments, and
renewals. This precisely defines the structure of the data so that it not only represents real-world insurance
operations but also provides sufficient details to enable meaningful risk classification and prediction, supporting
the need for fault-tolerant, context-aware data environments as emphasized in modern event-driven architectures
(
The visual below outlines the typical cycle followed in predictive analytics. It begins with data collection, followed
by model selection, training, deployment, and continuous monitoring. This process mirrors the workflow used in
this study to build, test, and integrate the insurance risk prediction model based on Guidewire DataHub data.
Figure 5: automation-and-ai-role-in-enhancing-predictive-analytics-for-marketers
5.2 The volume and structure of the data
It includes about 58,000 policy records over 120 attributes. The values are a mix of categorical, numerical, and
date/time (
). Customer age, policy type, coverage limits, vehicle details, number of claims filed, claim amounts,
geographic location, payment patterns, and policy tenure are some of the key fields. The dataset consists of
AMERICAN ACADEMIC PUBLISHER
https://www.academicpublishers.org/journals/index.php/ijns
9
transactional records, such as claims and billing events, which are linked together by a unique customer ID and
policy ID; each record in the dataset maps to an individual policyholder. The tabular format (CSV and Parquet) data
was easy to integrate with external modelling tools and visualization platforms as it was exported from a sandbox
version of Guidewire DataHub.
Various types of data attributes used in the analysis, including both numerical and categorical variables, are
summarized in the Table below.
Table 1:
Data Types and Examples
Data Attribute
Type
Example Value
Customer Age
Numerical
42
Policy Type
Categorical
Comprehensive
Vehicle Value
Numerical
25,000
Region
Categorical
Northeast
Number of Claims Filed
Numerical
2
Claim Amount
Numerical
6,300
Payment Method
Categorical
Auto-Debit
5.3 Data Quality and Data Preprocessing
The dataset was assessed for completeness, consistency, and accuracy before being analyzed. Much of the data
included in these fields (with optional fields such as secondary driver information or claim cause details) was either
missing or inconsistent. The multiple imputation technique, using chained equations, was employed to handle
missing data. This fills in missing values by modelling each field as a function of the other fields, making it more
robust to recovery than simple mean or median substitution (
). In fields like total claim amount and policy
premium, outliers were also identified. To mitigate the impact of extreme but rare cases, these values were capped
at the 95th percentile. To prepare categorical fields such as region, vehicle type, and policy channel for machine
learning algorithms, these were encoded via one-hot encoding. To avoid model training being skewed by attributes
on differing scales (such as age and claim amount), numerical variables were normalized. Derived features were
also created to augment the predictive power of the model. Some examples of these fields include claim frequency
per policy year, time since the last claim, payment delinquency score, and loss ratio (historical). Architecting these
engineered features is crucial in distinguishing risk profiles that would otherwise appear identical based on the raw
data.
The key variables incorporated into the predictive model, along with their respective types and importance levels,
are summarized in the Table below.
AMERICAN ACADEMIC PUBLISHER
https://www.academicpublishers.org/journals/index.php/ijns
10
Table 2: Key Variables Used in the Model
Variable Name
Type
Role in Model
Claim Frequency
Numerical High Importance
Loss Ratio
Numerical High Importance
Payment Delinquency
Score
Numerical
Medium
Importance
Time Since Last Claim
Numerical
Medium
Importance
Policy Tenure
Numerical
Medium
Importance
Vehicle Type
Categorical Low Importance
Geographic Region
Categorical Low Importance
5.4 Definition of the target variable
This dataset has a categorical label as its response variable, meaning they are the risk class of each policyholder.
The class is based on a composite of historical claims activity, policy behavior, and loss ratios. The categories into
which the variable is labelled are three: low risk, medium risk, and high risk. The internal underwriting rules,
commonly used in the P&C industry, were applied to the classification logic. For example, if there is a high frequency
of claims in a short duration or a high total loss ratio, the probability that a policyholder will be classified as high
risk would also increase. Supervised learning works from this labelled output. The model being learned is a trained
machine-learning algorithm that attempts to understand the relationship between input features and the assigned
risk class. To evaluate the performance during model evaluation, the accuracy of these predictions is compared
against their known risk classes in the validation dataset (
6. Research Methodology
This section outlines the steps taken to develop and evaluate a predictive risk model for P&C insurance, utilizing
data from Guidewire Data Hub. It involves data preparation, feature engineering, algorithm selection, model
training, and validation. Practical constraints found in the insurance industry, such as interpretability, data quality,
and business usability, serve as a guide for the methodology.
6.1 Research Design and Approach.
The study design employs a quantitative research approach grounded in empirical data and driven by a risk
classification methodology (
). On the historic policy and claims dataset, supervised machine learning is applied to
predict the likelihood of a policyholder being in a particular risk category. The objective is to develop the model
such that it generalizes well to unseen data and can be effectively utilized in a real-world setting (e.g., to support
underwriting decisions). This is a predictive approach, and unlike regression, the target variable is a set of discrete
AMERICAN ACADEMIC PUBLISHER
https://www.academicpublishers.org/journals/index.php/ijns
11
risk categories (levels), which means it is a classification rather than a regression. The methodology utilizes the
CRISP-DM process model, which helps maintain business alignment and technical rigor throughout the project
execution. It begins with business understanding and then proceeds through data preparation, modeling,
evaluation, and deployment, with each phase being highly iterative and incorporating refinements.
The overall data mining process followed in this project is structured according to the CRISP-DM methodology, with
its key phases and their applications summarized in the Table below.
Table 3: Simplified CRISP-DM Phases
Phase
Focus
Application
1. Business Understanding Define goals
Align model with underwriting and risk-based decisions
2. Data Understanding
Explore and assess data Review policy and claims data; check quality
3. Data Preparation
Clean and transform
Engineer features; normalize variables
4. Modeling
Build predictive model Use Random Forest, Decision Trees; tune parameters
5. Evaluation
Measure performance Use accuracy, recall, F1-score; validate with business goals
6. Deployment
Integrate and report
Deploy via Power BI dashboards for real-time risk scoring
6.2 Data Preprocessing and Feature Engineering
Raw data from Guidewire DataHub required certain preprocessing to ensure machine learning suitability. Outliers
were treated using percentile capping, and missing values were resolved through multiple imputations. To match
the one that requires numerical input, categorical variables are transformed into a numerical format using one-hot
encoding. New variables that provide a more profound understanding of customer behavior and claim patterns
were developed through feature engineering. The calculated fields included average claim size, time since the last
claim, and claim frequency per year, premium-to-loss ratio, payment consistency index, and other relevant metrics.
These engineered features allow the model to learn more about customer risk, which is deeper than the surface
level.
Two complementary techniques for dimensionality reduction were employed (
). The redundant and weakly
related attributes were removed using Correlation-Based Feature Selection (CFS), retaining the characteristics that
had a significant influence on the target variable. Additionally, Principal Component Analysis (PCA) was used to
create a compact representation of the data and assess its impact on model accuracy. Because it performed slightly
better in the test set and was more interpretable, CFS was ultimately chosen as the final model.
A summary of newly derived variables created during the feature engineering phase is provided in Table 4.
AMERICAN ACADEMIC PUBLISHER
https://www.academicpublishers.org/journals/index.php/ijns
12
Table 4: Feature Engineering Summary
New Feature Name
Description
Avg Claim Size
Mean of claim amounts per policy
Time Since Last Claim
Days since last claim event
Premium-to-Loss Ratio
Annual premium divided by total losses
Claim Frequency per Year Claims filed divided by policy years
Payment Consistency Index Score based on payment timeliness
6.3 Algorithm Selection
Several supervised learning algorithms were tested to evaluate their ability to predict policyholder risk class. The
algorithms used for training were Logistic Regression, Decision Tree, Random Forest, Gradient Boosting Machine
(XGBoost), and a simple Artificial Neural Network. These algorithms were chosen due to our experience in insurance
analytics and their future decision tree algorithms, which are available, for instance, in the Scikit-learn and XGBoost
open libraries. The prepared dataset was used to train each algorithm with the selected features. A grid search was
employed to optimize the models and determine the optimal hyper parameters (
). It entailed tweaking aspects
such as the number of trees in Random Forest, the learning rate for boosting models, and the depth of decision
trees.
The diagram below illustrates the hierarchical structure of a decision tree model, consisting of a root node, decision
nodes, and leaf nodes. This format helps in classifying policyholders by following decision paths based on their
attributes. Models like Random Forest and Gradient Boosting are built using ensembles of such trees.
Figure 6: Algorithms, Real-World Applications
AMERICAN ACADEMIC PUBLISHER
https://www.academicpublishers.org/journals/index.php/ijns
13
6.4 Training and Validation
An 80/20 ratio of training and validation sets were used to split the data. To avoid overfitting and obtain robust
results, 10-fold cross-validation was performed on each model during training. This method involves dividing the
dataset equally into 10 parts, training the model with nine parts, and testing the model with the remaining part.
This is repeated 10 times to obtain the average performance across another sample. Accuracy, precision, recall, F1-
score, and the area under the receiver operating characteristic curve (AUC) were all used as model evaluation
metrics. These metrics were selected to provide a scoring approach to tolerance, particularly in situations where
that was unevenly distributed across risk classes. Analyzing precision and recall for each class can help identify, for
example, a model that performs well only on low-risk cases but poorly on high-risk cases. The model that performed
the best was the Random Forest classifier due to its highest accuracy and consistency across all metrics; therefore,
it was selected for deployment (
). Logistic regression was highly interpretable but made slightly fewer predictions.
While the models implemented with Gradient Boosting performed competitively (as evaluated on out-of-sample
data), they required longer training times, which was not acceptable for real-time application scenarios.
6.5 Tools and Technologies Used.
Data was ingested, inspected, and transformed, for example, using Pandas and NumPy.
python
CopyEdit
import pandas as pd
import numpy as np
# Load data from CSV
df = pd.read_csv("insurance_claims.csv")
# Preview structure
print(df.info())
print(df.head())
Data Preprocessing
Pandas and NumPy were used to handle missing values, create new features, and convert categorical variables.
python
CopyEdit
# Fill in missing numerical data
df['Claim_Amount'].fillna(df['Claim_Amount'].median(), inplace=True)
# Encode categorical variables
AMERICAN ACADEMIC PUBLISHER
https://www.academicpublishers.org/journals/index.php/ijns
14
df = pd.get_dummies(df, columns=['Policy_Type', 'Region'], drop_first=True)
Standardization was performed using Scikit-learn:
python
CopyEdit
from sklearn.preprocessing import StandardScaler
num_features = ['Customer_Age', 'Vehicle_Value', 'Claim_Amount']
scaler = StandardScaler()
df[num_features] = scaler.fit_transform(df[num_features])
Exploratory Analysis
Matplotlib and Seaborn were used to visualize key relationships and support feature selection.
python
CopyEdit
import seaborn as sns
import matplotlib.pyplot as plt
# Correlation heatmap
plt.figure(figsize=(10, 6))
sns.heatmap(df.corr(), annot=True, cmap='coolwarm')
plt.title("Feature Correlation Matrix")
plt.show()
python
CopyEdit
Modeling with XGBoost
Model training and evaluation were performed using XGBoost and Scikit-learn:
python
CopyEdit
from xgboost import XGBClassifier
AMERICAN ACADEMIC PUBLISHER
https://www.academicpublishers.org/journals/index.php/ijns
15
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, classification_report, roc_auc_score
# Split dataset
X = df.drop(['High_Risk'], axis=1)
y = df['High_Risk']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Train model
model = XGBClassifier(use_label_encoder=False, eval_metric='logloss')
model.fit(X_train, y_train)
# Predict and evaluate
y_pred = model.predict(X_test)
print("Accuracy:", accuracy_score(y_test, y_pred))
print("AUC Score:", roc_auc_score(y_test, model.predict_proba(X_test)[:, 1]))
print(classification_report(y_test, y_pred))
Modeling with XGBoost
Model training and evaluation were performed using XGBoost and Scikit-learn:
python
CopyEdit
from xgboost import XGBClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, classification_report, roc_auc_score
# Split dataset
X = df.drop(['High_Risk'], axis=1)
y = df['High_Risk']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Train model
model = XGBClassifier(use_label_encoder=False, eval_metric='logloss')
AMERICAN ACADEMIC PUBLISHER
https://www.academicpublishers.org/journals/index.php/ijns
16
model.fit(X_train, y_train)
# Predict and evaluate
y_pred = model.predict(X_test)
print("Accuracy:", accuracy_score(y_test, y_pred))
print("AUC Score:", roc_auc_score(y_test, model.predict_proba(X_test)[:, 1]))
print(classification_report(y_test, y_pred))
Exporting Model Outputs
Final outputs, including risk scores and model predictions, were exported to Excel and SQL Server for use in Power
BI dashboards.
python
CopyEdit
# Add predictions to DataFrame
df_results = X_test.copy()
df_results['Predicted_Risk'] = y_pred
df_results.to_excel("model_results.xlsx", index=False)
SQL Server connection:
python
CopyEdit
import pyodbc
# Connect and upload results
conn = pyodbc.connect('DRIVER={SQL
Server};SERVER=your_server;DATABASE=your_db;Trusted_Connection=yes;')
cursor = conn.cursor()
for index, row in df_results.iterrows():
cursor.execute("""
INSERT INTO Risk_Predictions (Policy_ID, Predicted_Risk)
VALUES (?, ?)
""", row['Policy_ID'], row['Predicted_Risk'])
conn.commit()
AMERICAN ACADEMIC PUBLISHER
https://www.academicpublishers.org/journals/index.php/ijns
17
conn.close()
7. Experimental Results
The results from the predictive modeling experiments are presented here. The tasks it covers are model results
evaluation, comparison of variable algorithms, visualization of key insights, and reporting the results on the Power
BI platform.
7.1 Summary of model performance
The cleaned and feature-engineered dataset was used to train and test multiple machine-learning algorithms to
predict the risk class of policyholders. The overall performance was the highest for the RF classifier, with an average
accuracy of 84%. It also achieved strong performance in terms of class-specific precision and recall scores, especially
for high-risk cases, which are typically underrepresented in insurance datasets. Logistic Regression performed very
well in terms of transparency and ease of explaining the output, achieving an overall accuracy of 78% (
). However,
it was unable to thoroughly grasp the nonlinear interactions among the features. Random Forest showed slightly
better performance in some of the precision-recall tradeoff experiments with Gradient Boosting Machines
(especially with XGBoost). Still, it took longer to train and is less interpretable. The performance of the Decision
Tree model was moderate and was mainly suitable for visualizing rule paths. The basic neural network yielded good
results during training, but it over fitted under cross-validation, especially when the classes were imbalanced.
As illustrated in the bar graph below, the Random Forest model achieved the highest accuracy (84%) among all
evaluated algorithms, outperforming Logistic Regression, Decision Tree, XGBoost, and Neural Network models in
the predictive classification of policyholder risk.
Figure 7: Model Accuracy Comparison
AMERICAN ACADEMIC PUBLISHER
https://www.academicpublishers.org/journals/index.php/ijns
18
7.2 Comparison of Key Metrics
Standard classification metrics were used to evaluate these models. For the high-risk category, the Random Forest
model had 82% precision, 80% recall, and 81% F1 score. The results indicate that the model identifies most high-
risk policyholders and minimizes false positives. Overall, the Random Forest demonstrated a strong classification
capability, with an area under the receiver operating characteristic curve (AUC) of 0.91, indicating good
performance. For the medium-risk group, XGBoost had slightly higher precision (0.8 vs. 0.77), but this was
inconsistent across the folds. The AUC for the medium-risk group was 0.89. The AUC for Logistic Regression was
0.85, providing more stable predictions; however, it consistently misclassified high-risk students as medium risk.
The AUC of the Decision Tree was 0.82; it was shallow, thus easy to interpret, but was not suitable for scaling to the
complexity of the dataset. These comparisons verify that ensemble models achieve significantly better performance
on this structured insurance data than traditional models, such as LTDLs, for this type of data, especially when the
goal is to forecast risk distribution among multiple classes.
As shown in the Table below, Model Performance Comparison, the Random Forest model achieved the highest
overall performance, with an AUC of 0.91 and strong precision and recall scores for the high-risk class.
Table 5: Model Performance Comparison
Model
Accuracy (%) Precision (High-Risk) Recall (High-Risk) AUC
Logistic Regression 78
75
70
0.85
Decision Tree
74
72
68
0.82
Random Forest
84
82
80
0.91
XGBoost
83
80
78
0.89
Neural Network
79
77
74
0.86
7.3 Visualization of Results.
The exploratory visualizations and model outputs were plotted to see how different features influence the risk
classification (33). Feature importance plots from the Random Forest model revealed key variables, including but
not limited to claim frequency, premium-to-loss ratio, payment delays, and time since the last claim, which had the
highest predictive power. The pros had these features matched fundamental world underwriting criteria, so they
felt confident that these models were working. The predicted classes were compared to the actual courses to form
confusion matrices for each model, enabling an assessment of how well each model predicted the results. Random
Forest
—
the matrix showed a high correct classification rate at all three risk levels. The most misclassifications
occurred between medium and high-risk classes, which could be due to almost overlapping behavioral patterns in
these groups. In addition to static plots, an interactive risk dashboard was developed using Power BI. With this
dashboard, end-users can view real-time model outputs and drill down into policy-level predictions, as well as filter
results by region, vehicle type, policy term, and other attributes. Then, one of the dashboard's sections summarized
the model accuracy, and I explained the individual predictions using SHAP value approximations integrated through
the exported summary tables.
AMERICAN ACADEMIC PUBLISHER
https://www.academicpublishers.org/journals/index.php/ijns
19
As illustrated in the pie chart below, Claim Frequency emerged as the most significant predictor in the model,
contributing 35% to the classification outcome. This was followed by Premium-to-Loss Ratio (25%), while Payment
Delays and Time since Last Claim each contributed 20% to the model's decision-making process
Figure 8: Feature Importance from Random Forest Model
7.4 Integration into Business Reporting
Once the model evaluation and testing phases were complete, the results were embedded into Power BI for
operational use. Live scoring tables of policy-level risk scores were connected to the dashboard. Users (underwriters
and analysts) were able to access the dashboard through a secure web interface, view individual customer risk
profiles, and filter across key attributes, including coverage type, channel, and period. This generated a heat map
of risk pattern distribution across geographies, bar charts illustrating claim trends by risk class, and policy details
for flagged high-risk cases. Viewing these models allowed underwriting teams to fine-tune pricing or coverage limits
to their expectations of future risk. The predictive model was embedded in a predictable, reliable, and user-friendly
interface, removing it from the realm of a technical artifact and making it a usable business tool. The most important
part of this process was to gain adoption and ensure that the model provided value beyond just technical
experimentation.
8. DISCUSSION AND BUSINESS IMPLICATIONS
The results from experimental findings are then interpreted in a broader business context. It explains how the
predictive model aligns with the P&C insurance operational goals and examines the advantages, drawbacks, and
strategic opportunities of utilizing data-driven risk classification in real-world environments (
A detailed overview of these use cases, along with the benefits and scalability potential across the organization, is
AMERICAN ACADEMIC PUBLISHER
https://www.academicpublishers.org/journals/index.php/ijns
20
provided in the Table below: Strategic Impacts of Predictive Risk Classification Model.
Table 6: Strategic Impacts of Predictive Risk Classification Model
Business Area
Use Case
Benefits
Scalability Potential
Underwriting
Real-time risk scoring,
standardized decisions
Faster approvals, reduced
manual review, improved
consistency
High
—
applicable to all
policy types
Pricing
Premium adjustments based
on predicted risk
Better loss ratios, competitive
advantage, loyalty incentives
Moderate to High
—
customizable across
products
Fraud Detection
Early flagging of suspicious
behavior
Reduced fraudulent payouts,
improved financial security
High
—
extendable to
claims, billing, and customer
behavior
Business
Intelligence
Interactive dashboards with
historical and real-time views
Enhanced decision-making
speed, stakeholder
accessibility
High
—
via platforms like
Power BI or Superset
Marketing &
Product Design
Segmentation based on
behavioral and risk profiles
Personalized offerings,
targeted campaigns
Moderate
—
depends on
feature integration
Regulatory
Compliance
Explainable AI with traceable
feature contributions
Transparency, ethical AI
practices, improved
auditability
High
—
necessary across all
product and compliance
lines
Organizational
Learning
Feedback loop between data
science and operational
strategies
Continuous improvement,
data-driven culture
High
—
supports enterprise-
wide analytics strategy
8.1. Interpretation of Results
The study's results confirm that predictive modeling, achieved through machine learning, can provide scalable and
reliable solutions for classifying policyholders based on their risk. The Random Forest classifier was consistently the
best at identifying high-risk profiles, which are usually the most important for business outcomes. Based on this
performance, decision trees and ensemble methods are particularly well-suited for structured insurance datasets
that integrate elements of both numerical and categorical fields across the dimensions of policy, customer, and
claim. Claim frequency, time since the last claim, and loss ratio are found to be the most influential variables,
consistent with known indicators commonly used in traditional underwriting (
). They validate the model and, in
doing so, also provide new directions for data-driven workflows. Their use of engineered features, such as payment
behavior patterns and policy renewal intervals, introduced additional layers of insight that were not uncovered by
traditional approaches.
AMERICAN ACADEMIC PUBLISHER
https://www.academicpublishers.org/journals/index.php/ijns
21
As illustrated in the bar graph above, Claim Frequency, Time since Last Claim, and Loss Ratio were the top three
most influential variables in the model’s ability to classify policyholder risk.
Figure 9: Most Influential Variables in Risk Classification
8.2 Underwriting Operational Improvements
The predictive model can utilize the reduced manual review time by underwriters and provide real-time insights
during policy approval and renewal processes. Rather than individually approving each application without
established risk standards, underwriters can view a standardized risk score based on thousands of historical records
and interactive modeling. By enabling this type of capability, consistency and transparency can be introduced to
underwriting decisions, allowing human expertise to manage exceptions. This is further enhanced by the dashboard,
which integrates with Power BI to provide the output in an intuitively displayable format (
). Users can now quickly
access risk classifications, view feature contributions, and see how things have historically unfolded. It reduces
dependency on static reports and enhances the speed of decisions. In high-volume application environments, such
a tool enables underwriters to focus on borderline or high-risk cases, thereby optimizing effort while improving
overall portfolio quality.
8.3 Improving Pricing and Fraud Detection Strategies
The model also helps refine pricing strategies by accurately classifying customers into risk categories. Predicted risk
scores can be used to adjust premiums, thereby improving loss ratios and enhancing competitive positioning. For
instance, loyalty can be encouraged among low-risk customers through the offer of discounts. In contrast, high-risk
customers may require additional verification and rate adjustments to mitigate the risk associated with those losses.
The model also has applications in early fraud detection, underwriting, and pricing. Suppose cases are flagged as
AMERICAN ACADEMIC PUBLISHER
https://www.academicpublishers.org/journals/index.php/ijns
22
high risk based on historical behavior patterns, such as a high frequency of claims, inconsistent payment habits, or
rapid policy changes. In that case, they can be reopened for investigation. If this insight is incorporated into the
claims processing tools or alert systems, the insurer can prevent unreasonable payouts and maintain financial
health (
8.4 Strategic value and scalability.
The approach in this study can be scaled across insurance product lines (e.g., motor, home, and liability coverage).
The model can be applied to other domains within the organization's data with minimal changes needed. It provides
a basis for enterprise-wide Analytics strategies. Additionally, by harnessing the model's output into an embedded
analytics platform, the organization establishes a feedback loop between data science and business operations. The
dashboard can be used to drive product development, marketing campaigns, and agent training based on patterns
of risk identified therein. Over time, the model will retrain on more data as additional information is accumulated,
helping it adapt to changes in updates and dynamic behaviors. Explainable machine learning, in turn, enables
regulatory compliance by promoting transparency in autonomous decisions. Increasingly important in an
environment under scrutiny for data ethics and accountability, Business leaders can audit model outcomes and
trace individual classifications back to specific (contributing) features.
The illustration below highlights the interconnected roles of Artificial Intelligence, Machine Learning, and Finance.
It shows how various AI subfields
—
like fraud detection, explainable AI, predictive analytics, and optimization
algorithms
—
converge to support scalable, transparent, and enterprise-wide decision-making systems.
Figure 10: Model-agnostic explainable artificial intelligence methods in finance
9. Limitations and Future Work
9.1 Dataset Constraints
The key limitation of this study is that the dataset relies on training and validation of the models, which are not
AMERICAN ACADEMIC PUBLISHER
https://www.academicpublishers.org/journals/index.php/ijns
23
subject-based (
). Data pulled from the Guidewire DataHub sandbox environment is very close to actual world
P&C insurance data structures; however, it is still synthetic and anonymous. This leads to some real behavioral
features and irregularities
–
such as interactions between agents, complex fraud behavior, or region-specific
behavior
–
not being entirely captured. Since there are no variables such as agent notes, rich customer
communication logs, and telematics, the model cannot accurately represent the operational complexity
encountered in production systems.
9.2 Minority Representation and Class Imbalance
There were significantly fewer high-risk policyholders in the dataset compared to the total population, resulting in
an uneven distribution of risk classes. Despite employing balancing techniques such as oversampling and class
weighting, the model struggled to identify infrequent yet crucial events, including fraudulent claims and
catastrophic losses. Such an imbalance can reduce the model’s generalization capability and introduce bias into the
predictions. More advanced resampling methods, such as synthetic minority oversampling techniques (SMOTE),
can be further explored for improved sensitivity to underrepresented classes.
9.3 Explain ability and transparency gaps
Feature importance plots were used to explain some aspects. Still, more robust tools, such as SHAP (Shapley
Additive Explanations) or LIME (Local Interpretable Model-agnostic Explanations), were applied offline via exported
summary statistics. The tools were not embedded directly in the dashboard interface, which precludes end users
from seeing why risk scores were calculated in a particular way. In regulated industries where decisions must be
justified, such as eligibility and pricing, real-time explanations of predictions are a necessity. In future iterations,
explainable AI components will be integrated into the live dashboard to enhance transparency, compliance, and
trust (
9.4 Model Scalability and Maintenance
The data structures and business rules underlying insurance operations continue to change as operations change.
An effective predictive model will need to be maintained over time by making continuous updates to feature
engineering scripts, retraining workflows, and integrating pipelines. Performing these tasks manually can be
resource-intensive and prone to errors. For future development, automated pipelines for data validation, version-
controlled retraining, and scheduled updates to the dashboard interface should be built to ensure long-term model
performance and usability.
9.5 External Data Source Integration
The area for future enhancement also includes infusing external datasets. Beyond merely incorporating internal
insurance records, other data, such as weather forecasts, regional crime rates, credit history, and vehicle
maintenance records, may also contain signals for assessing risk. As these datasets can improve model accuracy
(particularly in areas like catastrophe modeling, localized risk pricing, and behavioral underwriting in delegation),
they are attractive to modelers.
9.6 Dynamic Risk Scoring in Real-Time
The current model calculates static risk scores from past data at a single point in time (
). Risk profiles, however,
are likely to evolve over the policy’s lifecycle as more claims are submitted, behaviors change, or policy conditions
are modified. Using streaming data and event-driven architecture, a dynamic modeling approach can continuously
AMERICAN ACADEMIC PUBLISHER
https://www.academicpublishers.org/journals/index.php/ijns
24
update risk scores in real-time real-time. A system of this kind would enable insurers to respond proactively to
changes in customer risk and make informed decisions about coverage adjustments and alerts promptly.
As shown in the figure below, real-time scoring depends on a layered infrastructure including data integration,
processing pipelines, monitoring tools, and alert systems. This architecture supports continuous updates to
policyholder risk profiles as new data is collected and processed.
9.7 Deployment Potential across Products
The framework developed in this study is architected and designed to support other insurance product lines, as well
as property and casualty (P&C) insurance. Future work would test and validate the model across various domains,
such as homeowners, cyber, or commercial auto insurance. With a shared Guidewire DataHub infrastructure and
Power BI Visualization layer, predictive models can be deployed across various lines of business to maximize return
on investment and standardize analytics practice enterprise-wide.
10. Recommendations
Based on the findings and limitations discussed in this study, several actionable recommendations are proposed to
enhance the implementation and operational impact of predictive risk modeling in the Property and Casualty (P&C)
insurance sector.
Insurers need to prioritize upgrading their data infrastructure. Accurate and scalable predictive modeling relies on
a unified and well-maintained data environment, such as Guidewire DataHub. A consistent, consistent and broader
view of the data is ensured by consolidating data across policy, claims, billing, and customer systems. Data
governance protocols must be established within an organization to maintain and enhance data quality and lineage
while also ensuring regulatory compliance. Second, insurers are instructed to integrate the predictive modeling
output into their operational workflows
). Business intelligence platforms, such as Power BI, enable the
embedding of model results, making them accessible to underwriters, analysts, and decision-makers. Real-time risk
scores, policy insights, and other explanatory variables should be presented in customized dashboards tailored to
user roles, allowing them to streamline and inform their decision-making process. Utilizing this integration reduces
AMERICAN ACADEMIC PUBLISHER
https://www.academicpublishers.org/journals/index.php/ijns
25
technical reliance and fosters a data-driven culture within departments (
Third, model explainability and user trust will be important. Understanding the reasons behind automated decisions
in regulated environments is essential and valuable. SHAP or LIME tools will be deployed in real-time to dashboards,
improving transparency and aiding in audits, customer communication, and internal reviews. These are the tools to
configure for highlighting key drivers of each prediction in an easy-to-understand way. Fourth, future
implementations should strive to integrate additional external data sources. The inclusion of third-party data (e.g.,
telematics, geographic risk index, economic indicators, and vehicle repair history) can expand the breadth and
accuracy of the predictions. These may be particularly beneficial for identifying emerging risks or for developing
highly localized pricing and claims strategies (
Fifth, insurers should transition from static risk scoring of customers to ongoing, event-driven risk scoring that
updates dynamically in real time. Throughout the real-time lifecycle, customer behavior, claim activity, or
environmental factors may change, leading to a corresponding adjustment in risk profiles. If scores are updated
continuously, one can take proactive actions, such as early intervention on high-risk accounts or a rapid response
to unusual fraudulent activity. The architecture and modeling framework developed in this study should be
expanded to other product lines. Standardizing modeling processes, data integration techniques, and dashboard
deployment across homeowners, commercial auto, and cyber insurance products will improve the return on
analytical investment and enhance operational efficiency, reducing development overhead and fast-tracking rollout
timing (
). The lifecycle of predictive modeling should closely emdiv continuous improvement. These encompass
setting up feedback loops to connect model performance with business outcomes, automating retraining cycles,
and correlating model metrics with operational key performance indicators (KPIs). Over time, a predictive system
remains relevant, reliable, and impactful only if its model accuracy, data drift, and user adoption are regularly
reviewed. Adopting these recommendations will enable insurers to enhance their risk assessment capabilities,
minimize wasted resources, and deliver greater value to their customers by making their insurance services more
responsive and intelligent for all stakeholders. Predictive analytics will increasingly become an embedded capability
for the types of decisions insurers make every day to contend in an increasingly competitive and data-driven
insurance space.
As the figure below illustrates, predictive analytics delivers multiple operational advantages in the insurance sector.
Figure 11: future-of-predictive-analytics-in-the-insurance-sector
AMERICAN ACADEMIC PUBLISHER
https://www.academicpublishers.org/journals/index.php/ijns
26
12. CONCLUSION
Modern data platforms and machine learning techniques offer significant benefits for predictive risk modeling in
the Property and Casualty (P&C) insurance sector. Insurers can classify policyholders by their risk levels using
Guidewire DataHub for centralized data management and Power BI Embedded for real-time visualization, enabling
them to underwrite and price more accurately and quickly detect fraud. The framework in this research
comprehensively covers data preparation, feature engineering, algorithmic modeling, and operation deployment.
Supervised machine learning techniques were employed to transform structured insurance data, which included
customer profiles, policy history, and claim behavior, into actionable insights. In the model development process,
priority was given not only to accuracy but also to business alignment and the interpretability of the resulting tools,
which underwriters, analysts, and managers would use.
Among the tested algorithms, the Random Forest classifier showed the best predictive performance, as measured
across most evaluation metrics. Its strength lay in its ability to find complex, non-linear relationships in the data
while still being interpretable through feature importance measures. Claim frequency, payment behavior, and
historical loss ratio were shown to be central predictors of risk classification. Thus, these findings corroborate the
value of behavioral and historical indicators in enhancing more comprehensive and responsive risk assessment
models. The practical dimension of the solution involved integrating model results into interactive Power BI
dashboards. Dynamic visualizations, such as individual and portfolio-level risk scores, claim trends, and policy
segmentation, allowed users to explore and interact. The level of interactivity created allowed teams to make
decisions more quickly, maintain consistency in underwriting evaluations, and provide more information to
business stakeholders. The support for explainability was an additional contribution of the approach. More
advanced interpretability tools, such as SHAP, were used separately during testing, but future improvements could
include embedding real-time explanation layers in dashboards for total transparency. In regulated environments,
which are increasingly expected to deliver accountability and justification for automated decisions, such
enhancements are significant.
During the process, several challenges arose. Anonymized and synthetic data was used, which was structurally
correct yet distant from the real behavioral nuances. With a small portion of high-risk cases, there is a class
imbalance, which could limit the model's ability to generalize in a production setting. Additionally, the model relied
on internal data sources, which limited its understanding of external factors, such as environmental threats or
socioeconomic influences. A valuable direction for further development is to address these limitations through
broader data integration and improved feature engineering. Beyond the technical proof of concept, what is
described as a means by which insurers can scale and operate a framework to modernize their core decision-making
processes. As data-driven strategies grow in popularity in the insurance sector, the integration of predictive
modeling, domain-specific data platforms, and embedded analytics is a timely and forward-thinking innovation.
Moving forward, for insurers to proactively manage risk, optimize pricing, and deliver superior customer outcomes,
they will need to leverage integrated solutions backed by intelligent and data-driven operations.
REFERENCES
1
Adolfo, C. M. S., Chizari, H., Win, T. Y., & Al-Majeed, S. (2021). Sample reduction for physiological data analysis
using principal component analysis in artificial neural network.
Applied Sciences
,
11
(17), 8240.
AMERICAN ACADEMIC PUBLISHER
https://www.academicpublishers.org/journals/index.php/ijns
27
2
Amadi, A. (2023). Integration in a mixed-method case study of construction phenomena: From data to
theory.
Engineering,
Construction
and
Architectural
Management
,
30
(1),
210-237.
https://www.emerald.com/insight/content/doi/10.1108/ecam-02-2021-0111/full/html
3
Belete, D. M., & Huchaiah, M. D. (2022). Grid search in hyperparameter optimization of machine learning
models for prediction of HIV/AIDS test results.
International Journal of Computers and Applications
,
44
(9),
875-886.
https://doi.org/10.1080/1206212X.2021.1974663
4
Blier-Wong, C., Cossette, H., Lamontagne, L., & Marceau, E. (2020). Machine learning in P&C insurance: A
review for pricing and reserving.
Risks
,
9
https://doi.org/10.3390/risks9010004
5
Chavan, A. (2022). Importance of identifying and establishing context boundaries while migrating from
monolith to microservices. Journal of Engineering and Applied Sciences Technology, 4, E168.
http://doi.org/10.47363/JEAST/2022(4)E168
6
Chavan, A. (2024). Fault-tolerant event-driven systems: Techniques and best practices. Journal of Engineering
and Applied Sciences Technology, 6, E167.
http://doi.org/10.47363/JEAST/2024(6)E167
7
Chen, D. L., & Loecher, M. (2019). Mood and the malleability of moral reasoning.
Available at SSRN 2740485
.
https://dx.doi.org/10.2139/ssrn.2740485
8
Curia, F. (2023). Explainable and transparency machine learning approach to predict diabetes develop.
Health
and Technology
,
13
(5), 769-780.
https://link.springer.com/article/10.1007/s12553-023-00781-z
9
Deichmann, U., Goyal, A., & Mishra, D. (2016). Will digital technologies transform agriculture in developing
countries?.
Agricultural Economics
,
47
(S1), 21-33.
https://doi.org/10.1111/agec.12300
10
Dhanagari, M. R. (2024). MongoDB and data consistency: Bridging the gap between performance and
reliability.
Journal
of
Computer
Science
and
Technology
Studies,
6
(2),
183-198.
https://doi.org/10.32996/jcsts.2024.6.2.21
11
Dhanagari, M. R. (2024). Scaling with MongoDB: Solutions for handling big data in real-time.
Journal of
Computer Science and Technology Studies, 6
(5), 246-264.
https://doi.org/10.32996/jcsts.2024.6.5.20
12
Franklin, A., Gantela, S., Shifarraw, S., Johnson, T. R., Robinson, D. J., King, B. R., ... & Okafor, N. G. (2017).
Dashboard visualizations: Supporting real-time throughput decision-making.
Journal of biomedical
informatics
,
71
, 211-221.
https://doi.org/10.1016/j.jbi.2017.05.024
13
Gholamiangonabadi, D., Kiselov, N., & Grolinger, K. (2020). Deep neural networks for human activity
recognition with wearable sensors: Leave-one-subject-out cross-validation for model selection.
Ieee
Access
,
8
, 133982-133994.
https://doi.org/10.1109/ACCESS.2020.3010715
14
Goel, G., & Bhramhabhatt, R. (2024). Dual sourcing strategies.
International Journal of Science and Research
Archive
, 13(2), 2155.
https://doi.org/10.30574/ijsra.2024.13.2.2155
15
Gonçalves, C. T., Gonçalves, M. J. A., & Campante, M. I. (2023). Developing Integrated Performance
Dashboards
Visualisations
Using
Power
BI
as
a
Platform.
Information
,
14
(11),
614.
https://doi.org/10.3390/info14110614
16
Hasan, M. A. M., Nasser, M., Ahmad, S., & Molla, K. I. (2016). Feature selection for intrusion detection using
random forest.
Journal of information security
,
7
(3), 129-140.
AMERICAN ACADEMIC PUBLISHER
https://www.academicpublishers.org/journals/index.php/ijns
28
17
Helmus, L. M., & Babchishin, K. M. (2017). Primer on risk assessment and the statistics used to evaluate its
accuracy.
Criminal Justice and Behavior
,
44
(1), 8-25.
https://doi.org/10.1177/0093854816678898
18
Howe, J. (2020). Predicting the Unexpected: Applying Advanced Underwriting to Accurately Predict Early
Duration Claims in Life Insurance.
https://digitalcommons.lib.uconn.edu/srhonors_theses/674/
19
Joginipalli, S. K. (2024). Impact of technology on the property and casualty (P&C) insurance
industry.
International Research Journal of Modernization in Engineering Technology and Science
,
11
(06).
https://www.doi.org/10.56726/IRJMETS64426
20
Kanchetti, D. (2021). Optimization of insurance claims management processes through the integration of
predictive modeling and robotic process automation.
International Journal of Computer Applications
(IJCA)
,
2
(2), 1-18.
https://iaeme.com/Home/issue/IJCA?Volume=2&Issue=2
21
Karwa, K. (2023). AI-powered career coaching: Evaluating feedback tools for design students. Indian Journal
of Economics & Business.
https://www.ashwinanokha.com/ijeb-v22-4-2023.php
22
Karwa, K. (2024). Navigating the job market: Tailored career advice for design students.
International Journal
of Emerging Business
,
23
(2).
https://www.ashwinanokha.com/ijeb-v23-2-2024.php
23
Konneru, N. M. K. (2021). Integrating security into CI/CD pipelines: A DevSecOps approach with SAST, DAST,
and SCA tools.
International Journal of Science and Research Archive
. Retrieved from
https://ijsra.net/content/role-notification-scheduling-improving-patient
24
Krause, J., Perer, A., & Ng, K. (2016, May). Interacting with predictions: Visual inspection of black-box machine
learning models. In
Proceedings of the 2016 CHI conference on human factors in computing systems
(pp.
https://dl.acm.org/doi/abs/10.1145/2858036.2858529
25
Kumar, A. (2019). The convergence of predictive analytics in driving business intelligence and enhancing
DevOps efficiency. International Journal of Computational Engineering and Management, 6(6), 118-142.
Retrieved from
https://ijcem.in/wp-content/uploads/THE-CONVERGENCE-OF-PREDICTIVE-ANALYTICS-IN-
DRIVING-BUSINESS-INTELLIGENCE-AND-ENHANCING-DEVOPS-EFFICIENCY.pdf
26
Majka, M. (2024).
The Crucial Role of Insurance in Risk Mitigation Strategies
.
27
Nodeh, M. J., Calp, M. H., & Şahin, İ. (2019, April). Analyzing and processing of supplier database based on
the cross-industry standard process for data mining (CRISP-DM) algorithm. In
The international conference
on artificial intelligence and applied mathematics in engineering
(pp. 544-558). Cham: Springer International
Publishing.
https://doi.org/10.1007/978-3-030-36178-5_39
28
Nyati, S. (2018). Transforming telematics in fleet management: Innovations in asset tracking, efficiency, and
communication. International Journal of Science and Research (IJSR), 7(10), 1804-1810. Retrieved from
https://www.ijsr.net/getabstract.php?paperid=SR24203184230
29
Patil, D. (2024). Explainable Artificial Intelligence (XAI) For Industry Applications: Enhancing Transparency,
Trust, And Informed Decision-Making In Business Operation.
Trust, And Informed Decision-Making In
Business Operation (December 03, 2024)
https://ssrn.com/abstract=5057402
30
Paul, C., Gauthier, E., Aurégann, L., & P&C, A. G. (2017). P&C Reinsurance modeling.
brest.fr/euria/sites/euria.www.univ-brest.fr/files/2022-06/reinsurance_modeling2017-2018.pdf
31
Peter, H. (2023). AI and Cloud for Claims Processing Automation in Property and Casualty Insurance.
AMERICAN ACADEMIC PUBLISHER
https://www.academicpublishers.org/journals/index.php/ijns
29
32
Raju, R. K. (2017). Dynamic memory inference network for natural language inference. International Journal
of Science and Research (IJSR), 6(2).
https://www.ijsr.net/archive/v6i2/SR24926091431.pdf
33
Reddy, G. T., Reddy, M. P. K., Lakshmanna, K., Kaluri, R., Rajput, D. S., Srivastava, G., & Baker, T. (2020).
Analysis of dimensionality reduction techniques on big data.
Ieee Access
,
8
, 54776-54788.
https://doi.org/10.1109/ACCESS.2020.2980942
34
Sardana, J. (2022). The role of notification scheduling in improving patient outcomes.
International Journal
of Science and Research Archive
. Retrieved from
https://ijsra.net/content/role-notification-scheduling-
35
Schmidt, S. (2021).
Concepts towards an automated data pre-processing and preparation within data
lakes
(Master's
thesis).
https://web.archive.org/web/20220422020239id_/https://elib.uni-
stuttgart.de/bitstream/11682/11984/1/Ausarbeitung_Simone_Schmidt.pdf
36
Singh, V. (2022). Integrating large language models with computer vision for enhanced image captioning:
Combining LLMS with visual data to generate more accurate and context-rich image descriptions. Journal of
Artificial Intelligence and Computer Vision, 1(E227).
http://doi.org/10.47363/JAICC/2022(1)E227
37
Singh, V. (2024). Ethical considerations in deploying AI systems in public domains: Addressing the ethical
challenges of using AI in areas like surveillance and healthcare.
Turkish Journal of Computer and Mathematics
Education (TURCOMAT)
.
