Our ExpertiseFraud Analytics Model
Intelligence That Transforms Insurance
Swadata Analytics is a data science firm focused on insurance and actuarial innovation. We develop predictive models for claims, pricing, and fraud analytics using machine learning, AI, and remote sensing to deliver smarter, data-driven insurance solutions.
Design with a Global
Everything you need
Intelligence that scales with your insurance data.
Swadata Analytics delivers modular data science capabilities built for insurers — from model deployment to AI governance — so you can predict risks, prevent fraud, and optimize decisions at every stage.
- Automated Model Deployment
- Deploy actuarial and predictive models seamlessly with cloud-ready pipelines and version control.
- Secure Data Infrastructure
- Enterprise-grade encryption and governance ensure complete protection of data.
- Predictive Claims Engine
- Machine learning models detect anomalies, reduce leakage, and accelerate claims processing at scale.
- Advanced Fraud Analytics
- AI-driven fraud detection using behavioral risk mapping and real-time anomaly scoring.
- API-Driven Integration
- Connect analytics directly to core insurance systems through a unified, API-driven model.
- Continuous Model Monitoring
- Automated retraining, drift detection, and performance tracking keep models accurate over time.
Deploy faster
Everything you need to deploy your app
Cloud Ready Infrastructure
Deploy predictive and actuarial models effortlessly with scalable, cloud-based pipelines that accelerate time to production.
Performance
Enhance model speed and reliability with continuous training, real-time validation, and automated optimization workflows.
Security
Protect sensitive claims and policy data with advanced encryption, user governance, and regulatory compliance frameworks.
Powerful APIs
Connect analytics directly to your core insurance systems through a unified, API-driven model for seamless data exchange.
Model.py
App.py
import pandas as pd
from sqlalchemy import create_engine
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report
# Connect to Motor Insurance database
engine = create_engine('postgresql://user:password@localhost:5432/insurance_db')
# Load claims data from SQL table
query = """SELECT claim_id, claim_amount, driver_age, vehicle_age, num_prior_claims, policy_tenure, is_fraud FROM motor_claims WHERE claim_date >= '2023-01-01';"""
df = pd.read_sql(query, engine)
# Prepare features and target
features = ['claim_amount', 'driver_age', 'vehicle_age', 'num_prior_claims', 'policy_tenure']
X = df[features]
y = df['is_fraud']
# Split, train, and evaluate model
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = RandomForestClassifier(n_estimators=250, max_depth=10, random_state=42)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
print(classification_report(y_test, y_pred))Data-Driven Actuarial Intelligence
Empower insurance analytics with advanced stochastic, Markov, and simulation-based actuarial models built for precision and scale.
Actuarial models that evolve with your data
Build and deploy stochastic and probabilistic models to evaluate risk behavior over time. Simulate portfolio outcomes, transition probabilities, and claim frequencies with data-driven precision powered by AI and statistical inference.
Stochastic Simulations
- Use Monte Carlo and bootstrapped simulations to quantify uncertainty and explore risk distributions across portfolios.
Markov Chain Models
- Model policyholder transitions, claim development, and state-based probabilities using discrete and continuous-time Markov methods.
Predictive Analytics
- Combine GLMs, gradient boosting, and Bayesian inference to forecast losses, detect anomalies, and support underwriting precision.
Real-time insights, explainable results
Ensure every model remains transparent and interpretable. Access automated validation, continuous monitoring, and explainable analytics that enhance trust in predictive actuarial outcomes.
Model Monitoring
- Track accuracy drift, calibration stability, and parameter evolution across stochastic or machine learning models in production.
Explainable Analytics
- Visualize key drivers, probability weights, and variable sensitivities to make complex statistical models transparent and auditable.
A better way to build with Big Data
We handle petabytes of data for actuarial modelling, remote sensing, satellite and climate modelling.
- Be world-class.
- Scalable distributed pipelines (Spark, Flink, Ray) with optimized IO, fault tolerance, and low-latency processing.
- Take responsibility.
- Strong governance with schema enforcement, lineage, encryption, access control, and full auditability.
- Be supportive.
- Seamless integration across cloud storage, actuarial engines, geospatial tooling, and Kubernetes-orchestrated ML workflows.
- Always learning.
- Model drift detection, statistical QA, uncertainty checks, and adaptive ML pipelines from new satellite or exposure data.
- Share everything you know.
- Versioned datasets, reproducible notebooks, rich metadata catalogs, and real-time dashboards.
- Ensure high availability.
- Autoscaling, self-healing compute, continuous backups, and zero-downtime deployments.
Build with confidence
Powering actuarial, geospatial, and ML workflows with reliable, cloud-native data infrastructure.
- Scalable pipelines
- Cloud-native compute
- Geospatial ingestion
- Actuarial modeling
- ML automation
- Real-time insights
Frequently asked questions
Reach Us
We’re here to help with actuarial, machine learning, and data engineering solutions. Get in touch for collaborations, inquiries, or support.
Ahmedabad
Hasubhai Chambers, Sheth Mangaldas Road
Ahmedabad, Gujarat 380 006