Fraud & Risk Analytics
Mitigate frauds at transactions level, merchant level or account level by anomaly detection.

With the technological disruptions in both banking and payments sector, fraudulent activities has been an ever-growing issue with huge consequences to banks and customers alike, both in terms of financial losses, trust, and credibility.
TransOrg helps in mitigating risks and frauds by identifying anomalies at transactions, accounts and merchant level using various complex modelling algorithms.
We help our clients make strategic decisions resulting in sizable impact
Exploratory Data Analysis
Detect and analyse anomalous patterns.
Classify problem into supervised/unsupervised learning.
Feature Engineering and Model Building
Create new variables for precise insights.
Create and compare multiple machine / deep learning models.
Model Integration and Feedback
Near real time model integration in big data infrastructure.
Automated improvements in output based on self – learning models
Monitor and Monetize
Monitor model performance.
Track anomalies and quantify mitigated risks.
Want to learn more about TransOrg’s value proposition solution methodology and implementation approach?
Unique Range of Benefits
-
1
Reduced losses due to defaults
-
2
Improved Asset Portfolio Quality
-
3
Thorough assesment of customers
-
4
Improved customer experience
-
5
Reduced frauds and delinquencies
Success Stories

Revolutionizing Logistics Operations with Agentic AI
In the fast-paced world of logistics, efficiency, precision, and data-driven decision-making are essential for staying ahead of the competition. The traditional logistics landscape, often burdened

Demand Forecasting with TransOrg: Buy Versus Build-The TransOrg Advantage
Enhancing Demand Forecasting with TransOrg: Buy Versus Build-The TransOrg Advantage Why Demand Planning Matters Demand planning is essential in businesses ranging from retail to

Why Agentic AI is the Future of Personalized Shopping Experiences
Introduction: The Dawn of a New Shopping Era Imagine entering a store where the assistant instantly understands your preferences, past purchases, and style choices. No