Decision tree algorithms continue to be one of the most reliable methods for converting unprocessed data into useful insights as artificial intelligence transforms various industries. With its quickly expanding tech sector, India is home to a number of businesses that are highly skilled at developing and implementing decision tree-based solutions in a variety of sectors, including banking, healthcare, retail, telecommunications, and agriculture. In order to provide highly accurate, scalable AI solutions, these companies use decision trees not only for classification and regression tasks but also incorporate them into sophisticated ensemble techniques like Random Forests and Gradient Boosted Trees. This article will examine the top 10 companies that are at the forefront of machine learning innovation powered by decision trees.
- TCS
TCS uses decision tree models in Ignio in IT automation, including anomaly detection and predictive analytics. Its solutions span banking, manufacturing, and retail, assisting organizations in making reliable scalable advanced data-backed decisions.
- Infosys
With its proprietary Nia platform, Infosys is able to use decision tree algorithms for customer analytics, supply chain optimization, and fraud detection. This company is also known for combining decision trees with deep learning to improve both the interpretability and accuracy of the system.
- Entropik Tech
Entropik uses decision tree algorithms in emotion AI to classify user responses and predict behaviour. Their platforms combine decision trees with computer vision and EEG data to help brands decode consumer sentiment and improve engagement strategies.
- Wipro
With the help of Wipro’s HOLMES AI and Automation platform, decision tree models can be used for cognitive automation, IT service management, and predictive maintenance. Wipro also combines decision trees with reinforcement learning and NLP to provide smart solutions in the healthcare, energy, and telecommunications industries.
- Artivatic.ai
Artivatic.ai uses decision trees for its underwriting, fraud detection, and claims automation in insurance technology. Using them along with neural networks, Artivatic.ai’s platform provides explainable AI in health and life insurance, where decision trees are commonly used.
- Fractal Analytics
Fractal uses algorithms based on decision trees in its Qure.ai and Cuddle.ai platforms, which specialize in healthcare diagnostics and business intelligence. By integrating decision trees with deep learning, they strive to elevate the interpretability and accuracy of their solutions in critical settings.
- HCLTech
HCLTech’s DRYiCE suite uses decision tree algorithms to improve business functions, pinpoint anomalies, and improve workflows. Their models are applied and further developed with other methods in financial services, the automotive industry, and life sciences to improve functionality and scalability.
- Zensar Technologies
Zensar uses decision tree algorithms in the reshaping of customer experiences, predictive analytics, and in the digital supply chain. Their AI-powered platforms deliver retail and logistics business intelligence and leverage decision trees to provide real-time analytics for better business decision-making.
- Mu Sigma
Mu Sigma exploits decision tree techniques in their decision sciences, facilitating risk, churn, and operational optimization analytics for Fortune 500 firms. The company’s unique frameworks integrate decision trees with Bayesian methods, yielding more reliable analyses.
- Tredence
Tredence creates AI-driven models for retailers by integrating decision trees with demand prediction, inventory management, and customer segmentation. The models function on analytics platforms and can scale on the cloud.
Conclusion:
The use of machine learning models based on decision trees has become pivotal in India’s evolving AI landscape. Numerous organizations, ranging from major IT corporations like TCS, Infosys, and Wipro to niche analytical businesses like Fractal Analytics, Mu Sigma are showcasing the capabilities of decision trees particularly in conjunction with ensemble methods like Random Forests and Gradient Boosted Trees in offering actionable, explainable, and scalable industry solutions.
