Advanced Analytics Services
At SGA, we are committed to providing advanced analytics solutions where we take our analytical collaboration to the next level.
Advanced Analytics Services
Predictive Analytics
- Classification and regression models using explainable and implementable advanced ML models like XgBoost, LightGBM, and decision trees.
- NLP tasks such as text classification (multi-label), translation, and topic modeling.
- Training state-of-the-art models (BERT-based models, such as Distil Bert and Roberta, and GPT-based models like Latent Dirichlet Allocation) on cloud/on-premises environments, utilizing libraries such as NLTK, Gensim, Spacy, and TensorFlow.
- Recommendation systems such as content-based filtering, collaborative filtering, and hybrid algorithms.
- Time series analysis and forecasting using ARIMA, LSTM, TFT, DeepAR, and other suitable techniques.
- Making use of Churn Attrition models to identify the risk of attrition accurately based on past data and profiling them into micro-segments to run promotional campaigns accurately to improve customer retention.
Applied Data Science
- Model deployment on edge devices/cloud/on-premises servers, involving environment setup, containerization, latency testing, multiprocessing, and model optimization.
- Model lifecycle management involving experiments tracking, monitoring (KPI drifts), and managing API endpoints on cloud/on-premises environments using MLOps tools (MLFlow, TensorFlow serve, and Kubernetes).
- Performing clustering analysis using density-based clustering and hierarchical clustering, with appropriate distance measures.
- Network analysis with Markov chains and BFS/A* search techniques.
- Market survey designing using fractional factorial design and analyzing results of choice-based conjoint/max different surveys using hierarchical Bayesian models to determine individual and group utilities of the options.
Computer Vision
- Computer vision tasks, including image classification, object detection, and object tracking.
- Training state-of-the-art models (YOLOv5, Resnet50, VGG-16, and SORT) utilizing OpenCV, PyTorch, Keras, and TensorFlow.
Risk Analytics
- We develop credit lifecycle models (application behavior and collection) using explainable and robust ML algorithms like XgBoost and LightGBM.
- We design intelligent features using Bureau and other alternative data sources. We bring decades of credit risk management expertise across product lifecycles and geographies.
- We help reduce the model development and deployment lifecycle to 8–12 weeks.
