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.
Why SGA
Who We Work With
Our Ins(AI)ghts
Whitepaper
Agentic AI and the Future of Fintech and Banking
AI is rapidly evolving beyond chatbots and copilots into autonomous digital agents capable of proactive decision-making and task execution without explicit human instructions. Agentic AI marks the third wave of AI progression, following Gen AI. Acting as autonomous agents, these advanced AI models will significantly impact business operations, automation
BLOG
How AI-Driven Chatbots Are Redefining Customer Experience in Retail Banking
AI-driven chatbots are transforming customer experience in retail banking by offering 24/7 instant support, personalized interactions, and seamless service delivery. These bots leverage NLP and machine learning to resolve queries, manage accounts, and recommend products in real-time. They also help banks overcome challenges like unstructured data, limited human support, and lack of personalization. With use cases ranging from automated customer service and lead generation to fraud detection and alerts, chatbots are becoming essential to digital banking strategies.
