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Advanced Analytics Services

At SGA, we place a strong emphasis on offering sophisticated analytics solutions that strengthen our analytical collaboration.

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With our advanced analytics services, we combine data assets and ML models to design intelligent AI applications for the future

The analytics team at SGA assists in enhancing the data operations team’s skills with data science and ML experts who are well versed in big data and ML tools and frameworks, such as Python, R, TensorFlow, Keras, Pytorch, Databricks, Spark, Azure Machine Learning and Amazon Sagemaker. Using our advanced analytics services, we build OCR- and NLP-based machine learning pipelines to assist in extracting valuable insights from unstructured data across core data sources, including financial data, reports, earnings summaries and social media platforms. 

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Advanced Analytics services

What We Offer

  • The 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 modelling.
  • Training state-of-the-art models (BERT-based models like Distil Bert and Roberta and GPT-based models like Latent Dirichlet Allocation) on cloud/on-premises environments, utilising libraries, such as NLTK, Gensim, Spacy and TensorFlow.
  • Recommendation systems involving 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 recognise the risk of attrition accurately based on past data and profiling them into micro-segments to run promotional campaigns accurately to improve customer retention.
  • Computer vision tasks such as image classification, object detection and object tracking.
  • Training state-of-the-art models (YOLOv5, resnet50, VGG-16 and SORT) by utilising OpenCV, PyTorch, Keras and TensorFlow.
  • Model deployment on edge devices/cloud/on-premises servers, which incorporates environment setup, containerisation, latency testing, multiprocessing and model optimisation.
  • Model lifecycle management involves 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 by employing 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 analysing results of choice-based conjoint/max different surveys using hierarchical Bayesian models to determine individual and group utilities of the options.
  • We develop credit lifecycle models (application behaviour and collection) by employing explainable and robust ML algorithms such as XgBoost and LightGBM.
  • We create intelligent features using Bureau and other alternative data sources. We are backed by our decades of credit risk management expertise across product lifecycles and geographies.
  • We help reduce model development and deployment lifecycle to 8–12 weeks.

Why SGA

Capitalising on our Expertise

SGA’s predictive analytics solutions enable clients to proactively make the right decisions and enhance their profitability and market shares.

Driving Business Objectives

Leveraging data science solutions assist in enhancing our customer experience and delivering the best business outcomes.

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Insights

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CASE STUDIES

Simplifying Sales Cycles: SG Analytics' Comprehensive Solution to Streamline Deal Structuring and Automated Sales Contract Creation for Thomson Reuters

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