Project
Project Page
Finance: Credit Assessment and Risk Management
Credit assessment and risk management are fundamental processes in finance aimed at evaluating a borrower's ability to repay debt and minimizing potential financial losses. These processes involve comprehensive analysis of a borrower's creditworthiness, continuous performance monitoring, and strategic management of credit risk through various methodologies. Credit Assessment entails the thorough evaluation of a borrower's financial history, credit scores, income levels, and overall capacity to meet repayment obligations. This assessment is critical for determining the borrower’s reliability and suitability for receiving credit. Risk Management focuses on developing and applying models and techniques that assess and mitigate credit risk. This includes setting appropriate loan terms, interest rates, and other conditions based on the assessed risk level. Risk management strategies also involve understanding the factors contributing to loan defaults and market fluctuations, with the aim of enhancing credit scoring models and improving risk mitigation practices. Together, these processes ensure sound lending decisions and effective management of financial risk.
Dr. S. N. Tripathy
Marketing: Promotion Lift Analysis
Promotion Lift Analysis is a marketing technique used to assess the incremental impact of a marketing campaign or promotional activity on key metrics such as sales, conversions, or brand awareness. This approach compares the performance of a control group (which is not exposed to the campaign) with a treatment group (which is exposed to the campaign) to measure the actual lift, or increase, in the target metric. The goal is to accurately estimate the causal impact of advertising campaigns and promotions on sales, distinguishing genuine lift from trends that may be correlated but not directly attributable to the campaign.
Dr. S. N. Tripathy
Manufacturing: Fault Detection and Root Cause Analysis
Effective fault detection and root cause analysis are essential for ensuring quality, optimizing efficiency, and minimizing costly downtime in manufacturing operations. Root cause analysis plays a pivotal role in identifying the underlying factors contributing to defects or failures, enabling the implementation of targeted solutions that not only resolve the issue but also prevent its recurrence, thereby enhancing overall production performance. Utilizing fault tree analysis allows for the precise identification of component failures that drive system breakdowns, ultimately reducing both downtime and maintenance costs. Here is a generalized, step-by-step procedure for implementing manufacturing fault detection and root-cause analysis using Explainable AI (XAI) with counterfactual explanations in a Federated Learning (FL) framework, underpinned by causal Directed Acyclic Graphs (DAGs): By first constructing a causal DAG to represent the interdependencies among process variables, one can leverage a federated learning setup to train local models on decentralized data while preserving privacy. These models are then aggregated into a global model whose predictions are explained via counterfactual reasoning. Counterfactual explanations pinpoint minimal changes in inputs that would alter a fault prediction, and tracing these changes back through the causal DAG reveals underlying root causes. Iterating this loop refines both the model and the causal graph, driving continuous improvement in fault detection accuracy and prevention.
Dr. S. N. Tripathy
Cybersecurity: Breach Attribution
Breach attribution is essential for pinpointing both the perpetrators and their motivations, which in turn fosters accountability, strengthens deterrence, and informs more robust security strategies. By accurately identifying threat actors, organizations can pursue legal and diplomatic channels to hold them responsible, while the awareness that attacks are traceable discourages adversaries from targeting the enterprise. Moreover, detailed analysis of attackers’ tactics, techniques, and procedures (TTPs) empowers security teams to anticipate and defend against future threats of a similar nature.
Dr. S. N. Tripathy
Retail: Demand Forecasting and Inventory Management
Effective demand forecasting and inventory management are critical components of successful retail operations. By anticipating customer demand and aligning inventory accordingly, retailers can enhance efficiency, reduce costs, and improve customer satisfaction. Demand Forecasting in Retail Demand forecasting involves predicting future customer demand using historical sales data, market trends, seasonal patterns, and promotional activities. Accurate forecasts enable retailers to make informed decisions about procurement, production, and distribution, ensuring that the right products are available at the right time. Inventory Management Inventory management is the process of overseeing and controlling the ordering, storage, and use of products to meet customer demand efficiently. It aims to maintain optimal inventory levels, minimize holding costs, and prevent stockouts or overstock situations. Integration of Demand Forecasting and Inventory Management Integrating demand forecasting with inventory management allows retailers to: Optimise Inventory Levels: Aligning inventory with accurate demand forecasts reduces excess stock and minimises the risk of stockouts. Reduce Carrying Costs: Maintaining optimal stock levels lowers storage, insurance, and depreciation expenses. Enhance Customer Satisfaction: Ensuring product availability when customers need it improves service levels and fosters loyalty. Improve Cash Flow: Efficient inventory management frees up capital tied in unsold stock, enhancing liquidity. Support Strategic Planning: Accurate forecasts inform decisions on product launches, pricing strategies, and market expansion. By leveraging demand forecasting and robust inventory management practices, retailers can create a responsive supply chain that adapts to market dynamics, reduces operational costs, and delivers superior customer experiences.
Dr. S. N. Tripathy
Agriculture: Crop Management
Crop management is a systematic discipline aimed at maximizing crop yields and farm profitability while conserving soil, water and nutrient resources. By carefully planning and integrating each phase—from nutrient supply and water delivery to weed, pest and disease control—farmers can achieve optimal productivity with minimal environmental impact. Key Components of Crop Management Fertiliser & Manure Application Precisely calibrating the type, rate and timing of fertilisers and organic manures ensures crops receive the right balance of macro- and micronutrients throughout their growth cycle, improves soil health and reduces nutrient losses. Irrigation Scheduling Developing irrigation plans based on crop water requirements, soil moisture monitoring and weather forecasts allows for efficient water use, preventing both water stress and wastage. Weed Control Employing an integrated weed management strategy—combining cultural, mechanical and chemical measures—suppresses weed competition, safeguards crop yields and curbs herbicide resistance. Pest & Disease Management Implementing preventative and responsive tactics such as crop rotation, resistant varieties, biological controls and targeted agrochemicals minimizes crop damage while promoting ecological balance. Harvesting Timing harvest operations to coincide with peak maturity and employing proper post-harvest handling techniques preserves grain quality, reduces losses and enhances market value.
Dr. S. N. Tripathy
Disaster management
This concise statement underscores how disaster management systematically unites prevention, preparedness, response, and recovery to lessen impacts on communities and infrastructure. It emphasises proactive risk reduction, meticulous planning and training, swift operational response, and coordinated recovery—all aimed at safeguarding lives, preserving critical assets, and strengthening societal resilience against future hazards. Disaster management integrates prevention, preparedness, response, and recovery efforts to mitigate the effects of natural and man-made emergencies on communities and infrastructure. Through proactive risk reduction, robust planning and training, rapid response operations, and coordinated recovery strategies, it seeks to safeguard lives, preserve critical assets, and enhance societal resilience against future hazards.
Dr. S. N. Tripathy
Climate Science: Human Impact Analysis
This analysis examines the principal ways in which human activities—most notably the combustion of fossil fuels, large-scale deforestation, and intensive industrial operations—elevate atmospheric concentrations of greenhouse gases, thereby enhancing the natural heat-trapping effect and driving a rise in global temperatures alongside a suite of related climatic disturbances. By rigorously quantifying the direct contribution of anthropogenic emissions to key climate indicators—such as mean surface warming and the frequency or intensity of extreme weather events—this work provides an empirical foundation to guide and optimize evidence-based policy interventions.
Dr. S. N. Tripathy
Smart Urban Planning: Sustainability Analytics
Smart Urban Planning leverages advanced sustainability analytics—powered by big data and GIS—to design resilient, environmentally conscious, and socially equitable cities. By uncovering causal relationships between urban policies (for example, zoning reforms or public-transit investments) and measurable outcomes such as emissions reductions and eased traffic congestion, this data-driven approach optimizes urban functions, enhances resource efficiency, and elevates overall quality of life while minimizing negative environmental impacts.
Dr. S. N. Tripathy
Fraud Detection: Transaction Analysis
Transaction analysis in fraud detection encompasses the systematic examination of both historical and real-time transactional datasets to identify irregularities and anomalous patterns indicative of fraudulent behavior. By leveraging a combination of rule-based systems, statistical anomaly-detection models, and advanced machine-learning algorithms, organizations can continuously monitor transaction streams, compute dynamic risk scores, and generate real-time alerts for suspicious activities. Furthermore, uncovering causal relationships within these data enables practitioners to more accurately differentiate between legitimate and fraudulent transactions—thereby improving detection precision and reducing false-positive rates across credit-card processing, online payments, point-of-sale systems, and mobile-payment platforms.
Dr. S. N. Tripathy