Swati is a research associate at Alliance Manchester Business School, since October 2017. Her research is focused on the development of explainable AI and machine learning models to automate decision making. She designs package in Python to enable industrial research partners to use AI-based tool to automate decision making in finance, insurance, law, and healthcare. Her research interest includes Belief Rule-Based, Evidence reasoning, Petri-nets, explainability of Deep neural network and Non-linear constrained optimization.
Currently, she is working on SmartClaim insurance project. SmartClaims is a collaboration between Kennedys Law, AXA, Leap Beyond, and The University of Manchester, to develop an industry-defining toolkit for the augmentation and automation of the whole claims process, with specific attention on the liability insurance. In past, she has worked on Fintech project with Together Financial Services to develop explainable AI decision-support-system to automated mortgage lending and a consulting project with AstraZeneca for improvement of financial data quality by machine learning. She has collaborated with Berkeley Research Group to develop a multi-segmented deep-convolution neural network to detect the fault in current sensors.
Swati graduated with a Masters in Operational Research in Finance from the University of Edinburgh in 2011. She did her Masters dissertation on econometric modelling with Shell Royal Dutch plc and then worked as a statistician in School of Mathematics, the University of Edinburgh (2012). She completed her PhD in Operational Research and Applied Statistics from Glasgow Caledonian University in 2016. After completing her PhD, she joined the University of Nottingham as a Research Fellow, where she worked on Innovative Intelligent Rail (In2Rail) project funded by European Commission. This project was coordinated by Network Rail (UK), and SNCF (French National Railway Company).