Client Objective
Understand the client requirements. Develop customised solution for the client based on availability of type, volume &quality of data and current IT capabilities of the bank.
Analytics
Services
Power your business performance using analytics
Overview
Unlike Consulting Services where our objective is to help our clients in setting up analytics capabilities, here we help the clients by actually developing the requisite analytical models to support/augment the internal decision-making process.
We execute analytics projects primarily using our proprietary research in the area of data sciences, analytical techniques (from standard statistical techniques like regression analysis to Machine learning based models/techniques), suitable technologies and most importantly based on our domain knowledge of working in the financial sector.
Our domain and functional expert work in a collaborative approach with the client to provide a meaningful definition to the problems and requirements in accordance with the right business context. We have robust processes that address every phase of an end-to-end analytics cycle. Based on industry standards, these processes guarantee quality outputs.
We use proprietary data exploration tools, data models and model implementation tools to ensure that the solutions are delivered in the least possible time. Our relentless drive to capture its experience and expertise into products and tools ensures that the clients always have the advantage in terms of faster implementation and reduced efforts on part of company’s staff.
At Auronova, traditional statistics like regression, survival analysis is combined with modern techniques like neural networks, decision trees and genetic algorithms to increase the accuracy of the predictive models. Most of our experts have strong mathematical background ensuring strong foundation in analytics.
Our endeavour to bring the latest thinking in all the analytics projects in context of the current and future aspirational state of evolution of the client.
Approach
Data Preparation
Identify the requisite data both internal as well as external to improve the efficacy of the developed models. Define data dimensions such as volume, velocity, frequency relevant for the proposed customised solution. Prepare the data set for Modeling purposes.
Model Development
Identify the analytical techniques to be applied based on the nature of the problem, i.e. descriptive, predictive, prescriptive, optimization, clustering, Machine Learning, standard statistical techniques like regressions analysis, simulation etc. Develop the model based on the identified technique and prepared data set as part of Step 2 above. For developing the models, we may use client IT infrastructure of our own proprietary tools.
Model Documentation
Document the results of the Modeling activity inclusive of documentation of methodology, results, model assumptions, limitations and future areas of improvement.
Ongoing Model Monitoring
We also provide services to our clients in maintaining and improving the developed models on an on-going basis. Client may maintain the models internally based on internal capabilities. Irrespective of the client approach, it’s very important to maintain the models as we believe it’s a journey rather than a destination.
