About Me
Working Domain:- i) I work in Fraud & Credit Services Team where strategies are designed and implemented for Credit Decisioning and Lending engine .The decisions are Accept, Reject, Refer N/A, etc., that determines the credit lending and offers for a customer. The NTB applications are treated differently from ETB ones, so the strategy built is done accordingly. ii) My area of work lies in implementation of systems responsible for Corporate Account Opening and Retail Account Management Non-Cards. iii) Strategies are designed keeping the business requirements aligned after analyzing the bureau and credit history of a customer. The behaviour score is used to identify the credit worthiness. iv) Real time and batch data of customer is used to track the behavioural changes if any and then the categorization is done. It helps to fix the lending for an application. Tool and Technologies Used:- i) Power Curve Strategy Management & SAS Intelligent Decisioning Tool is used heavily for building the strategies and deploying them into production. . ii)SAS EG is used for data mining and feature Extraction that included data conversion from .sas7bdat to csv and vice-versa to make it compatible to the decisioning tool for unit testing. iii) SAS EG is also used to run query using PROC SQL and it’s macros in order to track the customer onboarding channels usage and their journey. The count of lending was also extracted and all were used to prepare dashboard for quick analysis of the data. iv) Python is utilized for automation of document classification, code comparison and filtering of the data. Pandas, Matplotlib, OS etc modules are used to automate the manual stuffs. v) Nowadays, Machine Learning Models are also deployed to fetch dynamic scores with the change in behavioural pattern. vi)Mismatch analysis is also my role to uncover the root causes of disparity in outcomes using traces so as to inform business for making fixes in the strategy if any. vii)At present we are transitioning to a new tool which is Python based decisioning platform that is AI enabled, integrated with cloud like GCP, Jenkins for code promotion, GIT hub for code repository to smoothen and make the process faster.
Education
2020
Bachelor Of Technology
Work & Experience
Tata Consultancy Services Ltd. 5 April 2021 - 20 Jan 2023
Assistant System Engineer
I worked as a Developer using SQL for preparing query to fetch the customer data for Dashboard Preparation and Analysis.
HSBC Electronic Data Processing Ltd. 25 Jan 2023 - Till Date
Data Analytics
Working Domain:- i) I work in Fraud & Credit Services Team where strategies are designed and implemented for Credit Decisioning and Lending engine .The decisions are Accept, Reject, Refer N/A, etc., that determines the credit lending and offers for a customer. The NTB applications are treated differently from ETB ones, so the strategy built is done accordingly. ii) My area of work lies in implementation of systems responsible for Corporate Account Opening and Retail Account Management Non-Cards. iii) Strategies are designed keeping the business requirements aligned after analyzing the bureau and credit history of a customer. The behaviour score is used to identify the credit worthiness. iv) Real time and batch data of customer is used to track the behavioural changes if any and then the categorization is done. It helps to fix the lending for an application. Tool and Technologies Used:- i) PowerCurve Strategy Management was used heavily in the initial phase for building the strategies and deploying them into production. Later on PSCM was replaced by SAS Intelligent Decisioning Tool. ii)SAS EG is used for data mining and feature Extraction that included data conversion from .sas7bdat to csv and vice-versa to make it compatible to the decisioning tool for unit testing. iii) SAS EG is also used to run query in order to track the customer onboarding channels usage and their journey. The count of lending was also extracted and all were used to prepare dashboard for quick analysis of the data. iv) Python is utilized for automation of document classification, code comparison and filtering of the data. v) Pandas, Matplotlib, OS etc modules are used to automate the manual stuffs. vi) Nowadays, Machine Learning Models are also deployed to fetch dynamic scores with the change in behavioural pattern.