Independent Consultant
Financial Services
Telecommunications
Worldwide
English
Tamil
A data scientist specialized in NBFC, developing an ‘innovative risk model’ for the finance and insurance domain. Gifted with an insatiable intellectual curiosity and the ability to mine hidden gems located within large sets of structured, semi-structured, and unstructured data. Has worked on various machine-learning projects like multilingual text mining, rule-based Text summarization, Information extraction, MOM Repayment default prediction, Fraud detection, and time-series foresting.
· Developed a product related to network capacity forecasting using trends by using a machine learning algorithm.
Identify the unexpected spikes, drops, trend changes, and level shifts in time series data by using a machine learning algorithm.
A database is created for reports analyzed and any observations made by officials. The report summarizes issues as well and stored in the database. Based on the report, if a model is run, it finds out repeated issues, gets a list of branches the issues have happened, pulls out the number of times the issue has happened, and also rates the branches. This helps in identifying the branches and sends a trigger as to why no fix was made.
· The notes have comprehensive details of a customer such as the exact nature of the applicant’s business, experience in this business, co-applicant information, etc. The note acts an additional feature for working on the credit risk model - which is a new innovative method to help improve the accuracy of the model
Alternate credit score is calculated based on non-traditional consumer information with traditional credit sources such as credit bureau reports and bank statements to predict the creditworthiness.
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