Badrish Davay is a Sr. Manager of Engineering at Capital One, building applications in Big Data, Event Streaming and Machine Learning space. With 15 years of technology expertise implementing complex data-driven solutions, Badrishâ€™s background includes building and executing technology strategy with focus in Big Data, Analytics, IoT and Cloud for startup organizations as well IT delivery for Financial and Mortgage space. Prior to landing at Capital One, he has worked in the Health Care and Professional services organizations building complex Big data ecosystems for generating insights.
In the Credit Card Companies, illegitimate credit card usage is a serious problem which results in a need to accurately detect fraudulent transactions vs non-fraudulent transactions. All organizations can be hugely impacted by fraud and fraudulent activities, especially those in financial services. The threat can originate from internal or external, but the effects can be devastating - including loss of consumer confidence, incarceration for those involved, even up to downfall of a corporation. Despite regular fraud prevention measures, these are constantly being put to the test in an attempt to beat the system. Fraud detection is a task of predicting whether a card has been used by the cardholder. One of the methods to recognize fraud card usage is to leverage Machine Learning (ML) models. In order to more dynamically detect fraudulent transactions, one can train ML models on a set of dataset including credit card transaction information as well as card and demographic information of the owner of the account. This will be our goal of the project while leveraging Databricks.
In the Financial markets with credit card companies there is always a need to measure the risk optimally and understand the performance of products before we could invest and make strategic decisions. At Capital One we are leveraging technologies to provide end to end analytical experiences for modelers and enable self service solutions for analysts and key stakeholders by allowing them to run loss forecasting scenarios seamlessly, perform gaming analysis, compare results of model runs, create new features, gain insights and produce outputs.