Deepak Singh

Spearheaded cross-functional initiatives across departments to implement global liquidity risk management & funding policies

• Monitored metrics and projected PnL for treasury which helped summarizing trends, drivers to the management
• Worked with the senior management & IT on project administration, reporting & management of key initiatives
• Analyzed market data and business trends to assist in decision making on funding & liquidity management.
• Oversaw hiring, developed a strong team of 12 valuable, talented professionals, establishing a strong presence in BLR.

Led the team of developers in the implementation of FRTB Capital Charge calculator models, Credit Spread Risk models, RNIV including Back-testing and PLA test.

• Spearheaded the validation of Credit Spread Risk models as part of CCAR submission
• RNIVs - Review, methodology development and documentation of RNIV(risk not in VaR)
• Conducted capital impact analysis on various scenarios
• Designed & directed implementation of dashboard for RNIV calculation and reporting using Python

• Led a team of developers & analysts in designing the architecture for data flow in the quantitative models for capital charge calculation and allocation
• Provided specialized financial planning support for clients with diverse portfolios and individual needs.
• Responsible for end to end validation of credit risk models (PD, LGD models)
• Applied ML techniques for customer segmentation by demographic data and spending pattern to help validate the transition matrix based CCAR model
• Designed a segment-specific recommendation engine for target marketing.

Led multiple projects covering Application Score, Fraud Score, Loss Forecasting, Tweet/Sentiment Analysis and probability default models using statistical and ML techniques

• Automated the performance report generation for various loans
• Developed & launched a risk management dashboard to track the performance of automated credit decision

• Analyzed transaction level data using statistical and machine learning tools to identify and predict prevailing fraud patterns
• Developed various strategic and tactical rules for fraud loss mitigation with multi-million dollar yearly business impact