Senior Risk Model Analyst, Crescent Bank & Trust; June 2015 - Present

• Use SAS and R programming to improve current life time loan loss forecasting models by applying strong statistical knowledge, including model selection, model assumption check, backtest, and stress test

• Found principle factors which are used to determine a charge off loan would be full balance charge off or deficiency charge off by setting up a logistic regression model in SAS EG

• Created roll rate model based on Markov chain theory, helped finance department increase the accuracy of forecasting monthly loan loss by 10% for next 6 months, and it is the first time for CBT to project future monthly losses for more than the following one month

• Generated different roll rate analysis to forecast the amount of each delinquency bucket for next month end, assisted collection department to set goals for all the collectors, and ensured our delinquency buckets stay healthy

• Applied most predictive factors in business to build new loss matrix (3 dimensions) which is applied to most of our pricing models

• Recently, created a new net loss timing curve which challenges our current curve and helps managers to better determine the proper level of funding to put aside for Crescent Bank's Allowance for Loan and Lease Losses (ALLL)

• Update daily and monthly reports using SQL, Excel and Crystal Report software, monitor the performance of different business sections, deep dive when found unique records, and good at exploring underlying business opportunities

• Do multiple analysis in the same time period, successfully complete those tasks before the deadlines along with well-organized charts and analysis reports

• Always think deeply and come up innovative ideas and have the ability to make them come true

• Senior Risk Model Analyst, Crescent Bank & Trust; June 2015 - Present

• Use SAS and R programming to improve current life time loan loss forecasting models by applying strong statistical knowledge, including model selection, model assumption check, backtest, and stress test

• Found principle factors which are used to determine a charge off loan would be full balance charge off or deficiency charge off by setting up a logistic regression model in SAS EG

• Created roll rate model based on Markov chain theory, helped finance department increase the accuracy of forecasting monthly loan loss by 10% for next 6 months, and it is the first time for CBT to project future monthly losses for more than the following one month

• Generated different roll rate analysis to forecast the amount of each delinquency bucket for next month end, assisted collection department to set goals for all the collectors, and ensured our delinquency buckets stay healthy

• Applied most predictive factors in business to build new loss matrix (3 dimensions) which is applied to most of our pricing models

• Recently, created a new net loss timing curve which challenges our current curve and helps managers to better determine the proper level of funding to put aside for Crescent Bank's Allowance for Loan and Lease Losses (ALLL)

• Update daily and monthly reports using SQL, Excel and Crystal Report software, monitor the performance of different business sections, deep dive when found unique records, and good at exploring underlying business opportunities

• Do multiple analysis in the same time period, successfully complete those tasks before the deadlines along with well-organized charts and analysis reports

• Always think deeply and come up innovative ideas and have the ability to make them come true

 

 

 

 

Statistical Consulting Center, Auburn, AL; Statistician; November 2014 – May 2015

• Leveraged statistical methods and problem-solving skills to assist customers with analyzing raw data, setting up models and examining the experiment designs

• Translated business issues into clear research objectives, and determined the right approach to answer the right business questions and also succeeded in accomplishing the requirements of customers efficiently

• Applied statistical analytics into practice, and learn more professional knowledge in different fields, such as biology, pharmacy, and finance