Barton Wang Email & Phone Number

Concord, Ca, Us

Wilmington, Delaware, Us

• Lead analysts to develop full-spectrum underwriting strategies from prime to no-hit/thin file segments • Lead and build 7 credit risk models based on bureaus / applications / partners and alternative data sources.• Support loan securitization process & provide loss forecasting and loan pool selections. • Evaluate machine learning methodologies & new data sources for risk/fraud models & strategies.• Build selection models and predict loss rates for Lending club and Prosper loan purchase & securitization.• Support board of director meeting risk deck, review/monitor loan performance and make recommendations. • Develop customized attributes from bureau tradelines / inquiries to use in model/strategy development. • Project management and partner with product/engineering teams on model and strategy implementation. • Lead analysts to build analytics database (application and vintage performance data) and initiate KPI MIS reports.

Chicago, Illinois, Us

Pricing and credit risk management for indirect lending portfolios - Auto, RV and Marine• Built new credit policies, underwriting & review rules, and expected loss rates for Auto Non/Sub-Prime loans. • Increased automatic decision rate and approval rate by using new bureau attributes, developing new strategies, and implementing new scorecards in the new underwriting platform. • Developed credit segments to differentiate risk and improved approval rates, reduced manual review rates, up-sell/down-sell, and pricing in the full specturm auto lending. • Worked with IT for underwriting strategy implementation and assist in system integration tests and user acceptance tests under new underwriting and workflow platforms. • Expanded RV/Marine underwriting strategies using loan-to-value ratio and age of collateral to achieve benchmark approval rates, mitigate credit risk and maximize profitability.• Developed formula-based risk pricing frameworks for Auto loans based on expected loss rates, cost of funds, and current market rates. • Worked on price elasticity by segments to quantify the impact between market shares and profits and create test and learn strategies to improve portfolio margin.

Marketing and financial analytics for a leading pre-paid debit card company that provides alternative online banking solutions to over 40 million people. • Head of analytics and managed a team of 3 to support acquisition marketing, annual/monthly marketing budgets, customer lifecycle & retention, product and pricing, and new business developments. • Played a major role in acquiring new accounts through multi-channels and provided marketing ROI for SEO, SEM, affiliates, display, mobile, TV, radio, email, and direct mail. • Developed campaign targeting scorecards to increase response rate by 20% & reduce mailing cost by 32%. • Built customer level prospect database and created test strategies to mail based on frequency of offers and triggers, resulting in a 30% increase in mail-able population from existing customers. • Created vintage-based customer lifetime value to guide marketing spend and performed financial modeling & analysis to evaluate the profitability of different marketing channels, products, customer segments and fee plans. • Conducted credit review for short-term loan products using deposit amounts and tenure of banking relationship and recommended new underwriting strategies, line, and pricing assignments.• Worked with business development teams for new products and features (e.g. free ATM withdraw, mobile deposit, and get your paychecks 2 days faster) and conducted break-even, scenario and payback analysis. • Used BI tools (Crystal Reports) to generate daily/weekly automatic dashboards and created monthly business-line portfolio economic dashboards and actual vs. forecast variance reports for the senior management team.• Revamped funding rate projection and attrition assumptions to reduce actual - forecast variance by 26%.

Us

Acquisition risk management for WaMu retail consumer cards, small business cards, and secured cards.• Led a risk team of 3 and managed underwriting strategies to book over 2 million new accounts with $4 billion new balance in 2 years, exceeding plan by 20% and generating new accounts with credit quality that is 20% better than existing accounts. • Utilized retail customer relationship data (e.g. deposit balance, number and tenure of relationships) derived from other WaMu business lines and designed relationship -based strategies in card decisioning, resulting in a 15% decrease in delinquency rate and a 3% increase in approval rate.• Developed custom line assignment strategy using bureau utilization and relationship score for WaMu branch customers; resulting in a 17% reduction in loss rates. • Conducted proactive risk management actions to prevent credit deterioration in home price depression areas using housing price index, mortgage information and WaMu home loan and equity account performance.• Supported the launch of WaMu Secured Cards by developing credit policies, designing credit requirements, and overseeing the risk strategy implementation and execution in retail branches.

London, Gb

Underwriting strategies for General Motors (GM) cards and Union Plus cards • Managed, mentored and trained analysts on the credit card life cycle, risk management concepts and account acquisition campaign processes. Shared best practices in analytics, technology and business concepts.• Identified growth opportunities by evaluating the existing underwriting policies and test results. Recommended changes on the existing decline criteria to increase account volume by 10% and risk adjusted return by 12%. • Compared the score performance of FICO and NextGen in separating good and bad accounts. Quantified the business impact from transitioning FICO to NextGen score. Suggested score mappings in underwriting strategies to minimize negative impact in account volume while improving new accounts’ creditworthiness. • Lowered charge-off rates and improved profitability in a high-risk segment by introducing a sub-prime risk score in near prime underwriting strategies which identified 45% of bad accounts in less than 5% of the total population in the high-risk segment. • Redefined existing risk-segments using statistical tools (Knowledge Studio/SAS) to analyze historical customer behavior data. Recommended new segment criteria to ensure volume stability and performance consistency.

Mba, Finance

Ms, Computer Information Systems

Ba, Economics