Tustin Recruiting

Commercial Banker Resume Template

A professional resume template tailored for Commercial Banker positions, highlighting technical skills and project experience brought to you by Tustin Recruiting.

[Your Full Name]

Commercial Banker

your.email@example.com (555) 555-5555 City, State LinkedIn Profile

Summary

Results-driven professional with extensive experience in providing financial services to businesses. Proven track record of delivering high-quality solutions and optimizing performance to enhance client satisfaction and profitability.

Skills

languages:
frameworks:
tools:
concepts:

Experience

Senior Commercial Banker

Greenfield Bank & Trust

Jan 2022 - Present
  • Increased portfolio revenue by 25% by acquiring 15 new mid-sized business accounts and optimizing existing customer relationships.
  • Led a team of junior bankers, providing mentorship and improving team performance by 30% through sales strategy workshops.
  • Implemented a new client onboarding system reducing approval time by 40%.

Commercial Banking Officer

Lifeline Financial Services

Mar 2019 - Dec 2021
  • Managed a $50M loan portfolio, ensuring a 98% repayment compliance via effective risk assessment and client management strategies.
  • Developed a new credit analysis process that cut down loan processing time by 20%.
  • Secured $30M in new lending opportunities through networking events and client meetings.

Commercial Banking Analyst

Pacific Trust Corporation

Jun 2017 - Feb 2019
  • Assisted in preparing financial statements, loan proposals, and client presentations, contributing to a 15% increase in loan approvals.
  • Conducted comprehensive market research leading to the identification of new business opportunities, resulting in a 10% portfolio growth.
  • Reduced data processing errors by 20% through the development of a standardized workflow procedure.

Education

B.S. Finance

University of Southern California, Los Angeles, CA

Graduated: 2017

Projects

Business Loan Optimization

Developed an algorithm to predict client creditworthiness, reducing loan default risk by 15% and enhancing lending decision accuracy.

Technologies: Python, SQL, Financial Modeling