Balancing Financial Inclusion and Portfolio Profitability

Provider: BankersLab

Type: Facilitated

Duration: 5 weeks / 4-5 hours per week (20-25 hours total)

Target Audience: Banks, Retail Lenders, Micro-Lenders, and Finance Companies; Retail Credit Risk Managers, Risk Analysts, MIS Analysts; Collections Managers; Product Managers; Retail Lending Analysts and Modelers. To gain maximum benefit from this course, it is recommended that three to five managers from the same institution take this course together.

Type: Cohort-based, facilitated course with simulation and self-paced content

About the course:

Financial inclusion in lending requires thoughtful product structure, savvy customer targeting, and diligent customer management. There is not one ‘right answer’ to reach this goal, but rather a systemic approach of “test and learn” strategies. By the end of this course, participants will be able to create and evaluate lending strategies and products with a financial inclusion mind-set.

Each week, participants will test drive their skills in underwriting, risk strategy, and portfolio management in a simulation game. In order to win, players must successfully operate the most profitable virtual bank with the most satisfied customers.

This course will guide participants through the lending life cycle. First, design your product offering to suit your customer’s needs and preference, whilst being financially responsible, and profitable for your portfolio. Next, create your loan approval strategy informed by the data and tools available. Finally, you must actively manage the portfolio to ensure good outcomes for both your customers and your bottom line.

Learning Outcomes:

After completing this course, participants will be able to:

  1. Describe the stages of delinquency, and best practice consideration for collection strategies for each

  2. Explain the best practices for contacting customers, and how to ‘tilt’ the timing, type and tone of the contact

  3. Apply techniques for segmenting accounts into specialized treatment queues, using data such as behaviour scores and ‘balance-at-risk’

  4. Calculate and forecast delinquency flow rates, full year losses, and collector capacity

  5. Design strategy trees and segmented actions for early and late stage collections