RISK / QUANTITATIVE OPERATIONS / 2016 — 2020

Using quantitative models to see through cash-flow and operating risk

For a national fintech platform, I built cash-flow forecasting, stress testing, portfolio risk pricing, and profitability management into the finance operating rhythm.

1–3 monthsEarlier liquidity warningForecasting moved beyond short-term visibility.

+15%Efficiency upliftCrisis response improved overall operating efficiency.

30 provincesOperating coverageRisk and profitability became locally actionable.

01 / THE CHALLENGE

What had to be made clear

Turn full-population user and repayment data into decisions on liquidity, risk pricing, resource allocation, and crisis response.

02 / MY ROLE

Where I carried the decision

As Finance Manager, I led a nine-person team and partnered with business leaders on modelling, risk response, budgeting, and performance management.

03 / THE APPROACH

How the work moved from judgment to execution

01 Modelled cash flows from full-population repayment behaviour.

02 Added stress tests to extend liquidity warning from weeks to months.

03 Built portfolio delinquency and risk-pricing models from user and behaviour features.

04 Created product and city-level profitability monitoring.

05 Reallocated resources and removed low-productivity capacity during the industry crisis.

04 / DELIVERABLES

What entered the operating system

↳ Cash-flow forecast and stress-test engine

↳ Portfolio risk-pricing model

↳ Product and city profitability monitor

↳ Business restructuring and resource-allocation plan

BEYOND THE RESULT

See the method in the system at work

This case is one part of a wider finance-and-AI practice. Explore the prototypes that make the underlying logic tangible.