Client Background
Client is a prominent consumer bank in Indonesia with a network of 400 branches, 3,000 ATMs and 1,000 recycle machines (RCMs).
Business Need
Our client was looking for an optimization solution to better manage the distribution of cash across their many customer interaction points. They requested a forecast of deposits and withdrawals on a daily basis at the branch, ATM and RCM level. In addition, Valiance needed to find optimal dates for cash-in (cash deposits) required at the branch, ATM and RCM level to meet the withdrawal demand subject to following constraints:
- Each cash deposit by a van incurs transportation costs. Transportation cost should be minimized in a way that we bring in cash needed for next few days in a single journey instead of many.
- Excess cash parked in branches implies additional interest cost paid to central bank. Bringing cash required to meet demand would save excessive interest paid.
- Limit to the cash amount every branch, ATM and RCM can hold.
Solution
- Valiance created a Neural Network-based machine algorithm to forecast transactions.
- A Quadratic Optimization model was implemented to optimize cash in/cash out for RCM, ATM and cash centers.
Outcome
- Daily forecasts for deposits and withdrawals helped the bank to more efficiently distribute its money across ATMs, RCMs and branches. Forecasts were accurate to the tune of +/-15% on average.
- Client reduced its transportation cost and saved interest on borrowed money by optimizing cash deposits to branches, atm’s and RCM’s once the forecast for deposits and withdrawals was complete.
