Cash Management Optimization
In today’s world, a lot of operational problems are solved by new technologies such as Machine Learning. In the ATM world, one of the main problems is, “How to transform the idle cash into active cash and decrease cost of maintaining cash” in an automatic way that is now solved via Machine Learning and Operations Research. This is called “Cash Management Optimization” which has the potential to save millions in USD, EUR or TRY every month for sizeable banks and financial institutions.
As a customer, access to cash from a bank ATM or branch counter may be free and easy. But providing enough cash available whenever a customer demands, can be a costly and challenging problem to solve. The cash management departments must meet the fluctuating customer demand and have to minimize their cash management costs and meet changing regulatory requirements. Hence, a Cash Management Optimization system takes a lot of constraints, processes a lot of data and conducts a lot different types of optimizations every day, and on an ongoing basis.
Cash Management Optimization becomes a major factor to generate huge cost efficiency for the Banks / IADs. By using Machine Learning and Operations Research based solutions, cash can be optimized while solving the following problems:
- Preventing unexpected cash-outs by predicting customer behavior and ATM/Branch transactions
- Decreasing cash replenishment costs by managing CIT parameters and producing optimal delivery amounts
- Decreasing manual workload by reducing the opportunity for personal bias via Automated Cash Planning
- Decreasing the idle cash amounts by precise cash forecasting and optimal replenishment plans
Arute’s Cash+ Solving Cash Management Optimization Problems
ATM replenishment problem is a perfect example of combining two areas of advanced analytics and achieving cash management optimization.
Once reasonably reliable forecasts of customer behavior at each ATM are obtained, the next step is to convert the forecasts into a daily execution plan for optimal reloading at just the right time. With the right optimization solution such as Arute’s Cash+, the optimization may perform an immediate %20 — %40 decrease in the idle cash levels.
Forecasting and Optimization for Cash Management Optimization
Using Time Series Forecasting techniques with a combination of Operations Research Optimization algorithms generates an efficient cash management optimization solution. Cash+ uses the historical transaction data for effective cash forecasting, uses OR optimization to convert them into optimal replenishment plans while satisfying different constraints and then monitors the outcome and daily results to improve itself. This cycle is depicted in the below figure:
The Cash Supply Chain Optimization
Cash Management Optimization via Machine Learning and Operations Research is an important aspect of effectively managing ATM and Branch networks. These solutions make the process more agile and reliable by automatically generating a network-wide optimal plan and decreasing idle cash levels. As the machine learning techniques adapt to changes in demand, investing in machine learning solutions provides long-term ROI.