RuProject for MTS Bank – Implementation of an information system for forecasting the amounts and timing of replenishment and cash-in-transit for self-service devices
The project is designed for the implementation, support, and functional development of an information system that forecasts the amounts and timing of replenishment and cash-in-transit for self-service devices using neural networks
Key Functional Modules:
- Use of neural networks (autoencoders, word2vec, GloVe, etc.)
- Selection of the optimal forecasting method for a specific device
- Continuous retraining of models to improve accuracy
- Breakdown by cash operating units
- Editing and approval of proposed amounts
- Accounting for all types of self-service devices, insurance limits, and nearby devices
- Display of balances, withdrawals, and forecasts
- Highlighting of deviations and risks
- Ability to respond promptly
- Differentiated calculation system
- Accounting for average cost and fee directories
- Creating and configuring custom roles
- Assigning functions and access rights
- Exporting/importing roles
- Management of calculation parameters (funding rate, rounding, operating modes)
- Accounting for public holidays (support for multiple calendars)
- ProView, OpenWay, processing, and others
- Receiving cards, balances, and turnovers
- Creating plans based on real data
Additional Features:
Automatic inclusion of devices in the plan (based on criteria)
Calculation of the optimal loading amount and banknote composition
Accounting for insurance amounts and funding
Automatic sending of requests by email using a template
Cost optimization: cash, insurance, labor
Automatic inclusion of devices in the plan (based on criteria)
Advantages of the system:
Результат
- Improved accuracy of cash flow planning and security
- Optimization of cash collection and cost reduction
- Flexible customization to suit the bank’s business logic
- Relevance ensured through continuous development and support
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