Cash Logistics Module

ATM.iQ    ATM.iQ

ATM.iQ predicts the demand for currency at each self-service device on an individual basis. By applying an optimization algorithm, the cash position for each device is determined. This optimization procedure, which determines the lowest cost of cash distribution - based on accurate supply and demand forecasting – provides the opportunity to lower its operational expenses and improve the return on its cash assets.

·          Automate and optimize network’s daily operations

·          Minimize expenses

·          Increase customer satisfaction

·          Increase network availability

·          Proactive management by analysis and forecasting

·          Real time view and control of all services

·          Shorten time to introduce new services to the market

·          Save time

·          Minimized risk of the human factor

The challenges of managing the cash cycle vary between the two key distribution and delivery channels to customers – in-branch and via self-service device. This is a huge logistical challenge for banks. To tackle it, they have developed a cash cycle that encompasses the entire cash supply chain including cash logistics, self-service device management, branch management (including forecasting and planning) and retail cash logistics.

It generally gets sufficient volumes of cash to the branches and self-service devices – both at branches and off-site – where customers need it. But when it fails, banks incur high costs both in terms of customer satisfaction and in financial loss. In comparison to the integrated supply chains seen in other industries, the banking industry's cash cycle combines high ongoing costs with low efficiency, tying up more financial, real estate and human resources than it needs to.

ATM^.iQ forecasting models are created using historical cash demand data and artificial neural networks techniques. The historical cash demand for every ATM varies with time and is overlaid with non stationary behaviour of users and with additional factors, such as paydays, holidays, and seasonal demand in a specific area. Cash drawings are subject to trends and generally follow weekly, monthly and annual cycles. The general idea behind the use of neural networks in cash forecasting is to allow the network to map the nonlinear relationships between various factors affecting the cash withdrawal and the actual cash demand.

ATM^.iQ performs efficient optimization procedure for ATMs’ cash uploading estimations. Figure below shows main steps for self-service device network effective cash cycle management.

·          Cash Flow Monitoring – view device cash flow in table or by diagram to within one hour

·          Cash Status Monitoring – device cash monitoring during cassettes of cash in and / or cash out replenishment

·          Cash Forecasting – forecast of cash in, cash out for appointed period of time

·          Replenishment Planning – device replenishment planning (when, how much, what currency

·         Optimized Replenishment Planning – device replenishment planning using minimum expenses for device support

·          Cash Logistics Reporting – generation of reports for every function of product representation

·          User Assessment

·          Device Assessment

Server

.iQ Server is build on Enterprise Message Bus (ESB) which provides orchestration and integration of a different business services provided by .iQ platform which are:

1.     UM – user management service;

2.     DM – device or asset management service;

3.     CM – cash logistics service;

4.     ATMeye^.iQ – video security service.

Hardware Configuration

Application Server - core server which includes the following components (servers):

·          Agent server – responsible for .iQ Agent lifecycle monitoring: handling transferred data, setting up parameters and neural network computation.

·          Services server – provides services for .iQ Clients and other services.

·          Database server – .iQ database schemes.