DN AllConnect Data Engine for customer facing endpoints (ATM)

Market Problem The self-service channel, and especially the ATM, is increasingly crucial for banks to engage consumers and offer them access to banking services 24 hours a day, 7 days a week. As banks are looking at transforming their physical network, reducing their footprint, and converting traditional branches to advising centers where staff has more time to develop stronger relationships with customers, they are keen to transfer a broader range of transactions from the teller to the self-service channel. This is possible as consumers have unquestionably embraced using the self-service channel and rely on it for a growing number of use cases. However, as with any 24/7 device that is sometimes exposed to the harshest weather conditions, an ATM can stop working properly. Every time an ATM is down, there is a cost associated with it. For example, the fee a bank has to pay as a result of their cardholders transacting at another ATM, loss of trust, damage to their brand reputation, customer attrition, or reduced exposure to ATM marketing campaigns. Hence, availability is key, which is why it’s critical to provide banking customers with the most reliable devices, coupled with effective maintenance and repair services, to minimize the risk of out of service events and ensure that any incident is resolved in the timeliest manner. Solution With that objective in mind, Diebold Nixdorf started the connected device’s journey nearly 10 years ago, gathering IoT sensor data from ATMs in the field and analyzing it to track and continuously improve the performance of existing devices, incorporating the learning within research and development activities. The knowledge gained over the past decade has enabled us to make critical engineering enhancements to the devices and components, while increasing their performance and reliability. With the development of DN AllConnect Data Engine as a key enabler of our maintenance services we went one step further. We embedded the latest developments in the Internet of Things (IoT), cloud computing and machine-learning technologies and Artificial Intelligence (AI) into our service framework. So, we can not only resolve hardware-related technical incidents and complete scheduled maintenance in the fastest and most efficient way, but also detect impending failures and fix them before they occur. What’s the end game? We optimize device availability by shifting from a reactive to a proactive service model, enabling financial institutions to meet the expectations of increasingly demanding consumers while optimizing their internal operational efficiencies. Engineered completely in-house, DN AllConnect Data Engine is the foundation of Diebold Nixdorf’s data-driven service model. It builds on a unique combination of decades of unmatched engineering experience and a continuously augmented global knowledge base, as well as the application of the latest developments in Internet of Things (IoT), cloud computing and storage, machine-learning technologies and Artificial Intelligence (AI).Once a DN Series device is linked to DN AllConnect Data Engine, deep technical and firmware-level data is continuously retrieved from all sensors and data points by a light-weight data-collection agent within every connected, deployed device. The data is securely sent to DN AllConnect Data Engine where it is continually aggregated with the anonymous data of more than 150,000 devices across a broad range of use cases and geographies, which enables us to identify and monitor patterns that occur through the devices’ lifecycles. Everything is then correlated with historic data, inventory data and our engineering knowledge base. This vast amount of information enables DN AllConnect Data Engine to build a precise and constantly refined personality profile for every single device and to generate tailored, actionable insights for each of them. Our service model relies on the generation by DN AllConnect Data Engine of three types of actionable insights that enable the shift to a more efficient and proactive service model: prescriptive – preventative – predictive. Prescriptive, fixing incidents faster and better DN AllConnect Data Engine continually receives data packages on a scheduled basis and also in real-time whenever a device fails. It leverages its unique knowledge of the failing device, analyses the latest deep data collected, diagnoses the issue and identifies the most likely root cause(s). Root cause identification is completed remotely within a matter of seconds, without the need to send a technician to inspect a device, which obviously contributes to making the resolution of an incident happen much faster and earlier. It then provides information about the precise fix, the required level of skills and experience of the technician, the spare parts needed and the time the repair should take. It is what we call the right tech - right part - right time - right fix approach. Preventative, ensuring every service call is optimized In addition to prescribing how to resolve an outstanding failure, DN AllConnect Data Engine analyses information about the entire ATM, providing insights into other areas of the device that may require attention and therefore be actioned by the technician while they are on-site. For instance, if the number of moved notes is high since the last service, a service ticket may be enriched with a request to clean and calibrate the tape sensor. This proactive, preventative intervention is designed to pre-empt a possible future outage and to maximize uptime. Predictive, recommending proactive service calls By analyzing data patterns, trends, leading indicators, etc., DN AllConnect Data Engine can identify an impending failure, triggering a recommendation to act upon this insight and schedule a maintenance visit at a time of low customer usage to avoid an unplanned future outage. We are shifting our service model from reactive to truly predictive. Business value for financial institutions Delighted consumers Increased revenue Lower Total Cost of Ownership (TCO) and increased efficiencies.