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Understanding Remote Payments Security

February 8, 2022


Remote payment security options

As remote work continues to play a major role for companies around the globe, organizations—especially in the financial services and technology sectors—must shift focus toward remote payment security.

Flexible working and remote work policies did exist before the pandemic, but the mass exodus from offices in 2020 marked a turning point for many industries. This looks to be a permanent move for many businesses, with Upwork forecasting that 36.2 million Americans will be working remotely by 2025 — an increase of 85% from pre-pandemic levels. The financial sector is one of the more impacted markets and this requires a whole new approach to operations.

There are many benefits to remote work, including flexible hours and the elimination of a commute. From the corporate side, however, there are several new challenges to overcome to maintain operational standards. This is because employees are now working on distributed networks, utilizing a personal internet connection and their own hardware. While this won’t pose a problem for some sectors, the privacy and security required in payments mean that FinTech companies must introduce new security measures.

There is cause to be optimistic: The same Upwork study found that 68% of hiring managers believe remote work is more efficient than it was at the beginning of the pandemic, suggesting that companies are finding solutions to these new problems. For the financial sector, these solutions rely on an understanding of remote payments security and the implementation of the right technical support.

The Challenge of Remote Payments

Security and data protection have always been priorities for financial services, but to be successful in the current environment, payments companies must first understand the unique challenges presented by a remote workforce. Only then can they design an appropriate response and ensure they’re meeting both customer expectations and government regulations.

There are two main concerns: maintaining sufficient data center capabilities to support a distributed workforce and ensuring customer data is protected at all times and locations, within the financial platform. The former is a capacity issue, while the latter is more of a security issue. Fortunately, both can be addressed with the right technology deployment.

The Cloud’s Role in Payments Security

The financial sector, whether processing transactions or handling people’s personal accounts, touches millions of data points and all of these must be stored securely. To accommodate this volume of activity, companies have to invest in some kind of storage system. Companies either invest in on-premise data servers — which requires building out larger server farms to scale — or they opt for cloud infrastructure.

While physical servers can enable a company to have direct ownership and control over its data, there is a much starker limit to their capacity. Syncing to this hardware and maintaining a steady, secure connection can be very challenging, especially in the era of remote work.

Cloud is not limited in this way and can quickly scale to support whatever volume the user needs. By accessing a cloud network, companies can upgrade or downgrade their capacity as needed without much disruption to operations or excessive cost. If employees are in multiple places, a cloud infrastructure can ensure fast and secure network connectivity without the need for local data centers.

Organizations that leverage cloud technology can also utilize the included security features and support of a much larger organization. While some companies may be apprehensive of the security of a public network, it can be much safer to store data here than on privately owned servers, due to the added support.

Machine Learning for Remote Payments Security

Despite this added layer of protection, it is still important for payment companies to enact their own security measures within the cloud network, especially if their customer data is being channeled through multiple remote computer locations.  Encryption should be the first port of call so that, even if a network point is compromised, the data is protected from cybercriminal activity.

The next step should be to implement technology that can improve the authentication process without creating too many manual steps for employees. When such great volumes of information are passed through multiple entry points, it can appear impossible to gain a cohesive view of digital payment activity. Without a single comprehensive overview, how can the verification process be automated in any way? The solution is to deploy machine learning (ML) in the cloud.

Once a company has a cloud network in place, it automatically sends all its data to the same ecosystem to be stored, analyzed, and processed. The information isn’t stored across separate servers, so it is already ready to be fed into a machine learning algorithm — without any need for manual intervention. This ML technology can look at every individual transaction and detect patterns, making it far more accurate than a human reviewing a sample of data.

These patterns can then be used to identify red flags for fraud, at which point the activity in question is sent to a human employee to evaluate. By ensuring that only risky activity is assessed, companies can free up their resources and also streamline the experience for consumers; fewer reviews on the back end translate to greater approval rates on the front end. This minimizes disruption without compromising the quality of fraud prevention.

Making Remote Security a Reality

For many financial institutions, operations now look different. Workforces are distributed, and payment activity is digitized, traveling across a myriad of network points instead of a few physical servers. Yet this doesn’t need to mean decreased service or security. With the right cloud technology in place, it is possible for FIs to not only ensure reliable connectivity but actually improve overall performance.

The ability to scale capabilities as needed means that you never need to fear a system overload — or pay for excessive storage. Machine learning can then be applied to strengthen fraud detection, without draining resources. High-quality, secure remote payments need not be a remote possibility.

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