Fintech firms are understandably wary of outsourcing data processing, but security measures exist to mitigate risk and maximize workflow efficiency.

Developing a secure fintech solution can be a complicated, time-consuming, and expensive endeavor, especially when factoring in the lofty demands of data security and confidentiality. For data-intensive projects in the finance space, outsourcing presents exciting opportunities for working around these challenges, but it is not without its caveats.

Ensuring the confidentiality, integrity, and availability of your data during the collaboration is of critical importance, which is why fintech companies must choose their outsourcing partners with the utmost care.

For example, the development of customer data platforms (CDPs) may involve highly sensitive data that is subject to stringent regulatory requirements. Training an AI model also involves vast amounts of data, and while AI training data is often anonymized, any closed-source data set constitutes valuable intellectual property that no company wants to put at risk.

Why Should Fintech Companies Outsource Data Processing?

Outsourcing tends to be the popular choice for time-consuming, data-intensive projects, such as building CDPs or AI training data sets. These tasks are notoriously difficult to scale when relying solely on an in-house team. Trying to manage everything in-house often leads to longer project delivery times, and it places a major burden on employees.

Whether or not outsourcing is a good or bad choice for a fintech company largely depends on the outsourcing partner, the engagement model they use, and the willingness of both parties to work together to achieve the best results. Working with the right partner, however, delivers a significant competitive advantage and a long list of other benefits, such as access to domain-specific knowledge and skills, reduced costs, and shorter cycle times. All the while, in-house teams can focus on more rewarding and impactful top-level business goals.

The Problem with Traditional Outsourcing Models

There are many ways to outsource data-processing and related projects. One of the cheapest options is crowdsourcing, but this is also the least secure. The problem with crowdsourcing is that there is minimal accountability and visibility into the workforce. In other words, companies rarely even know who they are actually working with, which naturally makes it near impossible to ensure high standards of data security.

Another popular option is outsourcing to gig workers, typically via popular platforms like Fiverr or Upwork. However, this approach is largely irrelevant for data-intensive projects, since they typically require a large workforce. While it might be possible to build a team of gig workers, all of whom have been carefully vetted, the burden on administration can be substantial, which makes it easy to lose sight over who has access to the data and which controls are in place to protect it.

A safer option is the traditional business process outsourcing (BPO) model but again, it is easy to end up losing sight of exactly who has access to the data and how it is being protected, especially in situations with high worker turn-over. While a reputable BPO should be able to give some guarantees regarding confidentiality and security, there is often a real risk of a lack of alignment between both parties.

Key Ingredients of a Secure Outsourcing Arrangement

The most effective collaborations are those in which there is a close alignment between your team and the outsourced workforce, especially in the case of financial data, where the stakes are higher than most. In other words, an outsourced workforce should serve as an extension of your team, augmenting their capabilities rather than replacing them. This approach provides a far greater degree of control and transparency, thus allowing you to ensure that all the key requirements of a secure outsourcing arrangement are met.

Your outsourcing partner should also be willing to sign a non-disclosure agreement (NDA) that legally prohibits them from disclosing your data to any third parties without prior permission. It must cover all potentially sensitive data, such as that which is subject to regulatory standards and any intellectual property.

While an NDA serves as a valuable starting point, it will not mean much without the necessary means of enforcement. In particular, you should be able to retain full visibility into your data and who has access to it. Having a unified workspace where you can track each worker’s activities and access to your data can help a great deal.

Data security begins with the individual, while technology provides the tools necessary for enforcing the rules and monitoring user activities. These include solutions such as data loss prevention (DLP), encryption of data in storage or in transit, and complete audit trails of every user login attempt and activity.

Chances are, these are similar to the controls and mechanisms you use to secure your own workforce, and there is no reason not to apply the same to your outsourced workforce too.

How Can a Managed Workforce Help Ensure Security?

A managed workforce presents an optimal blend of outsourcing and managed services to offer clients maximum transparency and control over their projects—much like when working with an in-house team. At the same time, it offers a high degree of scalability and flexibility without compromising on confidentiality and data security.

Our solution includes a Workforce Management Platform where your internal teams can collaborate directly with their managed workforces. This secure environment gives administrators full visibility into their data, including who has access to it, which is something that traditional approaches to outsourcing simply do not allow. For fintech companies dealing with highly sensitive data and valuable intellectual property, this advantage presents a value that is too important to ignore.

Our managed workforce offers maximum transparency and accountability for complete peace of mind when scaling financial data processing. Learn more about our Data Processing Services.

Crowd vs. Managed Team: A Study on Quality Data Processing at Scale

Data Partners Data Security Finance and Insurance AI & Machine Learning

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