Traditionally, data security has had a big hole, and confidential computing fills this gap in a way that can potentially help financial institutions reduce their risk of money laundering and digital fraud. This is especially compelling to financial institutions that use and process an immense amount of data.
This guide explains the basics of confidential computing. Then, it looks at a few ways financial institutions can utilize confidential computing to improve the customer experience and minimize their fraud risk.
What Is Confidential Computing?
To understand confidential computing, you need to understand the basics of data. Data has three different states: in transit, at rest, and in use. It’s in transit when being sent, at rest when being stored, and in use when being processed. While in-transit and at-rest data can easily be encrypted, in-use data can’t be.
Confidential computing addresses this risk. It secures a portion of a computer’s processor and memory to create a protected and isolated environment for in-use data. Called a trusted execution environment (TEE), the TEE reduces the risk of the data being seen by an unauthorized person or program. This is especially compelling to financial institutions that are constantly processing a lot of data.
Confidential Computing to Improve the Customer Experience
Confidential computing can allow financial instructions and other businesses to share information and insights without disrupting client privacy. Banks have to offer personalized services to their clients if they want to stay relevant, and if they leverage confidential computing to facilitate multi-party dataset sharing, they can learn more about their clients.
For instance, if banks and retailers collaborate on a data set, they will see different information when a consumer makes a purchase. The bank that issued the payment card will see a horizontal data set that shows where the customer shopped. But the bank won’t be able to see the details of the transactions.
Retailers, in contrast, will see a vertical dataset with deep information about the purchases made at their stores. But they will not be able to see where else the consumer used that card.
Financial institutions can use this information to personalize the recommendations they give to clients, improving the customer journey. Retailers can do the same with their information. However, confidential computing ensures that no data is compromised or misused.
Confidential Computing to Learn About Fraud Patterns
Financial institutions can also use confidential computing to learn more about fraud patterns. Just as in the above example, this technology allows financial institutions to share information without compromising the privacy or integrity of their data. However, in this case, the data is being used to identify fraud patterns.
Using this technology, teams from different financial institutions can work together to create prediction models. They all share information about transactions from their financial institutions. Then, the software looks at multiple legitimate and fraudulent transactions, and over time, it learns how to tell the difference. Then, it creates fraud detection models that can be utilized by multiple financial institutions.
Confidential computing also improves its approach over time. This boosts accuracy and reduces false positives. The more data the system has, the better it becomes.
Traditionally, machine learning required data to be centralized, but confidential computing allows the data to be kept on the bank’s internal systems. This setup lets banks share information without exposing their raw data. This process utilizes federated learning which means that the data is kept in local environments. It doesn’t need to be stored in the cloud as data usually does for collaboration to be possible.
Fighting Money Laundering and Digital Fraud
Historically, financial institutions have been reluctant to work together because they don’t want to give up their proprietary data, but they also lacked the tools to collaborate effectively while safeguarding privacy. The reluctance coupled with a lack of tools reduced the industry’s ability to fight fraud.
By allowing financial institutions to learn from each other, confidential computing improves banks’ abilities to learn about money laundering and other digital fraud schemes. Using their shared experiences, financial institutions can come up with more effective algorithms to detect fraud.
Regulatory Compliance and Confidential Computing
As financial institutions foray into digital offerings including confidential computing, they often work with a variety of third parties. To ensure their regulatory compliance, banks need to choose their partners carefully. They need to ensure that their partners’ practices stand up to the relevant regulations.
At its core, confidential computing helps banks maintain their regulatory compliance. But before choosing a partner, you should do your due diligence to make sure they understand the unique rules and nuances of working with the banking industry.
At SQN Banking Systems, we focus on fraud so that our clients can focus on running their financial institutions. We always stay abreast of the latest trends and technologies in fraud prevention and detection, and we use that knowledge to help protect our clients.
Want to enhance your anti-fraud measures? Then, contact us today. We offer a variety of tools and solutions to help financial institutions prevent, detect, and fight fraud. Let’s start with a conversation to learn more about your anti-fraud efforts, and then, we’ll help you move forward.