Bahaa Abdul Hussein is a Fintech expert and shares his experiences with his audience through his blogs.

The financial services sector is under the constant threat of fraud, which is unlikely to end. During the COVID-19 pandemic alone, this sector witnessed a 24% surge in fraud. Financial institutions (FIs) are pressurized to find the right solution to reduce this threat. The struggle is challenging as the FIs have to balance making real use of their data while remaining completely compliant with regulators to avoid major fines.

In the first half of 2022, global FIs were fined over £186,208,915 (USD $224 million) for breaching anti-money laundering (AML) compliance. To avoid such fines, many FIs may fear making full use of all possible data analytics available.

Using technology in the fraud battle

FIs have adopted technological innovations like Artificial intelligence (AI) to analyze a tremendous amount of transactional data, remove false alerts, and identify potential criminal activity that normally evades humans. However, these technologies do not deter financial criminals who are always one step ahead. In addition, the deterrent faced by the FIs is the strict regulatory rules which hinder the way they can make effective use of data to monitor and adopt decisions.

Currently, the financial services sector is able to glimpse only a fraction of their customers’ financial dealings, making it difficult to identify questionable patterns and potential criminal activity.  FIs need a way to meet all regulatory requirements while continuing to use their data more. This would enable them to catch fraudulent activities and other potentially nefarious financial dealings.

Confidentiality computing

FIs need to enhance and improve their fraud-detection models. Machine-learning-based fraud detection models largely rely on synthetic data and do not use much of the internal and external data available fearing going against strict regulations. A solution to this could be confidential computing and its main feature, the trusted execution environment (TEE), which is a secure area within a central processing unit (CPU).

FIs could use confidential machine learning within a TEE to use real customer data when testing their models, improving the rate of identifying fraud while remaining within all regulations of privacy. This would also significantly cut down costs as no expensive external datasets have to be purchased.

In addition, a secure TEE provides a recognized trust via remote attestation. This feature provides many service providers the confidence to share customer data with others. There is also no risk of going against regulations, and organizations can be ensured that data will be invisible to other providers.

This remote attestation feature in confidential computing is critical as it creates a new path of digital trust and helps fight financial crimes.

No more data dilemma

Confidential computing is a solution that provides FIs the ability to fully utilize the data available while avoiding the damaging consequences of a major regulatory fine. Now is the time for FIs to deploy confidential computing and upgrade their financial crime monitoring models.

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