Devin Partida is the Editor-in-Chief of ReHack. As a writer, her work has been featured in Inc., VentureBeat, Entrepreneur, Lifewire, The Muse, MakeUseOf, and others.
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Artificial intelligence (AI) is one of the most promising but uniquely concerning technologies in fintech today. Now that DeepSeek has sent shockwaves throughout the AI space, its specific possibilities and pitfalls demand attention.
While ChatGPT took generative AI into the mainstream in 2022, DeepSeek brought it to new heights when its DeepSeek-R1 model launched in 2025.
The algorithm is open-source and free but has performed to a similar standard as paid proprietary alternatives. As such, it’s a tempting business opportunity for fintech companies hoping to capitalize on AI, but it also presents some ethical questions.
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- DeepSeek’s R1 Model Sparks Debate on the Future of AI Development
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Data Privacy
As with many AI applications, data privacy is a concern. Large language models (LLMs) like DeepSeek require a substantial amount of information, and in a sector like fintech, much of this data may be sensitive.
DeepSeek has the added complication of being a Chinese company. China’s government can access all information on Chinese-owned data centers or request data from companies within the country. Consequently, the model may present risks related to foreign espionage and propaganda.
Third-party data breaches are another concern. DeepSeek has already suffered a leak exposing over 1 million records, which may cast doubt over the AI tools’ security.
AI Bias
Machine learning models like DeepSeek are prone to bias. Because AI models are so adept at spotting and learning from subtle patterns that humans may miss, they can adopt unconscious prejudices from their training data. As they learn from this slanted information, they can perpetuate and worsen issues of inequality.
Such fears are particularly prominent in finance. Because financial institutions have historically withheld opportunities from minorities, much of their historical data showcases significant bias. Training DeepSeek on these datasets could lead to further biased actions like AI denying loans or mortgages based on someone’s ethnicity rather than creditworthiness.
Consumer Trust
As AI-related issues have populated headlines, the general public has become increasingly suspicious of these services. That could lead to an erosion of trust between a fintech business and its clientele if it doesn’t transparently manage these concerns.
DeepSeek may face a unique barrier here. The company reportedly built its model for just $6 million and, as a fast-growing Chinese company, may remind people of the privacy concerns that affected TikTok. The public may not be enthusiastic about trusting a low-budget, quickly developed AI model with their data, especially when the Chinese government may have some influence.
How to Ensure Safe and Ethical DeepSeek Deployment
These ethical considerations do not mean fintech firms can’t use DeepSeek safely, but they do emphasize the importance of careful implementation. Organizations can deploy DeepSeek ethically and securely by adhering to these best practices.
Run DeepSeek on Local Servers
One of the most important steps is to run the AI tool on domestic data centers. While DeepSeek is a Chinese company, its model weights are open, making it possible to run on U.S. servers and mitigate concerns about privacy breaches from the Chinese government.
However, not all data centers are equally reliable. Ideally, fintech businesses would host DeepSeek on their own hardware. When that’s not feasible, leadership should choose a host carefully, only partnering with those with high uptime assurance and security standards such as ISO 27001 and NIST 800-53.
Minimize Access to Sensitive Data
When building a DeepSeek-based application, fintech firms should consider the kinds of data the model can access. The AI should only be able to access what it needs to perform its function. Scrubbing accessible data of any unneeded personally identifiable information (PII) is also ideal.
When DeepSeek holds fewer sensitive details, any breach will be less impactful. Minimizing PII collection is also key to remaining compliant with laws like the General Data Protection Regulation (GDPR) and the Gramm-Leach-Bliley Act (GLBA).
Implement Cybersecurity Controls
Regulations like the GDPR and GLBA also typically mandate protective measures to prevent breaches in the first place. Even outside of such legislation, DeepSeek’s history with leaks highlights the need for additional security safeguards.
At a minimum, fintechs should encrypt all AI-accessible data at rest and in transit. Regular penetration testing to find and fix vulnerabilities is also ideal.
Fintech organizations should also consider automated monitoring of their DeepSeek applications, as such automation saves $2.2 million in breach costs on average, thanks to faster, more effective responses.
Audit and Monitor All AI Applications
Even after following these steps, it’s crucial to remain vigilant. Audit the DeepSeek-based application before deploying it to look for signs of bias or security vulnerabilities. Remember that some issues may not be noticeable at first, so ongoing review is necessary.
Create a dedicated task force to monitor the AI solution’s results and ensure it remains ethical and compliant with any regulations. It’s best to be transparent with customers about this practice, too. The reassurance can help build trust in an otherwise dubious field.
Fintech Companies Must Consider AI Ethics
Fintech data is particularly sensitive, so all organizations in this sector must take data-reliant tools like AI seriously. DeepSeek can be a promising business resource, but only if its usage follows strict ethics and security guidelines.
Once fintech leaders understand the need for such care, they can ensure their DeepSeek investments and other AI projects remain safe and fair.
