"FINSMART: Pioneering a New Era of Autonomous AI in Finance with DeepSeek"
While global AI technology waving in the financial industry, FINSMART has responded with a groundbreaking innovation. Today, FINSMART officially announced the successful deployment of a domestic open-source large model, DeepSeek, which available to fully replace third-party services such as OpenAI. This milestone represents a significant breakthrough for FINSMART in the field of financial AI and also marches toward safer and more efficient stages for its four core AI-empowered financial business scenarios. How FINSMART utilizes the power of DeepSeek to reshape the Fintech landscape in this huge transformation? Let's take a closer look!
___________________________________________________________________
1. Breakthrough “Bottleneck”: From Dependency to Autonomy, DeekSeek reshapes the foundation of financial AI
In the process of digital transformation of the financial industry, AI has confronted two major weaknesses: 1. Reliance on the interfaces, raising concerns about data security and model controllability; 2. Inability of general-purpose models to fulfill the stringent requirements for accuracy and compliance in financial scenarios. FINSMART CAO, First Chen, stated, “In the past, when using AI’s online services, we were unable to customize model logic, and response delays along with cost pressure incurs bottleneck situation for business expansion. For financial institutions intends to customize its professional models, the investment required was astronomical."
However, the arrival of the DeepSeek open-source large model has completely transformed this landscape. DeepSeek supports localized deployment for enterprises and opens up its technology roadmap. Leveraging its million-level financial database, FINSMART has created a domain-specific “FIN-DeepSeek” model through specialized training. According to actual test data, this model has achieved a 37% improvement in accuracy for tasks such as financial text comprehension and numerical reasoning compared to general-purpose models, with a 50% increase in reasoning efficiency, while completely ensures data stays within the domain and enables autonomous model iteration.
2. Four Scenarios: How AI Makes Financial Business "Intelligent and Compliant"?
1.AI Financial News Generation: From 1 Day to 1 Minute, Dynamically Tracking Market Trends
Confronting the rapidly changing global market, FINSMART’s AI engine relies on the DeepSeek large model, capable to real-time capturing vast amounts of information such as financial reports, policies, and public sentiment, automatically generating bilingual financial news in Chinese and English. Currently, it produces over 3,000 articles daily for the Hong Kong and U.S. stock markets. Leveraging the robust reasoning capabilities of the large model, it effortlessly handles tasks like news filtering and pushing, providing a 24/7 news service throughout the year.
2.AI Research Report Analysis and Integration: Penetrating Hundreds of Pages to Extract Investment Signals
To address the challenge of "information overload" in brokerage research reports, FINSMART has developed a report analysis system that can deconstruct unstructured documents with one click, extracting core viewpoints, financial forecasts, risk alerts, and generating visual comparison charts. A leader from a private quantitative team noted, “The system can automatically identify valuation discrepancies from different institutions on the same asset, helping us quickly pinpoint market expectation differences.”
3.AI Customer Service and Investment Advisory Robot: A “Financial Assistant” with humanity
With the enhancement of the large model’s multi-turn dialogue capability, FINSMART’s intelligent investment advisory robot can now understand vague customer expressions (such as “seeking stability but wanting to outpace inflation”), relate to historical portfolio data, and interpret sudden market events in real-time. Pilot data shows that the customer issue resolution rate for AI advisory has increased from 68% to 92%, with the average response time reduced to 1.2 seconds. Additionally, FINSMART can resolve over 95% of customer inquiries in the customer service system by training on the private domain data of financial companies.
4.AI Financial Compliance Assistant: Reducing Costs and Improving Efficiency in Complex Compliance
By embedding thousands of regulatory rules into the DeepSeek reasoning framework, the FINSMART compliance system can scan transaction records, customer service dialogues, and other content in real time to automatically identify suspicious activities such as money laundering and misleading sales. During a trial run at a certain institution, the system preemptively intercepted three high-risk transactions and generated compliance rectification reports, helping the institution avoid potential fines amounting to millions.
3. Industry Insights: Open-source Large Models Are Rewriting the Rules of FinTech
A report by the renowned consulting firm iResearch Consulting points out that by 2025, the penetration rate of large models in the financial industry will exceed 40%, with open-source models expected to account for 65%. FINSMART’s transformation undoubtedly aligns with this trend, by mastering model autonomy, the company can avoid API interruption risks caused by geopolitical issues while optimizing for specific business scenarios.
FINSMART CEO Tom Luo commented on this, “FINSMART’s practice demonstrate the explosive potential of the open-source ecosystem. When financial institutions transition from being users of AI services to co-creators of models, a safer and more inclusive financial AI ecosystem is emerging.”
___________________________________________________________________
[Conclusion]
With the continuous deepening of the four AI scenarios, FINSMART is steadily moving towards the vision of “making intelligent finance accessible to everyone.” Perhaps in the near future, this technological revolution that began with the replacement of large models will redefine every aspect of wealth enhancement.
