The arrival of DeepSeek has sparked a transformative wave in the financial technology landscape, capturing the interest of numerous banks and fintech companies eager to integrate this new player into their operationsWith a robust background in quantitative finance, DeepSeek appears to be establishing itself as a significant force within the financial sector, as various financial institutions announce their decisions to join its ecosystemHowever, reactions are mixed; while some entities see this as an opportunity, others, particularly those who have already bet on self-developed AI technologies, are left grappling with uncertainty.

For smaller banks and financial institutions, the integration with DeepSeek represents a low-risk avenue into the AI arenaThey no longer face the daunting financial risks typically associated with heavy investments in proprietary AI development, allowing them to adopt DeepSeek’s solutions with relative easeIn contrast, firms that have previously invested heavily into their AI initiatives now face the abrupt reality that the industry has shifted overnightThe competitive landscape has changed, and their once-unique technological advantages may be significantly diluted.

Reports circulate that companies like Zhaoshang Consumption Finance and Citic Consumer Finance are actively pursuing partnerships with DeepSeekThis creates a rather awkward predicament for these firmsOn one hand, not joining in with DeepSeek could leave them behind in this rapidly evolving landscapeOn the other hand, adopting DeepSeek’s technology could undermine the exclusivity of their own AI models—developed painstakingly over timeAs the tide of modern technology sweeps through the financial sector, seeking adoption of DeepSeek’s platforms may be less about innovation and more about survival amidst changing market expectations.

The impact of integrating DeepSeek resembles the hypothetical scenario in which a tech giant, akin to Elon Musk, announces an unprecedented open-source development program for aerospace technologies—forking out barriers and democratizing access like never before

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In this new era, the financial sector cannot afford to lag behindDeepSeek lessens the entry barriers for small and medium-sized banks wanting to develop niche applications based on its platform, making advanced fintech solutions accessible even to those with limited resourcesTasks formerly reserved for expert teams become achievable for general business personnel, akin to turning esoteric knowledge of ancient martial arts into guidelines for everyday street vendors.

As for the capabilities that DeepSeek offers, initial experiments show encouraging resultsFor instance, SuShang Bank has optimized its fraud detection models through the integration of DeepSeek’s inference models, achieving a remarkable 35% increase in the accuracy of fraud risk identificationSuch practical examples provide compelling evidence that the benefits of DeepSeek’s technology will soon inspire many other banks to follow suit.

However, the flip side of this technological embrace means challenges will arise, especially for those technology-driven financial platforms that focus on empowermentClient banks may begin to manage their AI projects autonomously, leading to potential customer attrition for these empowering service providers down the lineFor example, with Masang Finance having invested heavily in the development of its proprietary AI model known as Tianjing, its primary selling point—the exceptional AI capabilities—might become less noteworthy as the industry rapidly converges around DeepSeek.

Compounded upon this dilemma is the question of how Masang Finance should approach selling their solutions to small and medium-sized banks in the wake of DeepSeek’s emergenceWill price discounts be necessary to attract customers seeking cost-effectiveness? This becomes a critical yet non-trivial consideration for them moving forward.

Early movers in the AI field, such as Masang Finance, established their artificial intelligence research units as early as 2017. By December of last year, they unveiled the Tianjing 2.0 model, claiming to service over 200 million users across eight application scenarios

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However, the arrival of DeepSeek disrupts this pictureIt brings an unsettling question: Should smaller banks and financial institutions choose to adopt a private deployment of DeepSeek for certain service scenarios, can Masang Finance still safeguard that impressive user base and diverse application landscape?

It seems plausible that a significant portion of users may be diverted, especially considering forecasts suggesting that by 2026, up to 30% of consumer finance decisions may be made locally on edge computing devices with response delays reduced to below 80 millisecondsThis could lead to substantial market shifts affecting user allocations across various services.

As the market landscape evolves, the presence of DeepSeek—promising more convenient and interaction-rich private deployments—could weaken the rationale for choosing Tianjing among banks and financial institutionsFrom a banking perspective, the rationale is quite straightforward: When more cost-effective solutions are available, the motivation to invest in higher-priced alternatives diminishes, unless those solutions can markedly outperform.

This shifting dynamic compels Masang Finance to rethink its strategyThe unique advantages that Tianjing once held may no longer suffice; it now faces the stark reality of needing to establish new selling points around differentiation to attract payment users.

As this competition unfolds, operational challenges arise for Masang Finance as wellWith a potential dilution of the 200 million user scale, there are looming concerns about sustaining that impressive figureSuch logistical variability is underscored by similar trends that have already manifested in customer engagement and interactions on the consumer-facing sideA report by QuestMobile indicated a decline in daily active users for Kimi, a subsidiary of "The Dark Side of the Moon," which now trails behind DeepSeek and DouBao.

This serves as a clear indication that Kimi’s user base is being proportionately affected by DeepSeek’s aggressive market penetration

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Of course, Kimi is not alone; many consumer-facing applications, including those tailored for financial services, are likely experiencing similar redistributions of users as DeepSeek escalates its visibility and appealWhile the effects may not be as pronounced in the B2B domain as in the B2C realm, it's nearly a certainty that segmentation across various application scenarios will see impacts.

The real concern for Masang Finance lies in whether the customer segments that would traditionally engage with the Tianjing model will fragment as users migrate to DeepSeekAfter all the resources and capital invested, will they see a return on investment? This financial scrutiny transforms the narrative of the AI development landscape into a question of resource allocation and outcome justification.

As DeepSeek arrives, the financial technology landscape pivots sharply towards cost-efficiency and productivityHowever, with this comes a caveat: can financial institutions and banks realize profitability when integrating this new technology? Transitioning along this trajectory may take time even for DeepSeek to demonstrate scale effects born from its cost reductions.

Many existing B2C platforms implementing DeepSeek report losses, with major players such as Tencent and Huawei experiencing monthly deficits exceeding 400 million RMB post-adoptionIntuitively, one might assume that B2C models would have an easier path to achieving scale compared to B2B modelsYet, if tech giants struggle under the current market pressure, the question becomes even more pressing for smaller fintech platforms like Masang Finance, which must navigate toward profitability.

The overall situation presents an interesting paradox; firms either capitalize on traditional lending businesses or risk significant operational vulnerabilitiesFor example, financial reports indicate that in the first half of last year, Masang Finance’s lending segment generated around 7.7 billion RMB in revenue with a net profit of 1.068 billion RMB, largely driven by proprietary lending activities

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