Beyond Digital, Resilience Empowers AI-Infused Finance

Beyond Digital, AI will be Infused into All Financial Scenarios

AI is really accelerating financial AI inclusion.

Now, the LLM become low-cost and small size, which means AI is no longer exclusive to leading banks. Small and medium-sized banks can leverage AI effectively.

And the open-source LLM allows every bank can have its own model, resolves three problems: industry scenario adaptation, self-controllability, and sensitive data must be stored locally.

The chain of thought technology provides transparent traceability of AI decision-making processes. This meets regulatory and audit requirements, making AI not only a tool for tasks but also a trusted assistant in the core business of banking.

What will the future of banking look like? We believe every bank will have its own AI agent and two types of employees. Each bank will develop its own AI agent. Models and data will become the most critical assets for every bank.

In the future, banks will only need two types of employees: AI employees and AI-empowered employees. With the support of AI agents, each employee can handle more complex tasks efficiently.

AI will infuse every aspect of banking, from user devices and channels to marketing, risk control, and R&D. The way banks operate and serve customers will fundamentally change.

• AI-Powered Devices Redefining User Experience

LLM Inference accelerates embedded in phone. For example, in mobile apps, AI agents can now handle money transfers in just 2 steps instead of 10. It’s a huge leap for user experience!

• AI Takes Marketing to the Next Level with Hyper-Personalized Services

AI is transforming financial marketing. With AI, we can precisely target customers, tailor services, and choose the perfect timing for outreach. This creates hyper-personalized experiences. The account managers can now handle over 400 clients each, up from less than 100. This has significantly boosted conversion rates.

• AI Powers Smarter Decisions and Real-Time Risk Management

AI is completely changing how we handle risk control as well. With its thought chain capability, it can solves the challenge of auditability, making decisions transparent and traceable. Combined with graph engines, AI enables real-time fraud detection, making financial systems safer and more reliable.

AI Unlocks Opportunities, But Also Bring Challenges

AI is growing fast, bringing big chances but also big challenges for infrastructure. One of the biggest risk from AI is security. Hackers using AI can make viruses harder to spot, change faster, and attack more often. This makes stopping them much tougher. With AI entering mobile devices, the users of apps will shift from humans to AI agents. This will increase performance demands a thousandfold, impacting wide-area networks, and data centers. AI in risk control needs to work 24/7 to protect real-time transactions. This means AI inference needs dual or multi-active setups, reshaping infrastructure. It puts huge pressure on AI hardware, software, and inter-data center networks.

At last year’s MWC, we set a bold vision for the future of financial infrastructure: the 4 Zeros. These are: Zero Trust Security, Zero Wait Experience, Zero Downtime Availability, and Zero Touch Operation.

This year, we’re introducing the R-A-A-S framework to achieve the 4 Zeros. It includes Reliability , Availability , AIOps, and Security architectures.

According the principle of the design, the reliability is very important, its target is RPO equal zero. How we can approach it?

In terms of Active – Active Data center, We believe using three key technologies, banks should design multiple copies protection of storage, keep real-time synchronization of data, and the most importance is multiple technology collaboration among storage, network, and computing.

Second one is Availability with 99.999%. How we can achieve it?

First of all, we should pay more attention on cell-based cluster design, for Database and micro service. In addition, we should focus on unified multi-active services platform.

The third one is autonomous operation, we put forward to 0-1-5-10 as its target. What’s the mean of this? It means 0 human error, 1 mins discovery, 5 mins location the issue and 10 mins recovery.

Across network-security-cloud and across multiple vendors, to build a unified AIOps system to manage the whole infrastructures. By using digital twins technologies to simulate the network to escape the issue in advance, using AI technology with the indicator library, fault mode library and Repair script library, to quickly troubleshoot and recover fault.

The last one is security. Fast blocking response is very important, it should be form mins to seconds. Three aspects of technologies should be take into account for E2E Security of the whole network.

The collaboration across domain is very important. Using secure endpoint, branch firewall and datacenter firewall to build terminal–edge-cloud collaborative protection, and in datacenter, build cloud-network-storage collaborative protection, then an in-depth security management system is necessary, which can coordinate command in a unified manner, aggregates information from all defense lines, and delivers security policies to each defense line. Finally, we should emphasize that we also have to use AI to fight AI, detecting viruses to block it in advance.

‘Beyond Digital, Resilience Empowers AI-Infused Finance. ‘ Huawei provides computing, storage, network, cloud, and AI products and solutions, provides R-A-A-S architectures design, and is willing to help global financial customers build a solid digital foundation, accelerate customers move forward to AI-infused Finance.

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