Industry Insight: In-depth Insight into the Application of AI + Financial Industry Segmented Business Scenarios

Look at the market: FinTech development enters a golden age, AI market scale rapidly rising

Global Artificial Intelligence in FinTech Market Size Expected to Climb to $14.41 Billion by 2024, benefiting from the in-depth application of intelligent risk control, algorithmic trading and customer service, will expand at a CAGR of 27.1% over the next five years, underscoring the accelerating drive of AI technology for the digital transformation of the financial industry.

Figure 1: Global Market Size for AI in FinTech, 2021-2031

See the industry: fintechs and banks lead the practice of AI in finance

Figure 2: The practice of applying big models in various industries in the financial field

▪ Insurance: head to explore AI applications
In the core business segment, most insurance organizations are in the initial exploration stage of the application of big models, and the big model products of individual head organizations have already taken the shape of Al Agent.

▪ Securities: experimenting with non-decision-making
Focus on relatively simple and non-decision-making aspects of business processes. Head securities organizations are beginning to experiment with big model applications in areas such as wealth management, investment research middle and back office, etc.

▪ Banks: steady growth and expanding applications
The application of big models in the banking industry mainly focuses on two major areas: one is to serve the optimization and upgrading of internal operation and management, and the other is to help reshape and expand external business scenarios. Driven by the need to find new growth points and improve operational efficiency, the strategic core of digital transformation of banks is increasingly focusing on the model of "data + algorithm".

▪ Fintech companies: iterative solutions for total empowerment
Relying on the strong technical strength of big models, financial technology companies have completed the iteration of application solutions from their own business, and the value of industry empowerment has gradually emerged.

Looking at banks: oriented towards internal operations management and reshaping external business

The development of financial AI applications in China is in the policy dividend period, and most banks are still in the stage of technological reserves and shallow experimentation.Currently, banks mostly apply AI technology to empower and reduce the burden of employees as well as to improve customer experience, and some head banks gradually cover AGI technology into the core business links of the bank.

Figure 3: Core scenarios of AI applications in the banking industry

Due to differences in technological foundation and operational focus, all types of banks show differentiated development paths in the AI application field, with state-owned banks, joint-stock banks and small and medium-sized banks in thestrategic center of gravity,Scenario Landingup toTechnology deploymentThere is a focus on each.

state-owned bank

Full Stack Technology Leads Deep Intelligence

state-owned bankFocus on serving national strategies(e.g., rural revitalization and technological self-reliance), promote the substitution of domestically produced technologies, and strengthen the industry-leading role of the full-stack technology system, while balancing risk control and business innovation.

► Scene landing:AI Deep PenetrationCredit approval,Intelligent Risk Control,Intelligent Customer ServiceThe core business, such as risk assessment accuracy and approval efficiency, and extends to thecross-border finance,green financeand other national strategic areas.

► Technology Deployment:With the full-stack self-research system as the core, complete thePrivate deployment of large models for hundreds of billions of dollarsand building an autonomous and controllable technical architecture based on localized arithmetic infrastructure.Emphasis on data security and privacy protection.

joint-stock bank
Scenario Innovation Drives Efficiency Breakthrough

joint-stock bankCompetitive differentiation orientedIt focuses on its strengths in retail banking and wealth management, replaces manual operations through an "intensification + intelligence" strategy, and optimizes resource allocation to cope with the pressure on profitability.

► Scene landing:Focused optimizationOperational Processestogether withPrecision marketingcapabilities, improve product conversion rates through customer behavioral data analysis, and exploresmart investment bankingand other emerging scenarios.

► Technology Deployment:Adoption of lightweight models and open source ecologyIt is also a key component of the company's business strategy, which combines cloud computing to enable agile development, reduce deployment costs to the millions, and shorten technology iteration cycles with code generation tools.

small or medium-sized bank
Open Source Ecology Enables Low-Cost Transformation

small or medium-sized bankAdoption of the "small steps" strategyIn order to optimize the internal management process and then extend the service to the customers, the company will take advantage of the geographical data advantage to compete in a wrong position and avoid direct confrontation with the large banks.

► Scene landing:deep plowinginclusive financetogether withLocalization Services, such as dynamic credit scoring to cover microfinance approvals, as well as the development of differentiated tools for characteristic scenarios such as the county economy and the agricultural industry chain.

► Technology Deployment:Fast fine-tuning with open source modelsIn addition, the company has been able to land intelligent customer service, contract auditing and other scenarios at a low cost with 100,000-level samples, and break through the technical shortcomings by building an ecological alliance with technology companies and local governments.

As we can see.state-owned bankFocusing on AI applications in national strategic fields, emphasizing full-stack self-research, autonomous control and risk control..;Joint-stock banks have adopted lightweight open source around efficiency improvement, pursuing cost reduction and differentiated innovation; small and medium-sized banksDeeply cultivate local characteristics and rely on eco-cooperation to break through the universal market at low cost.At present, the AI application field has gradually formed a differentiated layout and synergistic evolution situation, the overall application from a single point of tool embedded in the "data-scene-ecology" synergistic leap, AI technology has gradually become a strategic infrastructure to reshape the competitive landscape of the financial industry.

The application of AI technology, based on data-driven and intelligent interactions, realizes traditional businessBusiness Process Re-engineeringand customersService Experience Enhancement; In addition, AI transforms fragmented business experience into a structured knowledge base, providing strong empowerment for business operations.

Figure 4: AI empowers financial business scenarios to improve efficiency

Below, we further focus on the application of AI in business operations. We can see that AI has achieved intelligent penetration in all aspects of customer service, and it is driving the financial industry toLeapfrogging from "experience-driven" to "data-intelligent", guiding the evolution of customer service towards scenario adaption and closed-loop value creation.

01 Customer Service: Multimodal Interaction and Accurate Response

AI has taken shape in the customer service segmentMulti-modal, omni-channel intelligent systemThe current technology is upgrading from "passive response" to "active prediction", gradually promoting AI customer service to the "decision center" transformation.

■ In terms of business answering:Not only can it achieve 7×24 hours online response, but also assist in handling account inquiries, loan applications and other 80% standardized services with the help of thedigital personSimulated expressions and speech enhance the intimacy of interaction.

■ Other aspects:Based on capabilities such as data integration and analysis, it can realize scenarios such as accurate recommendation of financial products, real-time detection of fraudulent transactions and compliance risks, and cover process automation in scenarios such as contract review and mail classification.

Case: ICBC - Intelligent Customer Service Assistant

ICBC realizes service upgrading through the dual engines of "Gong Xiao Zhi" and "Gong Xiao Hui".▪ Customer service end:Relying on the three-layer technical architecture of "expert rules + small model + large model", "Gong Xiaozhi" realizes in-depth analysis and prediction of customer needs, and the intelligent outbound call system covers 110 channel contacts;▪ Employee empowerment end:Through the three modules of knowledge assistant, training assistant and service assistant, "Gong Xiaohui" shortens the training cycle of new employees and realizes the whole process of human-machine cooperative work.

Figure 5: Introduction to ICBC Intelligent Customer Service Assistant

02 Wealth Management: Personalized Investment and Lifecycle Planning

Currently AI is starting to take shape in the wealth management spaceIntelligent penetration of the whole chainThe company's service model has evolved from "standardization" to "ultra-personalization". Through the generative model and machine learning technology to achieve dynamic optimization of asset allocation, real-time response to risk early warning and accurate insight into customer needs, AI is deeply integrated into theInvestment research analysis, strategy generation, compliance reviewand other links, while improving decision-making efficiency and risk control accuracy, lowering the service threshold to promote the development of inclusive.

Case: Bank of Beijing - "i Smart Match"

"iMatch" asset allocation service▪ Based onThe "4321" theoretical frameworkConstruct a full life cycle service model to cover the needs of the three major scenarios of wealth enhancement, accumulation and inheritance. ▪ Rely on the data center to tandem with account managersPre-sales, sales and after-salesThroughout the entire process, the service efficiency is enhanced through intelligent risk control models and real-time yield monitoring systems, while special financial products are integrated to achieve personalized asset allocation with the synergy of "strategy-product-channel".

Figure 6: Introduction of Bank of Beijing's "i Smart Matching".

03 Marketing Management: Precise Demand Mining and Omni-Channel Reach

Avery data show that the head of the enterprise has realized the marketing cost down 30-50%, conversion efficiency increased 2-3 times, AI is driving theThe evolution of marketing from standardization to a more granular era of "1,000 people, 1,000 faces" personalization.

◽️ Dynamic user profiles built based on big data and big modeling technology can realize accurate demand prediction;

◽️ Generative AI drives efficiency gains in full-link content production;

◽️ Intelligent algorithms optimize ad delivery to improve marketing conversion;

◽️ Full process automation covers the customer lifecycle and improves service responsiveness;

◽️ AI-enabled decision-making closure, compression of the marketing feedback cycle from weekly to minute level through real-time data monitoring, and establishment of risk warning models to avoid marketing risks.

Case 1: China Merchants Bank - "Intelligent Marketing Engine for a Billion Customers"

Relying on big data, artificial intelligence and other technologies, through the"Data+Algorithm+Scenario" Trinity Architecturecenter on (an issue)people, goods and servicesIt also provides a marketing template of "Data Center + Intelligent Engine + Open Ecology" for the same industry by providing accurate matching between customers and personalized content recommendation of "Thousands of people, thousands of faces" to achieve double improvement of customer experience and business effectiveness.

Figure 7: General Architecture of China Merchants Bank's "Intelligent Marketing Engine".

Case 2: Ping An Bank Credit Card Marketing-AI+Short Drama

Based on a data modelInsight into the characteristics of the audience for short plays(predominantly young 19-35 year olds) with a high overlap with bank credit card customers; utilizing natural language processing technologyAnalyzing Social Media HotspotsAI generates the "boss" persona, workplace dilemmas, and other elements.Script frameworkand incorporate credit card interest-free installments, overseas spending rebates, etc. in the scriptsbusiness scenario, realizing natural product implantation;AI algorithms optimize placement strategiesIt targets the high-potential user group of 20-35 years old in platforms such as Jieyin and B Station, which greatly enhances the data of short drama playback and likes.

Figure 8: Ping An Bank Credit Card AI Skit Marketing

04 Credit risk control: dynamic monitoring and proactive defense

The empowerment of AI on credit risk control work mainly realizes the leap in risk identification efficiency and accuracy through data intelligence and process automation.▪ Generalized functions:AI integrates multi-dimensional data from industry and commerce, justice, taxation, etc., to build dynamic customer profiles and realize real-time risk assessment and second response. At the same time, it combines knowledge graph and RAG technology to dynamically retrieve external knowledge base and enhance the interpretability of decision-making.▪ Risk control in lending:Real-time monitoring of capital flow, identification of funds diverted to prohibited areas, and automatic detection of contractual violations; by analyzing market fluctuations and customer operating data, early warning of supply chain risks and dynamic adjustment of credit limits.▪ Pre-loan risk control:Utilizing identity verification technology and implicit correlation analysis, the anti-fraud accuracy rate is increased to more than 99%; through automated cross-validation of financial and non-financial data, an interpretable credit assessment report is generated and dynamically matched with risk pricing strategies, significantly enhancing the transparency and efficiency of approvals.▪ Post-credit risk control:AI matches differentiated collection strategies through case grading and deploys automated tools to replace the 80% manual outbound call task; dynamically assesses the value of collateral, warns of the risk of depreciation in real time, and generates the optimal disposal plan by simulating the probability of repayment to shorten the disposal cycle.

Figure 9: Application of big models in credit operations

Industry practice shows that AI technology significantly improves risk assessment accuracy and approval efficiency, but data privacy protection, model interpretability and algorithm bias are still the main challenges. Future technological evolution will focus on multimodal fusion and the construction of a credible AI system to promote the transformation of risk control from rule-driven to cognitive interaction.

05 Physical branch services: human-machine collaboration and virtual-reality integration

The total number of bank outlets decreased by 1,455 in 2024, reflecting the fact that banks are going through a process of structural adjustment, and the application of AI in physical outlets undoubtedly provides more empowerment for daily operations.

◽️ Smart Devices:Replacement of basic services by intelligent devices, self-service processing of 80% high-frequency services, and release of manpower to shift to high-value services;

◽️ Dynamic triage:AI dynamically optimizes customer triage and waiting time management, combines real-time traffic forecasts to guide staggered processing, and improves service temperature through emotion recognition and multimodal interaction (voice, digital people);

◽️ Precision Marketing:Generate personalized product recommendations based on customer profiling and behavioral analysis, and automate monitoring of equipment status and compliance risks to reduce O&M costs.

Currently, AI applications for physical branch services have fully penetrated business processes and customer interactions, but technical stability, customer habit cultivation and staff capacity transformation remain the core challenges of the current transformation.

Case: CITIC Bank-Smart Branches

CITIC Bank throughReconstructing the whole customer lifecycle service system of customer demand insight, marketing, transaction and after-sales service in visiting outletsIn addition, it developed new mobile intelligent financial machines, introduced branch digital human lobby managers, developed a unified information dissemination platform for branches and a digital full-cycle equipment management and monitoring system, created a big-data branch layout and site selection platform, and innovated the construction of on-line cloud stores and meta-universe outlets. Smart outlets effectively reduced the leasing area of outlets by 12.5% compared with traditional outlets.

Figure 10: Introduction to CITIC Bank's Smart Outlets

 

Conclusion:A look at future trends in AI applications 

In recent years, the application of AI has shown explosive growth, with commercial banks and financial institutions joining in, and through the application of AI it has indeed brought multi-dimensional energy efficiency improvement for financial entities.In the future, more specialized models in vertical fields will emerge in the financial field, and the application of AI will be integrated into the cross-border scenario ecosystem to develop convergent financial products and achieve full-process business empowerment.From local application to full-process penetration, from standardized service to dynamic personalized marketing, from automated operations to intelligent decision-making, and from passive response to value creation, the evolution of AI technology will further change the future marketing landscape in the financial sector.