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Financial institutions are facing challenges such as intense internal market competition, rising labor costs, and increased regulatory scrutiny. Digital transformation has become a consensus within the industry. Developing a digital blueprint, creating a digital transformation path that aligns with their resources, and deeply integrating business with technology to implement in practical scenarios can drive the restructuring of business models, innovation in operations management, enhancement of customer service, and optimization of risk control and compliance through digital technologies. This approach can create new competitive advantages and bring new value growth points for enterprises. Therefore, continuously increasing budgets, procuring the latest technologies, updating traditional operating models, and enhancing the operational level of financial businesses with fintech as the core engine is the future trend.
AI + Finance
The application of artificial intelligence in the financial industry is primarily concentrated in the mid-tier and channel layers of IT architecture, accurately addressing digital needs in various specific scenarios, covering areas such as intelligent investment advisory, trading, customer service, information services, and investment research. With the growing internal demand of financial institutions, continuous investment, and policy support, AI + Finance has broad development prospects.
The financial industry has accumulated a vast amount of high-quality data, with user numbers on various financial platforms reaching hundreds of millions and transaction data being enormous, containing invaluable information. Supported by mature algorithms and the surge in data, financial large models have emerged. These models are a type of vertical product and belong to the category of large models. A leading example is BloombergGPT, which plays a significant role in financial news classification, Q&A systems, and intelligent writing.
Examples of AI + Finance Business Scenarios
1. Computer Vision and Speech Recognition
Utilizing visual perception and content analysis technologies to process image and video data collected by front-end hardware, with the analysis results applied to areas such as early warning or decision support. The core goal is intelligent voice and conversational AI, building highly contextual and interactive products through ASR (Automatic Speech Recognition), TTS (Text-to-Speech), and NLP (Natural Language Processing) technologies, enabling human-computer voice interaction.
2. Knowledge Graph
Focusing on knowledge extraction and computation, constructing association networks based on financial big data to achieve functions such as risk anomaly identification and monitoring.
3. Machine Learning
This technology can be used to process the vast amounts of form data in banks, extracting labels and obtaining data features to effectively intercept financial risks and prevent significant losses caused by fraud and risk vulnerabilities. Additionally, it can enhance marketing precision, bringing more revenue to financial institutions.
Computer vision products are widely used in business scenarios such as OCR review, facial recognition access control, and secure payments, thereby saving labor costs. In credit risk control and compliance scenarios, financial institutions mainly procure machine learning and knowledge graph products. For precision marketing scenarios, in addition to the aforementioned products, they also procure some natural language processing products as well as intelligent voice and conversational AI products.
Security Solutions--Integration of Privacy Computing and Blockchain
Despite the financial industry's rapid growth spurred by advancements in artificial intelligence (AI), which has introduced innovative services and improved efficiency, significant security challenges persist. As AI becomes more deeply integrated into financial systems, the risk of cyber threats also escalates. Complex algorithms can become targets for hackers, who may exploit them through algorithm manipulation, data tampering, and system vulnerabilities. Additionally, automated decision-making systems, which often lack transparency, can lead to unforeseen security risks and compliance issues. Furthermore, AI technology relies heavily on vast amounts of personal and financial data, and if this data is accessed by malicious actors, it could result in severe privacy breaches and financial losses. Therefore, while the financial industry benefits from the efficiency and innovation brought by technology, it must also place a high priority on enhancing security measures to address the evolving cyber threats and technical challenges.
At the same time, addressing the issue of data silos is crucial. Financial institutions have an increasing need for cross-domain modeling by integrating data from peers and other industries, such as telecom operators, internet platforms, and traditional enterprises. It is a significant challenge to use technological means to achieve data integration and value circulation while ensuring the security and privacy of data assets. Although financial institutions possess relatively rich financial data assets, they often lack other highly relevant multidimensional data, necessitating a heavy reliance on external industry data for cross-domain model construction and data mining. For instance, data from the telecommunications industry typically exhibits high quality and completeness, which can fully describe user profiles and potential groups. Combining these datasets can leverage the strengths and scenarios of both sectors, maximizing the value of the data.
Blockchain technology has become a crucial component of data security. With its features of data traceability, resistance to tampering, and automatic execution of smart contracts, blockchain enables closed-loop management of the entire data lifecycle. Its popularity has fluctuated over the past few years. Initially, blockchain was primarily associated with cryptocurrencies like Bitcoin, but its applications have now extended far beyond cryptocurrencies. Current hot areas include DeFi (Decentralized Finance) and NFTs (Non-Fungible Tokens).
Privacy computing refers to modern cryptographic and information security technologies, such as secure multi-party computation, homomorphic encryption, federated learning, and trusted execution environments.
The combination of blockchain technology and privacy computing has become an essential part of security solutions. Currently, the applications of blockchain and privacy computing are mainly in five directions: solving specific single-point problems, expanding the application scope of consortium blockchains, providing cybersecurity-related services, establishing blockchain + privacy computing infrastructure platforms for enterprises, and creating blockchain + privacy computing infrastructure platforms that serve both enterprises and individuals. The integration of blockchain and privacy computing is an effective technological means to solve data sharing challenges, build trusted operations, and achieve high-value data circulation.
Existing Challenges
1. Blockchain requires protection of on-chain data privacy.
Blockchain faces significant challenges in protecting on-chain data privacy, including conflicts between data transparency and privacy needs, the risk of sensitive information being unable to be deleted due to the immutability of data, and the complexity and high cost of privacy protection technologies. Additionally, balancing privacy with transparency, complying with regulatory requirements in different regions, managing user privacy, lack of unified privacy protection technology standards, and the impact of privacy protection technologies on performance are all pressing issues that need to be addressed.
2. Security Needs Further Improvement
Privacy computing usually assumes that participants in multi-party computation strictly adhere to protocol processes and that hardware providers are entirely trustworthy. However, these assumptions do not always hold true in practice. Additionally, during the commercialization of privacy computing technology, system security risks are inevitable. Given the high-security requirements of privacy computing products, any security vulnerabilities in the system become the most susceptible to attacks.
3. Privacy Computing Requires Greater Computational and Communication Load
The large-scale application of privacy computing faces limitations in computational and network load, which can be tens to hundreds of times greater than traditional machine learning. Moreover, privacy computing involves multi-party synchronous computation, where any bottleneck in one party's computational or communication resources will directly limit the performance of the entire computing platform.
4. Difficulty in Achieving Security Consensus Among Parties
Privacy computing requires multiple participants to reach a consensus on security. However, it is challenging for participants to intuitively verify each other's security, and currently, there is a lack of security grading standards for privacy computing.
5. Difficulty in Interconnecting Different Products
Each privacy computing application faces the challenge of conducting multi-party computation with different institutions. However, since the privacy computing platforms deployed by each party may be based on specific algorithms and designs, it is difficult to achieve information interoperability between different platforms, leading to the need for repeated deployments.
Security System Architecture
Industry Layout
AI Enterprises and Fintech Deployment
At the current stage, the main players in the AI + finance field include four major categories: Internet giants, AI enterprises, smart hardware companies, and technology subsidiaries of financial institutions.
Among them, the core advantage of Internet giants and AI enterprises focusing on vertical domains lies in AI + financial software business, capable of independently completing closed-loop AI + financial solutions from the technical layer to the application layer, while being supported by AI basic layer capabilities.
The technology subsidiaries of financial institutions mainly rely on the financial business knowledge and data accumulated by the parent company, entering the market from the AI financial application layer, leveraging the advantages of internal relationships within financial institutions, serving as integrators of resources and task contractors in the upstream and midstream sectors, and bridging their own technological gaps through cooperation with AI enterprises and smart hardware companies.
These subsidiaries are typically wholly-owned or joint ventures of banks, insurance companies, securities firms, etc., focusing on deep integration of AI technology with financial services. Their strategic positioning is mainly to provide IT construction and digital transformation services for the parent company, with the primary goal of empowering the development of the financial main business. While meeting the needs of the parent company, financial technology subsidiaries also consider outputting technological capabilities to other industry players as a business extension direction. Due to the majority of financial technology subsidiaries being established with investments from leading financial institutions, they potentially have an advantage in outputting technologies to other industry players. Banks have always been at the forefront of exploring financial technology, while securities firms are relatively conservative in establishing financial technology subsidiaries.
Large financial institutions tend to establish financial technology subsidiaries, while medium-sized financial institutions, due to limited resources, struggle to support the introduction of top IT teams or the establishment of financial technology subsidiaries. Meanwhile, AI enterprises can provide highly tailored products or solutions based on specific financial business needs of customers. Therefore, purchasing technology solutions and services outputted by other institutions becomes an effective way for medium and small-sized financial institutions to rapidly deploy in the fintech field.
Security Solution Company Layout
The trend of integration between privacy computing, blockchain, and AI models is evident. In the field of privacy computing, the company layout mainly includes technology innovation companies, cloud service providers, and startups. Technology innovation companies are dedicated to developing core technologies such as secure multi-party computation (MPC), homomorphic encryption, and differential privacy. They typically collaborate with enterprises in sectors such as finance, healthcare, and government to meet high privacy protection requirements.
In the blockchain field, the company layout includes blockchain platform developers, fintech companies, and enterprise application companies. Blockchain platform developers like Ethereum and Hyperledger provide underlying blockchain technology and development platforms. Fintech companies primarily use blockchain technology for cross-border payments, digital currencies, and supply chain finance, while enterprise application companies develop blockchain applications in areas such as supply chain management, identity verification, and the Internet of Things.
Some Companies Within the Industry
To sum up, the development of financial technology not only determines the depth of enterprise digital transformation but also shapes the future landscape of the financial industry. With the continuous maturity and application of technologies such as big data, artificial intelligence, blockchain, cloud computing, privacy computing, 5G, virtual reality (VR), augmented reality (AR), and edge computing, the financial industry will witness further transformations and innovations. The integration of these technologies will further drive the popularization and personalization of financial services, accelerating the development of digital payments, intelligent investments, and smart risk management.
Moreover, financial technology will continue to lead the emergence of new financial formats, such as digital currencies, DeFi (decentralized finance), smart contracts, injecting more vitality and dynamism into the financial industry. Therefore, the future prospects of financial technology are promising and full of opportunities, as it will continue to lead the financial industry towards a direction that is more intelligent, efficient, secure, and convenient.
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