In the wave of digital transformation in the financial industry, large models and generative AI are becoming the new engines of change. Not only have they revolutionized the way financial services operate, but they also play a key role in dramatically improving the customer experience. Whether banks, insurance companies, or securities brokers, financial institutions of all kinds are actively exploring and implementing these cutting-edge technologies to capture the new opportunities brought about by the transformation of the industry.
In this process, AI has a wide range of application scenarios, covering risk management, fraud detection, personalized financial product recommendations, robo-advisors, and automated customer service.
In the face of these changes, how should financial institutions strategically deploy AI technology? In practice, what are the typical cases and experiences of the industry that are worth learning from? These questions will be explored in depth in this article, which aims to provide a comprehensive guide to AI application and strategic deployment recommendations for the financial industry.
The impact of generative AI on the financial industry
1. Generative AI will significantly improve the efficiency of financial institutions
According to research by the McKinsey Global Institute (MGI), generative AI technology could create between $2.6 trillion and $4.4 trillion annually across industries around the world. Among many industries, the banking sector is expected to see significant opportunities, with a potential annual value of $200 billion to $340 billion, accounting for 2.8% to 4.7% of the industry's total revenue, equivalent to 9% to 15% of operating profit, mainly due to significant improvements in the efficiency of the industry.
Since last year, leading financial companies have been working intensively to implement generative AI in their business scenarios. Citigroup's risk and compliance team, for example, began using generative AI technology last year to analyze and assess the impact of new capital rules issued by federal regulators. In addition, the Wall Street Journal recently reported that Goldman Sachs plans to launch its first generative AI tool for code generation to thousands of developers within the company by the end of June.
Financial institutions that have successfully implemented and leveraged Gen AI are working to develop an appropriate, customized operating model that takes into account the characteristics and risks of the new technology in detail, rather than simply embedding Gen AI technology into existing operational processes.
2. Four organizational forms of generative AI for financial institutions
McKinsey surveyed 16 of the largest financial institutions in Europe and the United States on the use of artificial general intelligence, with assets totaling nearly $26 trillion. The results show that more than 50% of research companies have adopted a more centralized organizational structure to implement general AI technology.
But this centralization may be temporary, and as the application of AI technology continues to mature, the structure of financial institutions will become more fragmented. Ultimately, financial institutions may find it beneficial to have individual functions prioritize AI general services activities based on their needs.
In McKinsey's research, although there are different ways for financial companies to implement generative AI, they can be summarized into the following four main organizational forms.
(1) High degree of centralization
In this highly centralized organizational structure, a core team is fully responsible for the design and implementation of Gen AI solutions. This team is at a low level within the organization and is able to quickly provide the latest AI-related skills and capabilities. In addition, the team has autonomy in the decision-making process and is not directly influenced by other business units or functional units.
(2) Centralized decision-making by the leadership and implementation by the business department
This architecture optimizes communication between leadership and the Gen AI team, significantly reduces friction in collaboration, and ensures that the application process of new technologies is effectively integrated within the enterprise. But this collaborative model can also cause some delays in the AI team's technology implementation, as each project must be reviewed and approved by leadership before moving forward.
(3) Led by business departments and supported by leadership
Based on this model, teams are able to quickly gain buy-in from business units and functions as the Gen AI strategy is endorsed from the bottom up. However, it can be challenging to roll out the application of Gen AI across different business units, as each department may have different maturity levels in the development and application of Gen AI capabilities.
(4) Highly decentralized
In this model, communication and collaboration between cross-functional or cross-functional teams are smoother, valuable insights can be quickly generated, and internal integration can be effectively facilitated. However, when business units work on Gen AI projects in isolation, they may face some risks, such as a lack of the wealth of knowledge and industry best practices that centralized management can provide. This lack can make it difficult for companies to tap into the potential of AI technology, which in turn affects opportunities for major innovation breakthroughs.
The main application scenarios and cases of large models and Gen AI in the financial industry
1. The main application scenarios of large models and Gen in the financial industry
DeepEngine Technology believes that AI is helping financial institutions to achieve "intensification" and "refinement" of service models in the field of retail business, and significantly improve operational efficiency and effectiveness.
The "intensive" service model is aimed at a large number of long-tail customers with scattered and diversified needs, through artificial intelligence technology, adopting a centralized and large-scale business strategy, optimizing resource allocation and improving operation and management efficiency. In short, use artificial intelligence technology to cover the medium and long-tail customer groups that are not covered by manual energy at a lower cost, and provide customers with a service quality of 70 points.
The "refined" service model is based on the customer's asset size, transaction characteristics, financial needs, family situation and other portrait information, and the customer group is deeply segmented. Limited by the bottleneck of service capacity, it is difficult for financial institutions to provide refined and differentiated services for customer segments under traditional technical conditions. The use of artificial intelligence technology enables financial institutions to provide personalized products and services tailored to each customer in an ideal state. This includes providing personalized content services based on the customer's investment life cycle, transaction cycle, and important time points to meet the personalized needs of customers.
According to a senior executive of a leading brokerage firm in South China, large models and generative AI usually have the following main application scenarios in the financial industry:
(1) Intelligent Q&A
By building a centralized Q&A system based on large model technology, users can ask questions through a single channel and get answers quickly. This system not only facilitates the mastery of basic business knowledge by employees, but also enables front-line account managers to deal with common customer problems more efficiently, which significantly improves work efficiency.
(2) Investment consulting scenario
Due to the limited resources of investment advisors, traditional stock diagnosis and analysis services often only cover important customers of large branches. The use of large-scale model technology, combined with regular reports and professional materials, such as research reports and prospectuses, can effectively improve the work efficiency of investment consultants, expand the coverage of services, so that more customers can enjoy professional stock diagnosis services, help them quickly grasp investment opportunities and risks, and make wise investment decisions.
(3) Individual stock diagnosis scenario
Investment advisors are often challenged by the rapid pace of market change as they distill market dynamics and hot news to form investment opinions. The use of large model technology can assist investment consultants to quickly capture market hotspots, stimulate creativity, and transform complex information into a form that is easy for customers to understand, effectively promoting customer conversion. At the same time, this approach also helps to extend the capabilities of professional investment advisors to front-line account managers and cultivate more talents with professional investment advisory skills.
(4) Fund diagnosis scenario
Investment consultants need to deeply analyze the market environment when conducting fund diagnosis, which requires solid investment research capabilities. Through large-scale model technology, it can provide comprehensive fund research and diagnostic support for professional investment advisors, help them improve their investment research capabilities, and optimize fund portfolio management.
(5) Account diagnosis scenario
A comprehensive account diagnosis is one of the important responsibilities of an investment advisor. Using large model technology, we hope to provide professional investment advisors with comprehensive account diagnosis capabilities, support them to analyze clients' portfolios, evaluate risks and returns, and provide clients with objective and professional diagnostic reports, so as to improve investment research capabilities.
In addition, generative AI can also have the following typical application scenarios in the financial industry:
(1) Fraud detection and prevention
Data in the financial sector, such as credit card information, personal records, and bank account details, makes it a prime target for cyberattacks. Generative AI combined with fraud detection algorithms improves data protection.
Traditional fraud detection algorithms use machine learning (ML) to train themselves on historical data, making it difficult to keep up with emerging fraud methods. Generative AI, on the other hand, can optimize detection algorithms by creating synthetic "anomalous" patterns to stay ahead of fraudsters. This reduces the need for oversight, enables greater automation, and makes it more efficient to identify cyberattack attempts.
(2) Personalized financial services and support
Personalized service and support are key factors in improving the competitiveness of a business, and it is estimated that it can lead to an annual revenue increase of up to 10%. In the financial sector, personalization is challenging due to the large amounts of customer data that need to be processed, such as transaction history, spending preferences, and savings goals.
Generative artificial intelligence (GenAI) plays an important role in this regard, quickly using this data to generate customized recommendations and offers, improve customer satisfaction, promote cross-selling, and enhance the competitiveness of businesses. In addition, GenAI also provides efficient self-service through intelligent virtual assistants and automated form submissions, helping financial institutions reduce costs and improve customer engagement, which is a key tool to optimize service efficiency and customer experience.
(3) Risk assessment and credit scoring
Credit scoring is a core part of the loan approval process in which financial institutions must assess the credit profile and potential risks of their customers. Traditional credit scoring relies on historical data and fixed rules, but this approach can be inflexible and difficult to adapt to the complexity and variability of credit risk. In addition, these methods require constant monitoring and in-depth analysis, which is time-consuming.
Generative Artificial Intelligence (GenAI) offers a new solution in this area. It creates synthetic data that closely resembles real data and combines it with real-world data to build richer training datasets to train more accurate predictive analytics tools. GenAI's ability to efficiently process large amounts of dynamic data reduces reliance on manual operations and makes the credit scoring process more reliable and efficient.
and (4) compliance and regulatory challenges
Regulatory compliance is critical in banking and is closely linked to risk assessment and human error. Financial institutions must comply with a range of regulations related to operations, confidentiality, security, and more, requiring exhaustive data collection, analysis, and reporting, which are time-consuming and error-prone.
Generative Artificial Intelligence (GenAI) can handle these tasks effectively. GenAI generates high-quality synthetic data to enhance the accuracy of compliance controls and quality assurance, ensuring fast, consistent, and error-free compliance reporting. In addition, GenAI is able to continuously monitor compliance, automatically notify violations, and take timely action.
(5) Market and investment analysis
Financial analysis involves dealing with large amounts of data such as market trends, company reports, financial estimates, etc. Analysts need to constantly monitor this data, which takes a lot of time and effort.
Generative artificial intelligence (GenAI) plays an important role here. It is able to quickly browse and analyze vast amounts of historical data to identify patterns and anomalies that humans may have overlooked. GenAI's automated analysis process not only generates insights, but also creates trading parameters such as the best time to buy and sell, stop loss points, and position size.
This data-driven approach provides banks with a significant competitive advantage, allowing them to better understand market conditions and develop more precise and effective strategies. GenAI is becoming a powerful tool for financial analysts navigating complex data.
(6) Document processing/report generation
Jobs in the financial industry involve a lot of information processing, especially when dealing with documents and information from different sources, which are often heterogeneous. For example, when analyzing various financial reports, deciding whether to grant a loan to a financial customer requires a combination of factors such as legal disputes, financial statements, shareholding structure, and articles of association. Therefore, one of the most direct and effective application scenarios of artificial intelligence is to deal with these multi-source heterogeneous report generation tasks, which are not only more costly, but also lower in quality if they rely on manual processing.
The main purpose of processing these reports in the financial industry is to identify and assess risks. When the quality of the report is poor, it often means increased risk, which in turn can lead to an increase in the non-performing loan ratio. Therefore, improving the quality of report processing is crucial for the financial industry.
(7) Marketing to reduce costs and increase efficiency
In the promotion of fund products and risk reputation management, financial practitioners often face problems such as difficult data capture, small amount of information, slow content production, and low communication efficiency.
Through multiple training and a powerful data platform, the commercial AI model of Youlianyun Public Fund has opened up the real-time link of information production, realized 7*24 hours of automatic generation of various types of text and intelligent rewriting, and can generate short videos of funds across modalities, surpassing the efficiency of traditional operations.
(8) One-click auxiliary product sales
In recent years, the domestic ETF market has developed rapidly, and the number and scale have continued to grow. Wind data shows that up to now, the total share of domestic ETFs has increased by 470 billion to 1.94 trillion, the total scale has increased by 320 billion yuan to 1.98 trillion yuan, and 54 new ETF funds have been issued, with a total number of more than 800.
Kirin provides solutions for public funds, brokers, banks and other fields. It obtains and analyzes ETF product data in real time, generates key information such as product analysis, research report summaries, and opinions, and improves the efficiency of information retrieval and the ability of AI to generate information in batches. The powerful ecological connection penetrates into the ports of data, trading, search, news, video, and finance, assisting investors in education and value investment concept presentation with one click, and generating multi-dimensional BI visualization reports to help fund companies quickly analyze and make decisions.
(9) Easily navigate reputational risk management
In reputational risk management, financial institutions need to establish a whole-process management system. The strong data feedback capability of the Kylin model helps fund companies obtain risk information in a timely manner, quickly form solutions, provide closed-loop support from generation, use to data decision-making, release dependence on people and process pressure, and promote the reputation management and value presentation of fund companies.
Youlianyun Kirin Financial Scenario Commercial AI Model solves the multi-scenario needs of fund companies at AI speed, easily controls the demand pain points, and provides more scientific, reliable and professional links in the process of sales, marketing and reputation management, helping to gain gains, reduce costs and increase efficiency.
2. Typical cases of Gen AI application in the financial industry
(1)AlphaSense推出生成式AI助手-AlphaSense 助手
AlphaSense, a leading platform for financial market intelligence and search, follows the launch of AlphaSense Assistant, an innovative generative AI chat tool designed to transform the way financial practitioners extract industry insights from millions of business and financial documents. In addition, AlphaSense has launched an enterprise intelligence service that securely integrates its AI-powered search, summarization, and chat capabilities into customers' proprietary organizational knowledge and AlphaSense's extensive content library.
Powered by AlphaSense's Large Language Model (ASLLM) tailored for market intelligence, the AlphaSense Assistant provides a conversational chat interface based on AlphaSense's industry-leading content library, greatly improving the research efficiency of business and finance professionals. Users can easily consult investment opportunities or competitor analysis in a specific area and get accurate answers instantly. These answers also have built-in auditability, allowing users to trace back to the source material for contextual and validated checks.
（2）FeatureSpace 推出 TallierLTM™金融垂直大模型
FeatureSpace, the world's leading provider of enterprise-grade fraud prevention technology, has launched the world's first Large Transaction Model (LTM), TallierLTM™.
Using a self-supervised pre-trained approach, TallierLTM™ provides an in-depth analysis of transaction behavior across jurisdictions and market segments, enabling it to truly reflect consumer transaction behavior in the real world. Compared to industry standard models operating at a typical 5:1 false positive rate, TallierLTM™ delivers up to 71% improvement in fraud detection accuracy.
By analyzing billions of transactions, TallierLTM™ has the ability to uncover hidden transaction patterns and predict consumer behavior, providing data scientists with critical insights to distinguish between legal and criminal activity. Financial institutions can interact with TallierLTM™ through an embedded API to convert transaction history into machine-readable feature vectors, creating a unique "behavioral barcode" that fully represents the consumer's transaction behavior while protecting personal privacy.
(3) Visa launches generative AI-based fraud solutions
Based on generative AI technology, Visa launched an Account Attack Intelligence (VAAI) scoring tool for United States financial institutions in May to identify and prevent enumeration attacks in financial transactions. The tool detects suspicious activity in real-time and provides financial institutions with a risk score to help customers pinpoint when they need to block transactions to prevent potential fraud.
By learning the cardholder's transaction habits, the AI tool can automatically assess transaction risk within four milliseconds, distinguish between normal consumption and abnormal behavior, and quickly identify potentially offensive transactions. Trained on data from over 15 billion transactions, it generates a risk score by comparing historical enumeration attack patterns to predict whether a transaction is an enumeration attack. Compared to existing risk assessment models, VAAI tools have made significant improvements in reducing false positives, reducing false positives by 85%.
(4) The case of Agent of Landma Technology's due diligence report
A domestic bank has launched an inclusive loan service, aiming to provide convenient loan services for small and medium-sized enterprises to support the development and innovation of the real economy. However, in the process of granting inclusive loans, it is difficult for banks to fully understand the information status and repayment ability of borrowers, especially when facing small and medium-sized enterprises and individual industrial and commercial households that lack perfect financial records.
Therefore, bank relationship managers often spend a lot of time collecting and analyzing various information, conducting due diligence on the applicant, and writing due diligence reports, including customer situation analysis, financial data analysis, due diligence review analysis, etc.
However, the level of business analysis of front-line relationship managers of banks is uneven, resulting in the quality of due diligence and due diligence reports, and at the same time, the report templates in the system are often rigid and cannot be used directly, so front-line relationship managers spend a lot of time on report writing.
Based on this requirement, Lanma has built a due diligence report agent based on large language model for customers, which can automatically give analysis conclusions and generate reports to assist the work of front-line bank account managers, which can not only save 80% of the report writing time of front-line account managers, but also comprehensively review customer information, help manual find some points or omissions that are not easy to find, and reduce the error rate.
3. A representative AI financial service provider in China
Shenqing Technology
Founded in 2018, Deepbase Technology is a company focusing on providing enterprises with artificial intelligence technology empowerment, especially in the fields of natural language processing, personalized recommendation and large language models. The core founding team is from IBM China AI Lab, which has participated in Watson's research and development, and has excellent technical and engineering strength.
The company uses AI and big data analysis technology to provide content technology and intelligent marketing products for brokerages and banks to help customers acquire, activate and convert transactions. Deeply cultivate the financial industry, grasp the in-depth knowHow of growth scenarios, and build an application product system that the industry just needs based on AI Agent. The product has achieved PMF, covering 80% of large and medium-sized brokerages and 50% of large banks.
Enterprise Advantages:
Strong technical strength: The core technical team of Shenqing originates from the IBM artificial intelligence laboratory and has a profound technical background. The pre-trained model (L1) for the financial industry independently developed by Shenzhen Engine has an accuracy evaluation that exceeds that of multiple domestic 100 billion models and is close to GPT4. In the process of landing large models in 2B scenarios, the most important thing is accuracy, and Shenqing is in a leading position in the industry in terms of important technical indicators that customers care about, such as: the recognition accuracy of complex interfaces in multiple scenarios is more than 93%, the semantic accuracy and recall rate of multiple rounds are more than 95% and 90% respectively, and the qualification rate of AIGC content is over 95%.
In-depth business and scenarios: In the process of development, Shenqing has attracted a group of business experts from Party A's customers, so it can effectively integrate technology, products and customer business. Through scenario design, it is deeply integrated into the core business of customers to achieve quantifiable business value. For example, a number of self-developed AIGC content products have been launched on a large scale, embedded in the core marketing process of customers, and achieved a 100-fold increase in content production efficiency.
Abundant and high-quality data: After years of accumulation, Shenzhen Engine has accumulated a large amount of industry data and annotated data in the securities industry. And through the closed-loop of the product in the business scenario, it can continuously obtain more industry data. These key data are important guarantees for Shenqing's model training and product iteration.
Many customer cases and deep cooperation: Shenzhen Engine has cooperated with a number of top 10 domestic securities firms in large-scale model projects, and the number of cases is in a leading position in the industry. A large number of customers means that the effective iteration of the product is faster, rather than a closed-door operation out of the market. The head customer represents the latest development consensus in the industry and represents the new quality of productivity. There are many project cases, which means that there are more practical problems encountered, more pitfalls to step on, and more solutions to solve the problems, which are more reasonable. In addition, Shenzhen Engine has in-depth cooperation with a number of leading brokerages in the process of large-scale model implementation, and forms a closed-loop iteration through products, data, and business, which can enable customers to truly use the products, and continuously iterate AI products based on user feedback and data feedback.
Sweet new technology
Founded in 2016 and headquartered in Shanghai, Tianxin Technology is an artificial intelligence innovative high-tech enterprise with AIGC and 5G video communication technology and industry model as the core. It is invested by Sequoia China, GSR Venture Capital, Tongchuangweiye, etc.
The company's products include VCRM series products, which are marketing solutions based on Al+ video to help enterprises convert marketing. In the field of video marketing technology, we are committed to providing industry customers with scenario-based service solutions based on in-depth integration of Al+ video. The company's main service directions include retail e-commerce, new consumer brands, banking, insurance, consumer finance, games and other industries.
Enterprise Advantages:
A variety of customer reach methods: The platform can provide mainstream customer access methods in the market, including: AI interactive video, video notification, AI voice outbound call, video SMS, text SMS and other customer contact methods, which can be selected and combined according to different customer reach targets.
Improve user engagement: Through interactive video, users can interact with digital human content, which can greatly enhance user engagement and interest compared to traditional video content, thereby increasing user stay time and depth of engagement.
Enhance brand influence: First of all, through a 3-minute original video, you can quickly generate a digital avatar that is exactly the same as the real person of the spokesperson of a financial institution, with facial features, movements, expressions, and voices completely imitating yourself, and conducting video conversations with customers through digital avatars, based on natural language processing and large model technology, you can achieve multiple rounds of dialogue and intelligent interaction. Interactive video can enhance the brand image with its novel format and rich user experience, and through the user's interaction with the video content, it can increase the exposure and memory of the brand.
Customer service: Leverage AI technology to provide personalized video content recommendations based on users' behaviors and preferences to achieve a true "user-centric" approach. This personalized experience can lead to increased user satisfaction and loyalty.
Efficiency improvement: The outbound marketing platform can automatically analyze user data and interaction results, precipitate data, provide data support for marketers, and help them quickly adjust their marketing strategies and improve marketing conversion results.
AIGC-based content production: The outbound marketing platform can generate personalized marketing content according to the needs and preferences of customers, such as: using different digital human images, digital human replication, voice replica, and at the same time, in order to improve customer engagement and conversion rate, a large number of marketing content can be quickly generated, reducing the time and cost of manual editing, and meeting the rapid changes and update needs of the financial industry.
Lanma Technology
Landcode Technology is an enterprise-level AI Agent platform company based on large language models, with core team members from Google, IBM, Tencent, Byte, Alibaba, YITU and other well-known Internet and AI companies at home and abroad.
Lanma Technology has taken the lead in filling the gap in the middle layer of large models in China, and is a pioneer in exploring the application of large language models and AI Agents in China. Based on the underlying large language model, Landma has independently developed the enterprise-level agent platform "AskXBOT", which can connect people and systems, to help enterprises build super automation based on expert knowledge, thereby improving business quality and efficiency.
Landma Technology has completed tens of millions of Series A investments from IDG Capital, New Alliance Capital, and Atom Capital, and has reached strategic cooperation with a number of listed companies and unicorn companies.
Enterprise Advantages:
Advantages of "model neutrality": At present, large language model vendors only define parameters when releasing models, but do not define the specific feature parameters (FeatureList) of the model, which means that enterprise users often face the dilemma of blind people touching the elephant when applying large language models, it is difficult to accurately evaluate the applicability and efficiency of the model in specific application scenarios, and it is difficult to select, adjust and optimize the model according to their own needs in a cost-effective manner, which requires continuous matching and debugging based on experience and actual conditions.
As a model-neutral vendor, Landcode has accumulated a large amount of performance data of Agent's atomic capabilities in actual scenarios in the past year or so, so it has a better understanding of the performance and effects of models in different scenarios.
Expert knowledge is the key to the implementation of AI Agent in enterprises: the height of expert knowledge determines the value that AI Agent can provide, and data will help experts quickly iterate expert knowledge, thereby improving the versatility of AI Agent. Large language models lack knowledge of specific fields of enterprises and cannot solve practical business problems, just like a high-achieving student who graduated from a prestigious university, it is difficult to be competent for specific business tasks due to lack of practical experience; In addition, large language models have a vague understanding of the boundaries of their own capabilities, resulting in attempts to rely on their own understanding to give answers to problems beyond their own capabilities, and the results are often the opposite, which is often referred to as "model illusion".
Therefore, in enterprise-level application scenarios, expert knowledge is crucial to the implementation of large language models, and it can be said that expert knowledge determines the ceiling of AI Agent. In the AskXBOT platform, you can accumulate expert knowledge and industry experience, build an enterprise knowledge base, and promote knowledge sharing and inheritance. Based on this, the agent assists knowledge governance and cooperates with the digital precipitation of expert knowledge. Expert knowledge empowers agents and workflows to form a virtuous closed loop.
The ability to encapsulate mature skills for office scenarios, especially finance and accounting.
Integration with the existing organization, permissions, and infrastructure of the enterprise.
There are even clouds
Founded in 2015, Youlian Cloud is a leading financial AI application service provider in China. Its "Kirin AI Model" empowers financial institutions and listed companies through intelligent creation, recommendation, and push, helping customers achieve gains and cost reductions in scenarios such as marketing, product sales, and reputation management. The company focuses on the pain points of financial digital transformation, promotes the vertical application of large models, combines a huge corpus of financial professionals, integrates natural language processing, OCR and multimodal technologies, realizes accurate, real-time and intelligent acquisition of various event indicators, and meets the needs of customization and configuration.
Enterprise Advantage
Strong technical foundation: Youlianyun's "Kylin AI Model" empowers financial institutions and listed companies through intelligent creation, recommendation, and push, helping customers achieve gains and cost reductions in scenarios such as marketing, product sales, and reputation management.
Accurate data processing: The Kylin model can obtain and analyze ETF product-level data in real time, generate product analysis, research report summaries and opinions, improve information retrieval efficiency and AI batch generation of information, and solve the difficulties of fund companies in product marketing.
Extensive ecological connection: A powerful ecological connection can penetrate into investor gathering places such as data, trading, search, news, video, and finance, assist investors in education and value investment concept presentation with one click, and generate multi-dimensional BI visualization reports to help fund companies quickly analyze and make decisions.
Comprehensive compliance assurance: In the field of reputation management and investor relations management of listed companies, Kylin Model ensures the authenticity and compliance of information through intelligent creation, intelligent labeling and visual reporting services, and tracks the push status in real time to provide strong business decision-making support.
Industry recognition: Youlian Cloud has obtained the filing of 3 deep synthesis algorithms from the Cyberspace Administration of China, was selected into the "2023 Large Model and AIGC Industry Atlas" of the China Academy of Information and Communications Technology, and obtained the DSSC excellent certification of digital software product capabilities, and became the compilation unit of the "Digital Software Product and Service Capability System Specification" of the China Academy of Information and Communications Technology, and participated in the formulation of industry standards.
Key capabilities for Gen AI in the financial industry
Although the implementation of Gen AI in the financial industry will face challenges such as data privacy and security, technical and resource thresholds, model interpretability and transparency, and regulatory compliance, it will be less difficult to implement than traditional AI projects, and the implementation path will be more straightforward.
Tianxin Technology believes that the implementation of Gen AI in the financial industry needs to span the following 7 dimensions:
1. Define transformation goals and strategies:
Financial institutions first need to clarify their digital transformation goals and strategies, and determine the role and positioning of AI technology in them. An effective Gen AI strategy at scale must include the following key elements: vision, alignment, and commitment from senior leadership, as well as business unit-level accountability for delivering results, clear use cases and goals, and a comprehensive operational plan.
2. Select the right application scenario:
After clarifying the transformation goals, financial institutions need to select the application scenarios of AI technology. These scenarios should be pain points in business processes or potential areas of value creation. For example, AI technology can be used for risk assessment, credit approval, robo-advisory, customer service, and more.
3. Accumulation of data and technology:
Financial institutions need to accumulate large amounts of data and related technical capabilities. Data is the foundation of AI technology, and technical capabilities determine the competitiveness of financial institutions in the AI field. This includes technical capabilities such as building data warehouses, data mining, machine learning, and deep learning.
When deploying large models, financial organizations need to integrate with their existing systems, workflows, enterprise applications, and data sources. This is a critical and complex task. McKinsey believes that effective integration and model maintenance will rely on multiple architectural components: context management and caching, policy management, model center, cue library, MLOps platform, risk management engine, large language model (LLM) operations, etc.
(Photo courtesy of Shenqing Technology)
Data quality is critical, especially in the field of artificial general intelligence. With massive and unstructured datasets, it becomes more challenging to ensure the quality of the output answers. Leading financial institutions are leveraging high-quality talent and automation to intervene precisely at key points in the data lifecycle to ensure high standards of data quality. At the same time, leaders in the data space need to deeply consider the security risks posed by new technologies and be ready to act quickly in response to regulatory changes.
4. Build an efficient organizational structure:
In order to facilitate the smooth implementation of AI projects, financial institutions need to build an efficient organizational structure. This includes setting up a dedicated AI team, clarifying responsibilities and division of labor, and working closely with the business. Before optimizing their organizational structures, financial institutions must think about why their current architectures struggle to seamlessly integrate AI innovation capabilities.
Financial institutions that have successfully implemented AI do not encourage initiatives to be implemented, but rather do so by equipping existing teams with the resources they need and embracing the skills, talent, and processes required by AI generalism.
5. Focus on talent development and cooperation:
Financial institutions need to cultivate a group of interdisciplinary talents who understand both financial business and AI technology. You also need to regularly evaluate your own talent acquisition strategy to adapt to changing priorities. Clear opportunities for career advancement and advancement—as well as meaningful and rewarding work—are very important for the average tech practitioner. In addition, partnering with leading companies or research institutes in the field of AI can accelerate technological progress and innovation.
6. Strengthen compliance and security risk management:
Financial institutions must ensure the compliance and security of their operations when implementing AI technologies. This includes not only complying with relevant laws and regulations, but also protecting customer privacy, preventing risks such as fraud and money laundering.
Before introducing large-scale models and generative AI, financial institutions often need to redesign their risk management and model governance frameworks and develop new controls as needed. The explainability of models and the fairness of decision-making are critical issues that must be addressed comprehensively and deeply before any generative AI applications can be promoted. In this way, financial institutions can maintain the compliance and security of their business while ensuring technological advantages.
7. Continuous Optimization and Innovation:
AI technology is constantly evolving, and financial institutions need to continuously optimize and innovate in practice. This includes gathering feedback, improving algorithms, exploring new use cases, and more.
Challenges in implementing Gen AI in the financial industry
Although large models and Gen AI can bring great value to the financial industry, due to the highly regulated nature, the financial industry faces many more severe challenges than other industries in implementing large models and generative AI. Here are the top three most critical questions:
1. Ensure data quality and security
For financial firms, access to high-quality, representative data analysis to train AI models is key to achieving technological benefits. The performance and accuracy of AI models are highly dependent on the quality of the training data, so financial institutions must implement strict data governance processes to ensure the accuracy and reliability of the data.
Many banks have a large and complex data architecture, often spanning decades and involving multiple mainframe systems. Integrating and preparing this disparate data for use in AI projects is a daunting task that requires a significant investment of resources and effort.
At the same time, financial companies must strictly comply with data protection regulations to ensure that sensitive customer data is properly anonymized and secured. This is not only a technical challenge, but also a test of the company's responsibility and responsibility in terms of data privacy and compliance.
2. Comply with financial laws and regulations
The application of AI systems in the financial sector must strictly comply with a series of regulations, and involve multiple business links such as credit approval and transaction monitoring. Compliance requires financial institutions to keep detailed records of relevant information and maintain transparency of the model at all times, which makes it difficult and costly to manage. At the same time, financial institutions need to regularly monitor the performance of their AI systems to ensure that there are no deviations and to properly handle possible unexpected outcomes.
In addition, the application of AI technology in the financial field involves the processing and analysis of massive amounts of data, which not only requires financial institutions to have powerful storage and computing resources, but also brings challenges to infrastructure. While cloud computing offers flexible solutions, data security and regional regulatory constraints often become barriers to its widespread adoption. At the same time, the seamless integration of advanced AI tools with the existing IT systems of financial institutions is also a technical challenge to overcome.
3. Moral considerations and biases
The integration of AI in finance raises important ethical considerations, particularly in terms of bias and impartiality. AI systems can inadvertently perpetuate or even exacerbate bias in training data. For example, if historical loan data is biased against certain specific populations, AI models trained on that data may continue to disadvantage those groups.
In addition, the rapidly changing regulatory environment places higher demands on financial institutions. As legal and ethical expectations for AI continue to evolve, financial institutions need to constantly adapt to new regulatory policies and maintain the flexibility of their systems to respond to these changes.
The future of artificial intelligence in finance
Although generative AI is currently a buzzword across industries, the best way to put the technology into practice has yet to be determined.
Zhou Jian, CEO of Lanma Technology, pointed out that the main challenge facing the financial industry is that the accuracy of large-scale models has not yet reached a satisfactory standard, and the path to effectively match specific scenarios with corresponding technologies has not yet been found. The core challenge for the industry is how to carefully select or develop the right large models and solutions, and then ensure that they can outperform humans in specific application scenarios, which is also the most challenging part.
In addition, at the cognitive level, a common misconception is that people are often misled by the so-called "ideal path" and think that large language models can handle all tasks. However, in practice, it may be impossible to complete the corresponding tasks due to the inaccurate mastery of professional knowledge by business personnel. In this context, we need to enable business people to articulate their needs through more efficient human-machine interactions in order to achieve effective end-to-end support in business processes.
Another common misconception is that people expect users to adapt to technology, rather than technology to adapt to users. If the industry as a whole can innovate and improve in terms of how technology adapts to how users interact and understand needs, it is possible to realize the full potential of large models. However, at present, there is relatively little exploration in this field.
The implementation of large models and generative AI in the financial industry will not only require technological upgrades, but also a transformation of corporate culture to embrace innovation, which will be a long and challenging process.
Resources:
https://www.mckinsey.com/industries/financial-services/our-insights/scaling-gen-ai-in-banking-choosing-the-best-operating-model
https://www.datacamp.com/blog/ai-in-finance
https://usa.visa.com/about-visa/newsroom/press-releases.releaseId.20661.html
Author: qiuping
Source: Extraordinary Industry Research
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