Observation report of VBOX AI global financial services AI application

This is one of the largest international empirical studies on AI applications in Financial Services initiated by Vbox Ai Foundation. The Respondents involved 151 institutions and enterprises from 33 countries, of which financial technology companies accounted for 54% and traditional financial institutions accounted for 46%. This research completed the data collection by issuing online questionnaires, and carrying out comparative analysis to obtain the results, aiming to understand and analyze the current situation and impact of AI in financial services under the current background. The purpose of this study is to understand and analyze the current situation of AI application in financial services and its impact. This study completed the data collection by issuing the online questionnaire, and carrying out comparative analysis to obtain the results.

The research comprehensively presents how can current fintech companies and traditional financial institutions apply AI to financial services, how can AI achieve the promotion of diversified business models, how can AI facilitate the delivery of new products and services, and how can AI play a strategic role in digital transformation. The result shows how global financial service providers deal with the risks and the regulatory challenges, as well as the impact of AI on the competition pattern and employment situation brought about by AI application.  In general, AI is expected to bring about various different paradigm changes in financial services. These changes will be reflected in many aspects: how can data be used to generate more actionable insights; business model innovation (e.g., using AI as a service for sale); changes in the competitive environment caused by the emergence of large technology companies and the integration of the industry; the various impacts of AI on work and regulation; the impact of AI on risk and bias; and the further development and application of disruptive technologies.

As AI has been adopted by a large number companies to improve their profitability and expand their scale, the application speed of AI in the field of financial services has been significantly accelerated, which has led to complex and multifaceted impacts.

The main findings of this empirical study are as follows:

In the short term, AI is expected to become an essential business driver for financial services, 77% of respondents predicted that AI would be of high or very high importance to their business in the next two years. Although AI is seen as highly strategic in most cases with fintech companies, many traditional financial institutions expect to increase their use of AI in the next two years to follow the trend.

With the increasing importance of AI, its application in key business functions is becoming more and more extensive. About 64% of respondents predicted that AI would be adopted and would generate revenue in the next two years, including: new products and processes, enterprise process automation, risk management, customer service and new customer development. At present, the utilization rate of risk management is as high as 56%, which is regarded as the field with the highest utilization rate of AI, followed by the increase of revenue through new AI auxiliary products and processes, with the utilization rate as high as 52%. But these companies expect that in the next two years, the latter will surpass the former and become the most important AI application area.

AI is expected to be the key to taking the lead in specific areas of financial services. For example, it will be the main driver of return on investment for asset managers. Lenders generally hope to use AI assisted credit analysis to achieve profit, while payment providers hope to increase the application of AI in customer service and risk management.

With the gradual strengthening of AI leadership, major capitalists plan to further increase their investment in R & D, and the technology gap between major investors and small investors is widening. Such an ambitious investment plan seems to be based on the long-term growth benefits of investing in AI. Once AI investment reaches the “critical” level of about 10% of R & D expenditure, the benefits of long-term growth will be achieved.

Compared with traditional financial institutions, the application of AL in financial technology enterprises seems to be different. Financial technology enterprises are more inclined to create products and services based on AI, they adopt autonomous decision-making system, and rely on products based on cloud technology; while traditional financial institutions mainly focus on apply AI to realize the update of existing products. This may explain why AI has a more positive impact on the profitability of fintech companies — among respondents, 30% of fintech companies indicated that AI could significantly improve their profitability, while for traditional financial institutions, the proportion is slightly less, accounting for only 7%.

Fintech is increasingly using AI supported products as services for sales. The successful practice in the market has proved that by attracting talent while providing continuously improved AI driven services through larger and more diverse data sets, selling AI services can promote large organizations achieve the production of “AI flywheel” (a virtuous cycle of self-promotion).

Leaders in AI field usually set up special company resources with data analysis function to carry out AI implementation and supervision, and reach cooperation with existing IT departments. On average, they also adopt more complex technologies to obtain more complex AI usage scenarios.

The key to making full use of AI’s advantages is to produce novel insights with alternative datasets. 60% of respondents choose to adopt new or alternative forms of data in AI applications. The most commonly used alternative data sources include social media, payment service providers data, and geographic location data.

Traditional financial institutions expect that nearly 9% of the work in their organizations will be replaced by AI by 2030, while fintech companies expect AI to increase the number of their employees by 19%. According to the survey sample , traditional financial institutions are expected to reduce about 336000 jobs, while financial technology enterprises are excepted to increase 37700 jobs. Among them, the reduction rate of investment management department is expected to take the first place, with a net decrease of 10% in five years and a net decrease of 24% in 10 years.

The quality and acquisition of data and the recruitment of talents are the main obstacles in the development of AI for any type of departments and enterprises. More than 80% of respondents hold the view that the above factors constitute barriers to themselves, while barriers such as hardware / software costs, market uncertainty and technology maturity seem to receive less attention. Nearly 40% of the respondents hold the view that the existing regulations hinder the application of AI, while only nearly more than 30% of the respondents believe that regulations can promote the application of AI. The most significant obstacle for business organizations is the regulation of data sharing between jurisdictions and entities, and a large number of organizations believe that the complexity and uncertainty of regulations impose a burden on them. Chinese companies tend to be more positive about regulatory impact than those in the US, UK or continental Europe.

Using AI for large-scale operations is likely to lead to increased risk and bias in certain market areas, with at least one in five companies feeling unable to mitigate these difficulties. In particular, enterprises are worried that AI may lead to increased bias in decision-making, or expose themselves to the risk of big data and privacy violations through resource sharing. However, many companies have introduced risk and compliance teams into AI implementation, and these companies have shown more confidence in their ability to cope with their own risks.

Long-established algorithms based on simple machine learning are more widely used than complex solutions. However, many respondents plan to implement computer vision and NLP, which usually involve deep learning, within two years.

Nearly half of the respondents regard the “technology giants” (referring to large technology companies, such as Google, Facebook, Alibaba, etc.) that use AI’s ability to achieve progress in the field of financial services as their main threat for competition.

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