Big data and Artificial Intelligence (AI) financial management can increase market transparency, and as such the financial institutions could render investors with more pertinent services, and assist their clients to diversify investment risk, improve operation efficiency for financial institutions and sustain the real economic development, according to Greg Gibb, Co-Chairman and CEO of Lufax .
According to Gibb, big data and AI financial management can increase market transparency, and as such the financial institutions could render investors with more pertinent services, and assist their clients to diversify investment risk, improve operation efficiency for financial institutions and sustain the real economic development. Gibb made the comments on November 4, in a keynote speech at the 2nd Annual Conference on “The Chinese Era of Digital Finance” at the Institute of Digital Finance, Peking University, where he specifically analyzed the financial impact of big data and AI.
Gibb pointed out that big data and AI have great impact on the financial sector, For example, they keep institutions to understand the credit standing of individual borrower from multiple perspectives so that a speedy judgement can be made.
“It only takes a few minutes or even seconds to make decision on loan, which in turn will increase the loaning opportunity for each borrower,” Gibb said.
AI and big data will lead the market to a higher level of transparency and standardization. Therefore, financial institutions could have a faster understanding on corporations, including market changes within the respective industry, corporate position and cash flow status, etc., according to Gibb.
Big Data and AI can also help the platform to better understand the specific needs of the investors in order that a better choice of products can be arranged.
“We found that the accuracy of the traditional (questionnaire) method of assessing the customers’ need is relatively low. In the past few years, we found that the data from the clients’ end, including their answers to a few psychological issues, we can grasp even more understanding of their ability to withstand risk,” Gibb said, adding, “Therefore, in the future, not only that AI and big data can enable us to attain a thorough understanding of our clients and companies from the asset end, but also enable us to know if the investors are suitable participants of the market and which investment products are most appropriate from the investment end.”
“The development on big data and AI will lead to the emerging of many new product portfolios for clients to invest in diversified sectors; or if adopted dynamically, clients’ fund can be allocated to diversified areas, with relevant market trends taken into consideration in order to reduce overall investment risk and to increase overall investment return,” Gibb added.
The growing popularity of the US Exchange Traded Funds (ETFs) in recent years has served as evidence of the trend. ETF was originally a passive investment tool, while with the development of big data, it has been transformed into a more active and less costly way of investment. ETF had been gradually replacing the largest fund market.
The future development of big data and AI will also have a major impact on matching mechanism.
“If there are clear rating standards on the asset end and lots of automatic investment tools with detailed understanding of individual investor, the market can actually achieve automatic matching,” Gibb said.
The automatic matching mechanism can greatly reduce labor costs as well as financial costs and bring about inclusion in financial services.
“In the past five years, we think the internet has only exerted its influence on channel extension, but big data will affect the whole financial market in all aspects in the next five years. Firstly, AI and big data will enhance market transparency and standardization and support development in real economy; secondly, it can help investors to attain more diversified investment strategies, and clients can attain higher returns. This is a relatively big help to the society as a whole, yet a great challenge for the financial sector in itself,” Gibb said.
Gibb also put forward that there could be induced-risk coming along with big data and AI. Firstly, short-term accuracy does not guarantee the long-term one; secondly, as large number of transactions will be assigned to computerized automation mechanism, this can bring different problems.
“We cannot conclude that large data and AI must necessarily be good. We must be careful in controlling risk,” Gibb added.
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