The application of Machine Learning & Artificial Intelligence in the Financial Industry
Financial Advisor or Data Advisor？
In recent times, the financial service industry is increasingly using Artificial Intelligence (AI) and machine learning (ML) in a wide range of applications across the financial system. Most experts agreed that AI and ML have considerable potential benefits for financial institute including cost saving, improve efficiency as well as enhance the service quality. However, some practitioners also debate that it does not comply with the ethical issues as well as humanity. So, how does FinTech transform the financial industry? What is the future trend for this industry?
1. Chat-bot
Based on Natural Language Processing (NLP) and Intelligent Knowledge Based System (IKBS), Chat-bot has already applied in many financial service institutions. The Chat-bot can understand customers’ questions asked by spoken language through a variety of communication channel, search the accurate answer based on the IKBS and provide real-time reply based on NLP. The service including a balance inquiry, bank account details, loan queries etc.[1] It not only improved service efficiency, reduced waiting time, standardized answer but also reduced the human cost to achieve both costs decreasing and benefit increasing targets. According to Gartner, by 2020 chatbots will be handling no less than 85% of all customer service interactions.[1]
2. New credit assessment system
Based on behavior data analysis and social media analysis, the AI credit assessment system can set up a credit system to identify people’s credit. It solved the problem for those who want to borrow micro-loans but without enough financial records to identify their credit in developing country. The system also improves the credit assessment efficiency and reduce the default risk by more accurate assessment. This credit assessment system has a wide application in Chinese micro-loan industry like Yirendai(NYSE:YRD), PPDai(NYSE: PPDF)andChina Rapid Finance(NYSE: XRF). They provide P2P lending service and assess lender by new credit assessment to ward off risks, reduce the lending assessment time as well as lower the lending barrier in China.
As another example in EU, considering the EU General Data Protection Regulation (GDPR) that implemented on May 2018, it requires firms to provide a comprehensive and clear explanation to users on the data that is collected from them, how the firm uses this data, and who it shares this data with [2]. Firms are also required to gain explicit consent from users for retaining and processing this data. As the EU consumer, people begin to control their own data, including finance, consumption, traveling, social media, etc. Individuals become to be the supplier of their own data and take part in the data transactions independently. This regulation will totally transform the economic and business model for the individual’s digital data transaction.
3. AI Financial advisor and trend of AI fund
For the application in AI financial service, AI financial advisor can obtain customers’ real-time, scenario-based investment need and build up multi-dimension behavior characteristics through customers’ ‘1 porch’ personal data access. ‘1 porch’ means the 1st party authenticated, permission-based, on-demand, real-time, contextual, holistic personal data. Based on this personal data, a financial advisor can provide personalized wealth management advice. Some of the AI trend reports have already predicted that GDPR regulation would have a significant influence on the transformation of the industry. The report — AI in the UK: ready, willing and able? published by Select Committee on Artificial Intelligence, House of Lords in the UK has mentioned the light of GDPR’s requirement would change the game of many industries and they also mentioned the most advanced personal data transaction system: Solid from MIT and Hub of All Things from Cambridge. Both of the firms aimed to set standards and decentralized personal data, allowing an individual to decide where to keep their data and how their data used or exchange it in return for money or benefits [2]. By combining the static investment preference and dynamic investment opportunity, AI financial advisor can improve the insight of investment preference and provide a better asset matching management. This process will reduce the portfolio management fee, lower the barrier of investment and achieve the target of high efficiency, low fee with a broad group of investors.
The application of AI in the hedge fund industry is still at an early stage. Some hedge fund managers utilizing AI as a partial input into their trading process, whilst other ‘pure AI hedge funds using deep learning and make the decision independently according to the prediction with minimal input from the fund manager. The Quant transforming from top-down approach to a bottom-up approach, which leaves the AI fund manager to self — learning, adapting and predicting itself. In June 24th 2016, the Japanese AI hedge fund — Simplex Equity Futures Strategy Fund used self-learning that had successfully forecasted Britain was going to leave the European Union and sold Japanese stock-index futures before a sizable market advance ended the day, with a 3.4% gain during the period of biggest drop in Japanese shares in five years. According to the manager Mr. Nomura, the fund used self-learning model, keeping improve the predictive power and becoming more intelligent over time. [3]
However, the conflict appears between the High Net Worth Individuals (HNWI) with an ordinary investor. Before the AI financial advisor appears, only HNWI group could have the personalized portfolio management service and the entry barrier for PE or Trust fund investment is very high [4]. The private equity funds run by famous names such as Blackstone, Apollo, and Carlyle, which may require a minimum investment of $1m, $5m or more, only the very wealthiest group can afford [5]. However, the HNWI client’s power will be reduced and the financial company’s bargaining power will be improved after AI financial advisor emerging and developed, every investor may have the chance to get the same service in the future which will harm HNWI’s interest. It would become easier for fund management firms to gain capital with a lower investment barrier and firms can focus more on enhancing the fund performance instead of dealing with obtaining capital.
The most important of all, investor pass their money to financial institutes to invest based on their trust. According to the main forecast — the personal data would create value and can be exchanged in return for money and benefit, does the financial institute prepared to take over value of personal data as a new category of money? Does the financial institute begin to transform from financial data analysis to personal data analysis? How to deal with the conflict between HNWI and ordinary investor when the barrier is breaking down？
Reference:
1. Meet 11 of the most interesting chatbot in banking
https://thefinancialbrand.com/71251/chatbots-banking-trends-ai-cx/
2. Artificial Intelligence Committee
AI in the UK: ready, willing and able?
https://publications.parliament.uk/pa/ld201719/ldselect/ldai/100/10002.htm
3. Japanese hedge fund robot outsmarts human master, passes Brexit test
4. Artificial Intelligence: The new frontier for hedge funds
http://www.eurekahedge.com/Research/News/1614/Artificial-Intelligence-
5. Private equity begins to entice ordinary investors
https://www.ft.com/content/e85240c4-b150-11e4-831b-00144feab7de
