AI in Fintech

8 Nov, 2018 21:20
source: Singularity Financial November 8, 2018

Artificial Intelligence (AI) has developed rapidly in recent years, and its application level is becoming more and more widespread. The financial market has a large number of standardized large data, which is naturally suitable for the development of AI. In recent years, AI has attracted much attention in the financial field. In the whole process of financial business, from customer service at the front desk to financial transaction at the middle desk, and then to risk prevention and control at the back desk, AI can participate in it. AI has changed and will continue to change the traditional financial model. Artificial intelligence has the advantages of cost saving, rapid processing of massive data and continuous optimization of output effect. It can be applied to market analysis, customer service, personal investment consultant, loan approval and other scenarios in finance.

1. Advantages of AI

1.1 Cost Saving

One of the key drivers for such swift adoption of AI in the financial industry is cost savings. By 2030, traditional finance sector organisations could reduce their costs by 22%, according to fintech research company Autonomous Next, in what would amount to more than $1 trillion in efficiencies. These savings would be found across front, middle and back office operations with, for example, a reduction in retail branches and bank tellers, the application of AI to compliance and data processing, and the automation of underwriting and collections systems.

1.2 High-speed Data Processing

IN THE WORLD OF FINANCE, there’s been such an explosion of data collected over the past decade that even those twenty-something analysts working around the clock don’t stand a chance of being able to process it all. But machines might. Bloomberg, FactSet Research Systems, and Thomson Reuters have all developed an array of data science tools and techniques—including machine learning, deep learning, and natural language processing (NLP)—to quickly unearth valuable insights for thousands of financial professionals. Bloomberg was a pioneer of sentiment analysis (an example of NLP), which it began developing around a decade ago, in which machine-learning techniques are used to flag a news story or tweet as being relevant to a stock and assign a sentiment score.

1.3 Continuous Optimization

The system based on artificial intelligence, through pure rational machine learning and quantitative analysis, can greatly improve the effect of information analysis and reduce the deviation caused by irrational factors of human staff. Bank of America Merrill Lynch and Morgan Stanley are among the bigger players in an emerging discipline known (awkwardly) as quantamental analysis. They aim to combine the quantitative processing for which basic A.I. is best suited (basically, the capacity to spot patterns in gargantuan loads of data) with additional algorithms trained in the sophisticated analysis associated with super smart humans—assessing, say, the growth potential of an industry or the strategic acumen of a company’s management. Machine learning could eventually enable a quantamental system to learn from its mistakes.

2. Application of AI in Finance

2.1 Market Analyzing

AI technology and machine learning can also be used to scan data flows in real time, quickly analyse a huge amount of data and then filter it according to a complex set of criteria. As futuristic as it sounds, this technology is already integrated into ZCN’s analytical platform – CryptoEYE. It is designed to be a primary source of information about cryptocurrency markets: online price quotations for 1200+ digital assets, graphs for proprietary Zichain indices, a comprehensive CryptoWiki database of coins and tokens with all the essential metrics and a customizable news feed. This last module utilizes our groundbreaking AI and Big Data technology to scan the web for information and present it in the form of a news feed that is tailored for users’ needs and interests and prioritizes news pertinent to your investment portfolio and trading strategy.

2.2 Customer Service

Chat robot, a customer service solution based on artificial intelligence, will greatly change the customer service mode of enterprises. Customer service in major enterprises is increasingly replaced by artificial intelligence automatic reply. It has become the past to call for a long time to ask a simple question. A large number of enterprises have emerged to provide AI customer service outsourcing services for enterprises. We believe this trend will continue as advances in AI and machine learning will allow chat bots to answer ever more complicated queries, potentially making the idea of a call center redundant in the course of the next decade.

2.3 Personal Investment Consulting

Artificial intelligence is capable of using a bunch of parameters (your investment goals, risk appetite, existing portfolio, etc.) to provide you with ideas for potential investments that you may want to consider. The service of a financial advisor that used to be available only to the rich clients of Wealth Management offices would now be offered to all market participants.“ROBO-ADVISER” SERVICES, offered by startups like Betterment and traditional discount brokerages like Charles Schwab, are already using A.I. to serve the investing masses. Their low-fee investment tools rely on algorithms to determine how your assets should be split among stocks, bonds, and other assets, based on your needs and your stomach for risk.

2.4 Automatic Information Verification

Artificial intelligence makes information verification more rapid. One of the key technologies is face recognition technology. Tencent has already started using AI facial recognition technology in Shandong province to help with social security payments. Some elderly citizens in the northern coastal province used to have to spend a day on the road getting to their respective government offices to claim their social security benefits, he said, just to make sure no one else falsely claimed the money. But now, with the help of AI, they can just input their facial images into the system and the verification process can be done remotely after that.

Face recognition technology also helps to speed up the payment process. Tencent’s Face Recognition Payment (FRP) has provided face recognition payment solutions for off-line retailers such as Yonghui Supermarket, Carrefour Supermarket and Bubugao Supermarket. Alipay has also formally released its payment function in its APP. Payment method will complete the change of cash-credit card-mobile payment-face-brushing payment, which greatly improves the convenience of consumer finance.

2.5 Loan Approving

With the continuous development of financial science and technology, AI has begun to penetrate into complex loan approving in the financial industry. Due to the huge application volume, Tencent Weilidai, Ant Huabei & Jiebei, Jingdong Baitiao & Jintiao have adopted and will develop more intelligent machine learning algorithms to assist or even replace human beings to complete this highly repetitive lending work. The financial one-off account of Ping An Group exports a complete set of intelligent loan solutions named Gamma for small and medium-sized financial institutions. The intelligent loan solutions have many products, such as anti-fraud platform, scorecard, intelligent loan machine, etc. They cover the pre-loan, in-loan and after-loan processes of loan business, and greatly promote the development of the intelligent loan solutions.

References:

https://blog.coinspectator.com/2018/11/04/ai-in-finance-from-science-fiction-to-modern-financial-solutions/

https://www.forbes.com/sites/huawei/2018/10/23/financial-services-taking-ai-to-the-next-level/#52a356bd7858

http://fortune.com/2018/10/22/artificial-intelligence-ai-business-finance/

https://www.financeasia.com/News/448090,why-tencent-thinks-ai-could-transform-finance.aspx