Report: AI in Fintech

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Written by Samantha Thorne

Presenters: Paolo Sironi (FinTech Thought Leader, IBM Watson Financial Service), Sameer Gulati (COO, Lending Club), JinA Bae (Head of Corporate Venture, Hanwha Asset Management), Luchang Zheng (Founding Partner, Blockchain Hero), Gunnar Carlsson (Co-Founder and President, Ayasdi), Bill Reichert (Partner, FoundersX Ventures, Managing Partner, Garage Technology Ventures)

To read the full report click here for the digital edition.

FinTech has revolutionized the way that banks and insurance companies function. Rather than prioritizing themselves and their services as in the past, banks must emphasize client needs in today’s new technological era. This focus on personalized financial services manifests itself in FinTech—a financial infrastructure for consumer enablement. As FinTech applies data and technology to financial services in an effort to address industry challenges, artificial intelligence is essential to FinTech’s existence and usage.

The “AI in FinTech” panelists focused on how artificial intelligence has transformed financial services. Recognizing that financial technology startups must operate as tech companies while respecting financial regulations, the speakers underscored FinTech’s complexity. FinTech forsakes traditional banking to obsess over customers’ financial inclusion and credit accessibility and customization. This, in conjunction with FinTech’s five pillars of social media, analytics, artificial intelligence, blockchain and digitization, make FinTech companies face not only existing financial challenges, but security and regulatory dilemmas associated with artificial intelligence.

KEY TAKEAWAYS

Artificial intelligence enables FinTech to occur in real time. FinTech prioritizes financial inclusivity. To achieve this, real time plays an important role in FinTech’s ease of adoption as individuals with a smartphone gain access to quick, personalized and customized financial services. As AI steps in to disrupt the who, what, when and how of finance, as Bae notes, instantaneous decision-making and credit scoring will improve the availability of services in a real time basis.

AI creates deep personalization. Deep personalization in financial services allows FinTech to anticipate customer needs without the consumer having to act themselves. As artificial intelligence and machine learning generate and process individuals’ financial and nonfinancial data, Sameer Gulati notes that AI connects end users and FinTech companies to create continuous interaction. Artificial intelligence also helps evaluate lenders and debtors to speed up financial service processes and improve the customer experience. Because of the new type of relationship fostered with consumers at scale, AI redefines the concept of real time and applies it to finance.

FinTech prioritizes financial service speed. The benefit of FinTech is that it improves the customer’s financial services experience. As consumers prioritize speed and ease in their daily lives, so does FinTech. Technological applications such as mobile pay, Luchang Zheng recognizes, improve the efficiency and accessibility of financial transactions while quickening the pace of financial services. As individuals call for faster financial activities, FinTech is pressured to meet time demands by prioritizing that financial services are conducted on a real time basis.

FinTech changes financial services’ business model. FinTech is a business model innovation. As financial technologies prioritize information technology to innovate financial services, the financial industry must also become more innovative to keep apace with an increasingly more technological sector. As FinTech’s business model prioritizes peer-to-peer lending and aims to overcome legacy leadership—in which company leaders do not understand nor are motivated by technology and automation—entrepreneurship and technology play important roles.

Technology enables FinTech’s business model. FinTech’s fifth pillar, digitization, is central to its business model. Digitization has enabled innovators like Lending Club to capitalize on a technological approach to financial services and ultimately enable FinTech to be a category within the venture capital world. Technology has helped FinTech companies establish new business models as payment transactions occur by mobile phones and venture capitalists invest in financial technology companies.

Banks and FinTech companies do not pursue simple business collaboration. Noting that FinTech is much messier than “collaboration and kumbaya,” Bill Reichert underscored the role of Silicon Valley’s enterprising spirit and the financial industry’s ignorance in creating the current business model. The nature of financial services and their accompanying regulations tend to deter individuals that are familiar with financial proceedings from innovating within the industry. Reichert notes that many entrepreneurs set out to do “insane things” and through their more haphazard experiences with trial and error, the companies that survive either are adopted by large financial institutions that serve as mentors, or have disrupted the regulatory environment and business models enough to instigate progress within the industry.

AI regulatory applications can help manage financial risks. Financial crises are the greatest cause of job losses. Though many assign automation or regulation as the largest instigators of worker displacement, global financial crises have led to more unemployment over the past century than any other cause. As the financial industry endeavors to mitigate the risk of another recession, one questions whether AI can be applied at the highest regulatory level to manage financial risks while still innovating in financial services.

AI assists regulation to mitigate risks. Artificial intelligence and machine learning strive to minimize errors. As such, Reichert asserts that AI should play a role in the financial system’s overall governance. Currently, artificial intelligence only identifies strong signals. But to give AI a greater regulatory role in anticipating risks, systems need to capture weak signals as well. Carlsson notes that AI systems should identify smaller, underlying signals that are not primary drivers today, but could become strong signals and risks tomorrow. By doing so, artificial intelligence will apply its predictive powers to financial regulation.

China has benefited from its tech regulations. Although AI technology was born in the United States, Paolo Sironi notes that China has made great advancements in developing the technology, having the potential to become the largest tech owner in the future. As China has experienced continuous economic growth for the past 40 years and avoided financial recessions, the speakers link this to the nation’s regulatory environment. When compared to South Korea, JinA Bae highlights that China permits insurance companies and banks to pursue most financial activities unless explicitly stated. The opposite is true in South Korea where, unless defined as acceptable, most financial activities are restricted by the Korean government.

Overregulation can restrict innovation. Though regulation can help manage financial risks, it can adversely affect insurance companies, banks and conglomerates by stunting their innovative growth. Bae notes the challenges facing Hanwha Asset Management under South Korean regulation. After investing in the largest peer-to-peer lender in China and hoping to establish a joint venture together, the South Korean government essentially killed the P-to-P lending business within the country. However, Asian conglomerates are quite influential in terms of dealing with the government, providing opportunities to work with market leaders in advanced markets. Korean conglomerates help companies scale in Asia by convincing the government to open up its market over time.

AI and blockchain can overcome security dilemmas. Security dilemmas often accompany digitization. As AI continues to grow, so can its threat to data privacy and security. But while artificial intelligence can often contribute to security dilemmas, AI can also solve them. To ensure that technology overcomes security issues, machine learning and AI should be human-centric and recognize that humans often create security challenges and should thus be a part of their solution.

Financial services can be both digital and secure through AI usage. Machine intelligence software companies, such as Ayasdi, that build predictive models and automated applications using AI can overcome financial problems related to security. By using machine learning to segment populations and transactions based on levels of riskiness, artificial intelligence alerts investigators of fraudulent transactions, money laundering and threats to data protection. Gunnar Carlsson notes that alerting is not enough and that AI should enable investigators to predict when transactions are secure and when they are not, while recognizing that systems will not perfectly predict fraudulences, but can help in filtering cases.

Blockchain improves financial data transparency and traceability. As data in blocks cannot be unwritten and consequently cannot be tampered with, blockchain technology secures data. Blockchain also helps protect information and end user privacy by applying its zero-knowledge proof technology. Rather than extracting all information in the banking system when only some information is needed, Luchang Zheng notes that blockchain technologies extract only relevant information in an anonymous manner. Not only does blockchain shorten the data extraction process, but it secures the banking system as a whole.