The AI Revolution in Banking

Published on 21 Sep, 2018

AI is among the key trends that will reshape the banking industry going forward. Today, AI has evolved to play an integral role in many phases of the banking ecosystem, with a potential to make banks exponentially “smarter”. AI is not just another technology to enhance productivity – it is an industry disruptor with the potential to bring exceptional value to businesses and customers.

The AI Revolution in Banking

“I propose to consider the question, Can machines think?” – Alan Turing (1950)

Since the advent of computer science, computer builders and programmers have been fascinated by the thought whether or not computers and robots can literally be said to think. In his 1950 paper, Turing asked the question “Can machines think?” Six years later, research in the field of artificial intelligence (AI) started. At that time, many AI pioneers predicted that a machine as intelligent as a human would exist within two decades. Since then, AI research has witnessed several phases: optimism (1956–73); criticism and loss of funding, popularly known as “AI Winter” (1974–1982); resurgence of AI (1983–2010) and success and renewed funding (2011–current). Today, AI no longer appears to be a futurist vision or sci-fi myth. It is increasingly becoming a commercial reality, with new breakthroughs in technology and availability of high-powered computing at lower cost. As this technology matures, every business must ask itself the fundamental question: how will this disrupt my industry?

In this research piece, we focus on the adoption of AI in the banking sector, which, due to its high daily transaction volume, accurate historical records and quantitative nature, is best suited for AI disruption.

AI is reshaping the Banking industry as we know it

Banks have generally been early adopters of technology with the use of computers and internet. AI will transform the banking industry in a way similar to when computers first arrived in the industry. At that time, calculations and computation became easier and cheaper with the early adoption. Now with advent of AI, predictions and predictive technology will become ubiquitous. Today, AI has evolved to play an integral role in many phases of the banking ecosystem, with a potential to make banks exponentially “smarter”. “Smarter” in terms of delivering better insights, automating back-end workflows, and enhancing and personalizing customer experiences. Overall, the positive customer feedback creates a point of differentiation in the banking industry, leading to business growth and an increase in profits.

Banking and technology leaders agree that AI is among the key trends that will reshape the banking industry going forward. Big banks have introduced a wide array of AI technologies, including chatbots, personal assistants, and robo advisors, to revolutionize the traditional style of banking at the global level and gain competitive advantage. A few leading banks have already started jumping on the AI bandwagon using voice-powered devices (such as the Amazon Echo, Google Home and Apple’s Siri) to enhance customer experience and set themselves apart. According to Global Markets Insights Inc., AI in the banking and financial services industry is set to expand at a CAGR of more than 30% over 2018–24 to US$25 billion. We have explored possible applications of AI in the banking sector below.

AI for fraud detection is need of hour

The financial sector landscape is changing rapidly due to continuous digitization; technology has revolutionized the sector with omnipresent connectivity and seamless user experience. However, this has led to an increase in financial crimes as fraudsters have more data and means than ever before to execute frauds. Today’s fraudsters are more resourceful and continuously evolving tactics to evade fraud controls. The use of AI is the only way to keep up with resourceful fraudsters, as the sector needs a fraud detection system that would also evolve continuously.

While traditional fraud detection systems rely heavily on robust and complex sets of procedures, modern systems go beyond a checklist of risk factors: they actively learn and adjust to new potential (or real) security threats. Machine-learning algorithms process huge quantities of data to analyze and detect unique patterns or anomalies that may go unnoticed by traditional systems. AI-based tools scrutinize volumes of customer and transactional data and create customer profiles based on correlations established on historical transaction patterns; when an event or transaction inconsistent with such an established pattern occurs, the system raises a flag. AI-based advance fraud detection systems combine a variety of supervised and unsupervised methods in one system that makes it more effective than any single method.

However, the challenge for these systems is to avoid false-positives: situations where “risks” are flagged that were never risks in the first place. Nonetheless, with advances in machine learning, such systems are expected to improve efficiency in preventing fraud.

AI to disrupt traditional asset management landscape

Historically, active portfolio managers have found it difficult to outperform the market consistently, while typical first-generation robo-advisory platforms have struggled to deal with volatility in the market. An AI-driven asset management approach aims to overcome the hurdles faced by both traditional asset managers as well as the less sophisticated first-generation robo advisors. A typical first-generation robo advisor offers digital financial advice based on mathematical rules or algorithms. An AI-based asset management system, on the other hand, uses a mixed bag of techniques, including deep learning and machine learning, to examine years of financial research to create an extremely dynamic investment portfolio that can maximize gains and minimize losses, even during extreme market disruptions. These systems continuously process huge data to spot trends, access deeper insights and make real-world investment decisions. Adoption of an AI-based portfolio management platform can boost an asset manager’s productivity and profitability and also ease the burden of emotional investing.

AI is the future of trading

Algorithmic trading is the use of computer algorithms to determine trading strategies for optimal returns, place orders, and manage these orders post submission. Algorithmic trading systems largely have three components: the data, strategy, and trade execution handlers. This structure can be extended by integrating AI to construct a more intelligent algorithmic trading system, measurable by the degree to which the system is both self-adapting and self-aware

In addition to high-speed information networks, traders today rely on algorithmic trading systems to quickly generate and execute trading decisions. The objective is to execute the trades before other traders who possess similar information. High-frequency trading executes trades in sub-milliseconds. However, changes in market conditions would require manual intervention in statistical model. Hence, algo-traders aspire for solutions that offer dynamic models that can adapt to market changes using AI. Moreover, these smart systems can predict market trends based on alternative data such as news, weather updates, satellite images, videos, and geo-sensor data. Thus, with a high level of automation, markets in future are likely to be significantly different from what they are today.

AI chatbots are changing customer service channel

Chatbots are AI-based automated chat systems that interact and engage with customers, without human intervention. Built using AI and machine-learning technologies, chatbots can recognize multiple languages and accents and respond instantly, which makes these ideal for leveraging real-time communications to enhance overall customer satisfaction. The chatbots market is growing significantly due to the application of chatbots in customer service, social media, order processing, and marketing. According to a research report by Global Market Insights, the chatbots market is expected to grow from the current value of US$250.0 million to over US$1.3 billion by 2024. Customer service applications constituted the largest share of the chatbots market (43%) in 2017.

The banking industry has already started adopting chatbots to transform customer relationship management at a personal level. Elaborating on the benefits of chatbots in banking, these can offer 24/7 customer support, handle queries in real time, inform about new products, and create personalized products for customers based on their profiles.
Bank of America provides customers a virtual assistant Erica, which analyses customer data to offer suggestions on improving customer engagement. Erica provides balance and credit reports, sends notifications, suggests saving schemes, assists with transactions, and eases the paying of bills.

Underwriting gets AI upgrade

Underwriting is one of the main revenue-earning operations of banks and financial institutions. Banks perform the critical process of evaluating the creditworthiness of a potential customer, on the basis of which they decide whether or not to offer a loan to the said customer. To analyze the credit history of an individual, banks performs various operations such as evaluating the credit score, their past financial records and statements. With changing market situations, traditional credit-scoring techniques have fallen short of accurately evaluating consumers’ backgrounds. AI offers a new way to establish creditworthiness by analyzing non-traditional consumer data, such as social media activity and mobile-phone usage. It helps banks examine more abstract sources of information, such as SEC filings, social media postings, Yelp reviews, etc., and assimilate the relevant information to effectively evaluate the customer’s potential exposure. 

AI making automated KYC smarter

Banks and financial institutions are required to constantly maintain a high level of regulatory compliance. Consequently, a large number of records are involved in the lifecycle of a banking customer, from initial application to account management documents to deposits, withdrawals, several daily transactions, and loan documents - maintaining these generates reams of documentation. Moreover, in the banks’ bid to avoid the threat of non-compliance, the demand for AI in Know Your Customer (KYC) has increased over the past few years. To manage such vast documentation most effectively, the banking sector largely relied on many legacy systems. To provide better user experience, banks are using an effective tool: robotic process automation (RPA). With RPA, banks would be able to easily access customer information at the click of a button. AI permits these systems to re-examine and refine processes mechanically in response to user input, thus replacing banks’ heavy, complex, repetitive regulatory tasks. 

AI easing contract drafting/analysis process

Contract drafting, a common activity, needs to be done efficiently. Inefficient contract drafting can cost a firm 5–40% loss of value on a given deal, depending on conditions. In contracting/drafting contracts, banks could find it difficult to keep a track of details. There is no fast, orderly way to create/maintain a database of all the information in their contracts. AI can help in clarifying and extracting the content of such contracts. It could drag and arrange renewal dates and renegotiation terms faster from a large volume of contracts. AI contracting software trains its algorithm on a set of contracts to identify patterns and extract key variables such as clauses, dates, parties, etc

Endless possibilities

Although AI technology is still in the process of being developed, it has already begun solving real-world problems. The banking industry is one of the first sectors to adopt AI, but it has barely scratched the surface of its boundless potential. AI is set to transform the existing operational model of modern-day banks by redefining banking processes, for instance, how they engage with customers or create new products. AI is not just another technology to enhance productivity–it is an industry disruptor with the potential to bring exceptional value to businesses and customers. AI competencies are set to grow exponentially as technology solutions become smarter and efficient with continuous learning. However, banks must ensure that AI is used ethically and transparently. Several big banks have already started to focus on incorporating AI capabilities in their internal and/or external operations in order to secure their long-term prospects. While AI offers endless possibilities, whoever leverages it ethically, will gain competitive edge over others.

Article Exhibits

What is AI

Retail banking leaders in AI technologies

AI application in banking sector Key benefits and challenges

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