From minting coins to dispensing greenbacks on ATMs, the love affair between money and machine goes a long way back. The pervasive influence of technology in how we create, exchange and store money treads a colorful history — replete with culture-shifting innovations such as cash registers, magnetic credit cards and mobile wallets.

The romance is far from over. With recent advances in machine learning, computer vision, voice recognition, natural language processing and other areas of artificial intelligence, the chemistry between money and machine is just warming up. Accenture recently reported that the vast majority of industry insiders believe AI will become the “primary” channel through which banks and their customers will interact within the next three years.

Tell-tale signs are all over the place:

Capital One recently launched an NLP-capable chatbot named Eno at the wake of other industry firsts in terms of AI applications. Eno enables customers to chat with the bank using text-based natural language to pay bills and retrieve account information. Among the pioneering financial institutions to join the IoT bandwagon, Capital One also launched an Alexa Skill for Amazon Echo and plans to be the first to launch a similar service for Microsoft’s Cortana.

Mastercard is leveraging AI to enhance experiences across its ecosystem (for consumers, partners, issuers, and merchants), as well as to hike operational efficiency and close loopholes in areas such as fraud and false declines. The financial services giant worked with AI enabler Kasisto to build chatbots for banks and merchants to better engage consumers. Taking a step further, Mastercard also partnered with IBM’s Watson and General Motors to explore scenarios where users can “safely” conduct commerce even while on the road (such as pre-ordering their favorite cup of coffee and picking it up from the drive-through counter on their way to the office).   

Thomson Reuters has used complex algorithms for years to organize their humongous stores of information and generate time-critical financial market data for institutional clients. Their use of AI centers on improving the experience of knowledge workers by building systems that not only answer questions on structured data (e.g., give me all age discrimination cases in California in the last four years) but also provide proactive insights for decision makers (e.g., making a comparative macroeconomic analysis of two countries or showing differences and trade offs between two investment options). As a leading content publisher, Thomson Reuters also uses machine learning and AI to detect and identify fake news. The Reuters News Tracer leverages an algorithm that looks at more than 700 factors to determine whether a trending topic on social media is factual or not. 

Wells Fargo leveraged machine learning originally to prevent fraud, but AI now permeates other areas including compliance, customer experience, underwriting and authentication. The San Francisco-based financial institution is also exploring the use of consumer-facing virtual assistants for information updates, improvements in transaction capabilities and business insights.  Bipin Sahni, EVP and Head of Innovation and R&D, reports that “regularly collaborating with startups on a wide range of technologies helps us explore big ideas outside our walls.” 

After unleashing Watson on Jeopardy and offering it as an AI platform for companies in the healthcare, travel and other industries, IBM retrofitted its question-answering and NLP-capable brainchild for the financial sector in December 2016. Beta-launched as IBM Watson for Cybersecurity, the program already attracted dozens of clients, including key players in the insurance and banking industries. As reported by Business Insider, global firms such as Sumitomo Mitsui Banking Corp. and Sun Financial will help test Watson’s ability to identify and combat cyberattacks. In addition to accurately detecting suspicious behavior, Watson’s machine learning component will help improve its ability to interpret and analyze cybersecurity data over time.    

Even personal finance and wealth management have come to favor artificial intelligence, as shown by the rapid spread of AI-driven mobile apps, the comparatively higher success rates of quantitative trading and the cost-efficiency of robo advisors. Robo advisors such as Wealthfront and WiseBanyan are automated portfolio management services that use computer algorithms to manage and grow customer investments.

Banks and other financial institutions have been using computer automation to improve operational efficiency for decades. With big data and AI technologies on the uptrend, the development of smarter, better and more powerful automations will profoundly transform the industry. From providing regulatory compliance requirements to expediting reports generation, AI will become a pervasive force across all the components of a financial organization.   

 

The human minds behind artificial intelligence

TOPBOTS interviewed key industry leaders to make sense of the profound changes triggered by different applications of artificial intelligence in the world of finance. These influencers illuminated the details of how AI research and development work in their respective organizations.   

For Margaret Mayer, VP of Software Engineering at Capital One, customer needs and feedback set the tone for any AI initiative at the company. She said, “We use analytics in general to look for customer patterns, whether that be spending patterns or concerns about fraud.”

Mayer’s colleague, AI Design Head Steph Hay believes context is the biggest design challenge. Hay described the importance of context as the sweet spot between design and technology. According to Hay, AI developers often shift between two opposite directions: one that leverages the company’s massive datasets to build a common template for customer interactions; and  another that needs to be resolved as a unique scenario involving a single customer and the bank.  She emphasized her focus on how to make “better assumptions and provide smarter responses based on everyone’s data, not just your data.”

Meanwhile, Thomson Reuters’ VP of Research and Development Khalid Al-Kofahi has been a domain expert for his entire career and seeks to demystify the buzz around AI. He recounted the evolution of AI at the company as programmatic functions driven by analytics and designed to establish a specific user experience. For Al-Kofahi, the most difficult thing in AI development is “finding the right problem and scoping it right.” Otherwise, talent, time and other organizational resources can easily be wasted.

Mastercard’s forays into AI-focused research and partnerships stem from its corporate mandate to “enable commerce across every device and environment,” according to Kiki Del Valle who serves as SVP at the firm’s Commerce for Every Device unit. Scheduled for a year-end rollout, the company’s joint project with IBM and General Motors will use voice recognition technology and NLP to locate nearby establishments and make quick, effortless transactions. While AI has tremendous potential, Del Valle cautions organizations against over exposure: “Unless it’s solving a problem or addressing a specific need, then don’t jump into AI for the sake of it,” citing the crucial importance of scalability from the point of view of issuers, merchants and other stakeholders.

Wells Fargo currently works with promising AI startup Kasisto to enhance customer experiences via a conversational AI service. Head of Innovation Group Steve Ellis sees virtual assistants helping in three key areas: information updates, transaction capabilities, and customer insights. “We see a big future here,” he says, “and that’s why we recently formed our new Artificial Intelligence Enterprise Solutions team within the Innovation Group.”

 

Banking on AI today

Artificial intelligence already has significant imprint in many areas of banking such as fraud detection, insurance underwriting, customer service, back office operations and wealth management. Because it demonstrably improves efficiencies, reduces costs, generates smarter insights and provides better user experiences, AI will eventually be used industry-wide at an even deeper and broader scale. The only thing to expect are new and surprising use cases as bot and bank become one. 
 

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