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Artificial Intelligence, Customer Centricity and Banking’s Social Contract. By Bradley Leimer, Managing Director and Head of Fintech Strategy, EACThe concepts fueling the development of artificial- intelligence applications are hardly new. Attempts to understand how the brain thinks—how decisions are made, how it saves and processes memories, and how it leverages connected learning systems—have been perplexing humans since before the times of the classic philosophers. Soon after brilliant English mathematician Alan Turing published his groundbreaking 1. Computing Machinery and Intelligence”, the academic discipline aimed toward one day surpassing human intelligence was born. The term artificial intelligence (AI) itself was coined by Boston- born computer scientist John Mc. Carthy in 1. 95. 6, as he and his peers established research labs at MIT (Massachusetts Institute of Technology) and Stanford.

· I cover security and privacy for Forbes. I’ve been breaking news and writing features on these topics for major publications since 2010. As a freelancer. Some of the most brilliant minds in the field of AI will spend the day discussing the stakes of Deep Learning and Machine Intelligence. Followed by exchanges on.

The current wave of AI startups owes much to the decades of research and experimentation developed by these teams and those that followed. As it pertains to financial services and the creation of new sources of efficiency and customer value, however, the application of cognitive computing, machine learning and AI is, in many ways, a greenfield. Global financial- services firms are currently undergoing costly digital transformations as they innovate a centuries- old service model. Existing providers are being challenged by external venture investments threatening their customer relationships and revenue streams.

While banking has a history of resisting or delaying deployment of modern methodologies—agile development, open source, cloud computing, platforms—leveraging artificial intelligence is one area that the industry appears to be readily embracing. The twin catalysts: investment and innovation. The influx of new firms focused on AI is simply staggering. There are now more than 2,3.

AI startups globally, half of which did not exist just two years prior. In regard to investment, the number of venture transactions funding AI startups increased nearly 1. Last year, startups leveraging AI as a central component of their business raised a little over $5 billion, a number that is expected to double to more than $1. CB Insights). Technology giants such as GAFA (Google, Apple, Facebook, Amazon) are investing tens of billions more. Intense competition for people, products and patents has erupted between the United States, China and other tech centers.

Research firm Tractica forecasts that revenue generated from AI solutions will grow from $1. Given the rapid growth in the space, the numbers are likely to be much higher. Source: https: //www. Source: https: //www.

While not all of these companies are focused on financial services (we still need to improve self- driving cars, parcel shipments and Instagram selfies, after all), banks are increasingly looking at AI startups for sources of inspiration. One global bank that clearly sees the promise of AI and machine learning is the Spanish giant Banco Santander. Inno. Ventures, Santander’s $2. AI- focused startups. Pixoneye leverages stored photos to build customer profiles and unearth customer needs (photos of travel destinations might get you a travel- partner discount, for example). Gridspace analyzes voice patterns in real- time and determines the mood of the customer during service calls to adjust agent scripts accordingly. Curve links customers’ debit and credit cards to a single app and a Curve- branded Master.

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Card to optimize payments and better manage everyday spending. Inno. Ventures also invested in Elliptic, Socure and Personetics, all of which leverage customer data and machine learning. Adding more intelligent experiences into its digital- application stack will provide ways to engage its customers and differentiate its service offerings. Other banks have made notable investments across fintech—and like Santander, increasingly in AI. The most active European- based bank- led corporate investors are Santander, UBS, Deutsche Bank, Société Générale, BNP Paribas, Credit Suisse, HSBC, BBVA (Banco Bilbao Vizcaya Argentaria), Barclays, ING, Uni. Credit, RBS (Royal Bank of Scotland) and Crédit Agricole.

Interestingly, 7. European banks went to US- based fintechs. The top US- based bank investors include Citi, Goldman Sachs, JPMorgan Chase, Morgan Stanley, Wells Fargo, Bank of America, TD Bank, Capital One, US Bank and PNC. These American banks have participated in 7.

Clearly, these institutions are focused on combatting external threats and their evolving business models—but investment alone does not equal innovation. True innovation involves a long- term focus to apply these investments, partnerships and rapid internal developments toward services your customers love and are willing to pay for. Operational efficiency vs. Artificial intelligence and the umbrella of associated cognitive tools have proven to be a catalyst for both business- model reinvention and increased efficiencies. Advanced tools are casting a wide net of influence on financial- application development. These include improvements in KYC (know your customer), regulatory and compliance, portfolio management, algorithmic trading, security, collections and fraud detection, insurance and credit underwriting, customer service and engagement, predictive analytics, and product fulfillment.

Applications leveraging machine learning, deep learning, pattern recognition and natural- language processing can work together to simulate human- thought processes. With the rapid consumerization of artificial intelligence—seen through the rise of contextual digital experiences and voice- driven intelligent- response platforms, the way customers interact with financial brands is expected to shift significantly. Therein lies the rub. Should management teams focus on customer- facing improvements, or on internal efficiencies that are often invisible? While much of the current wave is focused on operational efficiencies, the most strategic executive teams are firmly focused on creating new forms of customer value and revolving revenue streams. Here is a selection of efforts that demonstrate the principles of optimization, and to a lesser extent, customer centricity.

JPMorgan Chase is leveraging AI to reduce costs and reduce legal research time by thousands of hours. The initial project, called COIN, for Contract Intelligence, interprets commercial- loan agreements and should save 3. The software reviews documents in seconds instead of hours, and runs 2. Chase also developed a proprietary AI- based trading platform (LOXM), which was trained on billions of historic transactions to execute equities trades at maximum speed at optimal prices. With Chase’s $9. 6 billion annual technology budget and digital developments, it is one bank to watch for further developments in the AI space. HSBC is undergoing a technology renaissance of sorts.

It is incorporating technology from Ayasdi to tackle fraudulent activities such as money laundering and KYC. HSBC is focused on gaining efficiencies from automating transactional- data analysis, and like several other banking entities, it seeks to gain a significant reduction in cost by eliminating false positives in fraud detection.

It has already seen a 2. Watch American Conjuring Mediafire there. Citi has invested in Feedzai to detect and deter fraud within transactional applications. Like HSBC, Citi is working with Ayasdi (and Cylance) to learn how to leverage machine learning across its international networks. Citi has invested in Clarity Money, which leverages machine learning to get customers to best allocate their non- allocated funds to earn better returns. With its focus on investment and partnership in the fintech space, Citi is a firm that will continue to demonstrate significant innovation in this area. Morgan Stanley is moving beyond the concept of robo- advisor to provide better tailored advice. It is investing in a platform to make its financial advisors more efficient.