Lately it has been all artificial intelligence, all of the time.
Reports, surveys, commentaries, all extolling the wondrous AI potential for all sorts of business and banking applications, have proliferated in recent weeks and months. Post an article about AI on the web and the Twitterverse will tweet and retweet it for weeks.
Frankly, such potential is, well, potentially wondrous. AI promises the ability to improve customer experience, detect money laundering and fraud, streamline operations, and reduce costs.
Yellow flag on AI from Gartner
More on all this in a bit. Just to put it in perspective though, Gartner Inc. tosses up a caution flag even while predicting that AI technologies will be in almost every new software product by 2020.
Jim Hare, research vice-president at Gartner, says: “AI offers exciting possibilities, but unfortunately, most vendors are focused on the goal of simply building and marketing an AI-based product rather than first identifying needs, potential uses, and the business value to customers.”
In other words, there’s a danger that AI will quickly grow into a gee-whiz type of technological fad that everyone will want to have, without having any idea what to do with it. (If you have an iPhone, do you have Siri activated?)
Even as the AI surge intensifies, Gartner offers these observations and suggestions:
• Lack of differentiation is creating confusion and delaying purchase decisions.
To build trust with end-user organizations, vendors should focus on building a collection of case studies with quantifiable results achieved using AI.
• Proven, less-complex machine-learning capabilities can address many end-user needs.
Vendors should use the simplest approach that can do the job, over cutting-edge AI techniques.
• Organizations lack the skills to evaluate, build, and deploy AI solutions.
If they had a choice, most organizations would prefer to buy embedded or packaged AI solutions rather than try to build a custom solution.
Business awakes to AI
That’s what vendors should take to heart, according to Gartner. But what about those end users, namely banks and businesses in general?
To start this discussion it’s necessary to establish the fact that the corporate world really is rapidly waking up to what AI can do.
Here are just a few bits of research that bear this out:
• PwC says in an April white paper that more than $5 billion in a total of 605 venture capital deals in the past two years went to AI endeavors.
“AI is poised to have a transformative effect on consumer, enterprise, and government markets around the world,” it says, noting that 67% of the business decision makers it surveyed see the future potential of AI to automate processes and optimize business efficiency and labor productivity.
• Accenture says in a June report that businesses that successfully apply AI could increase profitability by an average of 38% by 2035.
It found financial services in the top three industries that would benefit the most, following only information and communication, and manufacturing.
“Artificial intelligence will revolutionize how businesses compete and grow, representing an entirely new factor of production that can ignite corporate profitability,” says Paul Daugherty, chief technology and innovation officer at Accenture.
• The IBM Institute for Business Value reported that half of the firms surveyed plan to adopt cognitive computing (a form of AI) by 2019, anticipating a 15% return on investment.
The results, reported in July, came from research among more than 6,000 business executives from across industries.
• Promontory Interfinancial Network reported in a June survey of bank leaders that “using AI in the banking industry has been rather slow compared to other industries.”
Then it says: “However, survey results indicate that the pace of adoption may be quickening. Fifty-four percent of bank leaders expect to see AI systems become a familiar part of American banking in less than five years, and, of that number, about 10% think it will happen in two years or less.”
Where to plug it in first?
So, if these leaders were to heed the Gartner advice, to what applications would AI help the most, at least early on?
First, a real-world—albeit generalized—example. In July IBM announced it is working with Danske Bank, headquartered in Denmark, to integrate the first IBM Watson-based platform built on the IBM Cloud to augment human intelligence.
Says IBM: “It will allow Danske to easily integrate existing systems and can be further used for development and deployment of new financial services. Danske Bank’s use of the platform is aimed at ensuring uninterrupted banking operations and enabling a significant decrease of the number of incidents impacting business-critical applications and end users.”
To put a finer edge on this, Stephen Reiser, vice-president and partner in IBM’s Financial Services Practice, relates in a separate IBM blog what Watson can do specifically in marketing.
“With incisive understanding of customer needs,” he writes, “banks can: make personalized recommendations, engaging customers with the right solution, at the right time, and through the right channel; measure customer sentiment in social media and survey data to gain a deeper awareness of needs, wants, and intention; identify customers with comparable issues and proactively reach out to resolve their issues; maximize personalized upsell and cross-sell opportunities.”
To be sure, other providers are all on top of this. Just a few examples:
• Garima Chaudhary, Oracle Financial Services financial crime and compliance management specialist, points out in an Oracle blog how AI can help in transaction monitoring, case management, suspicious transaction reporting, and analytics as part of a single platform.
• Mastercard is acquiring Brighterion, Inc., proficient in AI and machine learning technologies, to expand the payment company’s suite of capabilities that deliver an enhanced customer experience and security.
• Infosys now offers Infosys Nia, an AI platform it says will tackle business issues such as predicting revenues, forecasting what products need to be built, understanding customer behavior, deeply understanding the content of contracts and legal documents, understanding compliance, and combatting fraud.
These just skim the surface of all the AI-related offerings that already are out there or are soon to be announced. Still, it would be prudent to keep in mind Gartner’s warnings about jumping blindly on the AI bandwagon.
Your organization and AI
So, just because IBM is really, really involved with this emerging technology, it’s worthwhile to read these suggestions from its Institute for Business Value. It says businesses ought to:
• Envision the future—Outline an 18-to-24-month digital strategy for adopting AI with a limited set of initiatives that paves the way for smaller, more exploratory investments with finite objectives and time frames.
• Think [or “ideate” as the institute says]—Focus on thorough and periodic assessments in the market and with target users. Experiment and educate the rest of the enterprise on how cognitive capabilities are being used.
• Incubate and scale—During the shift from planning and design to execution, apply [AI] to specific use cases, rapidly explore and prototype solutions to solve specific and measurable business challenges.
Bottom line, says IBM: Monitor the business case value realization and make adjustments, as necessary.
Sources used for this article include:
- The Fintech Capital: UK vs US
- Lessons Learned: What Other Countries Can Teach the U.S. About Open Banking
- What Bankers Can Learn From Voice Assistants
- Cybersecurity Start-Up Secures $23 Million in Funding Led by Intel Capital
- Banks and Other Financial Corporations Provide Double the Pension Contributions of Other Industries