The finance sector is among the keenest adopters of machine learning (ML) and artificial intelligence (AI), the predictive powers of which have been demonstrated everywhere from back-office process automation to customer-facing applications. AI models excel in domains requiring pattern recognition based on well-labeled data, like fraud detection models trained on past behavior. ML can support employees as well as enhance customer experience, for example through conversational AI chatbots to assist consumers or decision-support tools for employees. Financial services companies have used ML for scenario modeling and to help traders respond quickly to fast-moving and turbulent financial markets. As a leader in AI, the finance industry is spearheading these and dozens more uses of AI.
In a highly regulated, systemically important sector like finance, companies must also proceed carefully with these powerful capabilities to ensure both compliance with existing and emerging regulations, and keep stakeholder trust by mitigating harm, protecting data, and leveraging AI to help customers, clients, and communities. “Machine learning can improve everything we do here, so we want to do it responsibly,” says Drew Cukor, firmwide head of AI/ML transformation and engagement at JPMorgan Chase. “We view responsible AI (RAI) as a critical component of our AI strategy.”
Understanding the risks and rewards
The risk landscape of AI is broad and evolving. For instance, ML models, which are often developed using vast, complex, and continuously updated datasets, require a high level of digitization and connectivity in software and engineering pipelines. Yet the eradication of IT silos, both within the enterprise and potentially with external partners, increases the attack surface for cyber criminals and hackers. Cyber security and resilience is an essential component of the digital transformation agenda on which AI depends.
A second established risk is bias. Because historical social inequities are baked into raw data, they can be codified—and magnified—in automated decisions leading, for instance, to unfair credit, loan, and insurance decisions. A well-documented example of this is Zip code bias. Lenders are already subject to rules that aim to minimize adverse impacts based on bias and to promote transparency, but when decisions are produced by black-box algorithms, transgressions can occur even without intent or knowledge. Laws like the EU’s General Data Protection Regulation and the U.S. Equal Credit Opportunity Act require that explanations of certain decisions be provided to the subjects of those decisions, which means financial firms must endeavor to understand how the relevant AI models reach their results. AI must be understood by internal audiences too by ensuring, for example, that AI-driven business-planning recommendations are intelligible to a chief financial officer or that model operations are reviewable by an internal auditor. Yet the field of explainable AI is nascent, and the global computer science and regulatory community has not determined precisely which techniques are appropriate or reliable for different types of AI models and use cases.
There are also macro risks related to the health of the economic system. Financial companies applying data-driven AI tools at scale could create market instability or incidents such as flash crashes through automated herd behavior if algorithms implicitly follow similar trading strategies. AI systems could even functionally collude with each other across organizations, such as by bidding to achieve the highest or lowest price for a stock, creating new forms of anticompetitive behavior.
Toward responsible AI
Most AI risks are not, however, unique to financial services. Companies from media and entertainment to health care and transportation are grappling with this Promethean technology. But because financial services are highly regulated and systematically important to economies, firms in this sector have to be at the frontier when it comes to good AI governance, and proactively preparing for and avoiding known and unknown risks. Currently, banks are familiar with using governance tools like model risk management and data impact assessments, but how these existing processes should be modified in light of AI’s impacts remains an open conversation.
Enter responsible AI (sometimes called
By: MIT Technology Review Insights
Title: Deploying a multidisciplinary strategy with embedded responsible AI
Sourced From: www.technologyreview.com/2023/02/14/1066582/deploying-a-multidisciplinary-strategy-with-embedded-responsible-ai/
Published Date: Tue, 14 Feb 2023 18:00:00 +0000
Procurement in the age of AI
Procurement professionals face challenges more daunting than ever. Recent years’ supply chain disruptions and rising costs, deeply familiar to consumers, have had an outsize impact on business buying. At the same time, procurement teams are under increasing pressure to supply their businesses while also contributing to business growth and profitability.
Deloitte’s 2023 Global Chief Procurement Officer Survey reveals that procurement teams are now being called upon to address a broader range of enterprise priorities. These range from driving operational efficiency (74% of respondents) and enhancing corporate social responsibility (72%) to improving margins via cost reduction (71%).
To meet these rising expectations, many procurement teams are turning to advanced analytics, AI, and machine learning (ML) to transform the way they make smart business buying decisions and create value for the organization.
New procurement capabilities unlocked by AI
AI and ML tools have long helped procurement teams automate mundane and manual procurement processes, allowing them to focus on more strategic initiatives. But recent advances in natural language processing (NLP), pattern recognition, cognitive analytics, and large language models (LLMs) are “opening up opportunities to make procurement more efficient and effective,” says Julie Scully, director of software development at Amazon Business.
The good news is procurement teams are already well-positioned to capitalize on these technological advances. Their access to rich data sources, ranging from contracts to invoices, enables AI/ML solutions that can illuminate the insights contained within this data. Acting on these insights unlocks new capabilities that can enhance decision-making and improve spending patterns across the organization.
Predicting supply chain disruptions. In an era of constant supply chain disruptions, procurement teams are often faced with inconsistent item availability, which can negatively impact employee and customer experience. Indeed, the Deloitte 2023 Global Chief Procurement Officer survey finds that only 25% of firms are able to identify supply disruptions promptly “to a large extent.”
AI tools can help address this issue by recognizing patterns that indicate an emerging supply shortage and automatically recommending two or three product alternatives to business buyers, thereby preventing supply disruptions. These predictive capabilities also empower procurement teams to establish buying policies that proactively account for items that are more likely to go out of stock.
Answering pressing questions quickly. Sifting through data to understand the cause of a supply chain disruption, product defect, or other risk is time-consuming for a procurement professional. LLM-powered chatbots can streamline these processes by understanding complex queries about orders and “putting together a nuanced answer,” says Scully. “AI can query a wide variety of sources to fully answer a question quickly and in a way that feels natural and understandable.” In addition to providing fast and accurate answers to pressing questions, AI promises to reduce the need to explain procurement issues eventually. Instead, it will proactively analyze orders, buying patterns, and the current situation to provide instant support.
Offering customized recommendations. As business buyers increasingly demand personalized experiences, procurement officers seek ways to customize their interactions with business procurement systems. Scully provides the example of an employee tasked with hosting a holiday party for 150 employees who needs help deciding what to order. An AI-based procurement tool posed that scenario, she says, could generate a proposed shopping cart, sifting through “millions and billions of data points to recommend and suggest items that the employee may not have even thought of.”
Better yet, she adds, “as we get into really large language models, AI/ML can help answer questions or help buy items you didn’t even know you needed by understanding your particular situation in a much more detailed way.”
Influencing compliance spend. Procurement professionals aim to balance employees’ freedom to purchase the items they need with minimal intervention. However, self-sufficiency should not come at the cost of proper spend
By: MIT Technology Review Insights
Title: Procurement in the age of AI
Sourced From: www.technologyreview.com/2023/11/28/1083628/procurement-in-the-age-of-ai/
Published Date: Tue, 28 Nov 2023 16:00:00 +0000
Did you miss our previous article…
The Download: COP28 controversy and the future of families
This is today’s edition of The Download our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
Why the UN climate talks are a moment of reckoning for oil and gas companies
The United Arab Emirates is one of the world’s largest oil producers. It’s also the site of this year’s UN COP28 climate summit, which kicks off later this week in Dubai.
It’s a controversial host, but the truth is that there’s massive potential for oil and gas companies to help address climate change, both by cleaning up their operations and by investing their considerable wealth and expertise into new technologies.
The problem is that these companies also have a vested interest in preserving the status quo. If they want to be part of a net-zero future, something will need to change—and soon. Read the full story.
How reproductive technology can reverse population decline
Birth rates have been plummeting in wealthy countries, well below the “replacement” rate. Even in China, a dramatic downturn in the number of babies has officials scrambling, as its population growth turns negative.
So, what’s behind the baby bust and can new reproductive technology reverse the trend? MIT Technology Review is hosting a subscriber-only Roundtables discussion on how innovations from the lab could affect the future of families at 11am ET this morning, featuring Antonio Regalado, our biotechnology editor, and entrepreneur Martín Varsavsky, founder of fertility clinic Prelude Fertility. Don’t miss out—make sure you register now.
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 Instagram recommends sexual content to adults that follow kids
Test accounts were served risqué posts and disturbing videos. (WSJ $)
Meta was aware it had millions of underage users, a complaint alleges. (NYT $)
2 The first transatlantic flight powered by alternative fuels has taken off
Waste fats and corn leftovers are fueling the flight between London and New York. (BBC)
Here are the key phrases you need to know to understand climate change. (Vox)
Everything you need to know about the wild world of alternative jet fuels. (MIT Technology Review)
3 The United Arab Emirates planned to strike oil deals during COP28
Which doesn’t seem terribly climate-friendly. (BBC)
AUAE AI firm is believed to have covertly worked with Chinese companies. (NYT $)
China’s own carbon emissions are on course to peak soon. (Economist $)
4 Starlink can only operate in Gaza with Israel’s approval
That’s according to Elon Musk, who is visiting Israel currently. (FT $)
5 Foxconn is struggling to build iPhones in India
So the manufacturer started shipping over skilled workers from China. (Rest of World)
6 The world’s banana supply is in serious trouble
A deadly fungus is sweeping through crops—and there’s no known cure. (Bloomberg $)
7 Digital car keys don’t always work the way they’re supposed to
Which is a major problem if you can’t guarantee your vehicle is secure. (The Verge)
8 It’s not just you—dating is tough
But these tips can help to make it a less harrowing experience. (WP $)
Here’s how the net’s newest matchmakers help you find love. (MIT Technology Review)
9 Big dogs don’t live that long
But biotech company Loyal is hoping to change that with an experimental drug. (Wired $)
These scientists are working to extend the life span of pet dogs—and their owners. (MIT Technology Review)
10 The quiet bliss of living in an internet-free home
And how you can achieve it, too. (The Atlantic $)
How to log off. (MIT Technology Review)
Quote of the day
“He ignored me royally, which is his privilege. And he lost almost all the money that he had invested.”
—Christine Lagarde, president of the European Central Bank, explains to students in Frankfurt how one of her sons lost his money on crypto, despite her repeated warnings, Reuters reports.
The big story
Are you ready to be a techno-optimist again?
Back in 2001, MIT Technology Review picked 10 emerging areas of innovation that we promised would “change the world.” It was a time of peak techno-optimism.
By: Rhiannon Williams
Title: The Download: COP28 controversy and the future of families
Sourced From: www.technologyreview.com/2023/11/28/1083923/the-download-cop28-controversy-and-the-future-of-families/
Published Date: Tue, 28 Nov 2023 13:10:00 +0000
Finding value in generative AI for financial services
With tools such as ChatGPT, DALLE-2, and CodeStarter, generative AI has captured the public imagination in 2023. Unlike past technologies that have come and gone—think metaverse—this latest one looks set to stay. OpenAI’s chatbot, ChatGPT, is perhaps the best-known generative AI tool. It reached 100 million monthly active users in just two months after launch, surpassing even TikTok and Instagram in adoption speed, becoming the fastest-growing consumer application in history.
According to a McKinsey report, generative AI could add $2.6 trillion to $4.4 trillion annually in value to the global economy. The banking industry was highlighted as among sectors that could see the biggest impact (as a percentage of their revenues) from generative AI. The technology “could deliver value equal to an additional $200 billion to $340 billion annually if the use cases were fully implemented,” says the report.
For businesses from every sector, the current challenge is to separate the hype that accompanies any new technology from the real and lasting value it may bring. This is a pressing issue for firms in financial services. The industry’s already extensive—and growing—use of digital tools makes it particularly likely to be affected by technology advances. This MIT Technology Review Insights report examines the early impact of generative AI within the financial sector, where it is starting to be applied, and the barriers that need to be overcome in the long run for its successful deployment.
DOWNLOAD THE REPORT
The main findings of this report are as follows:
Corporate deployment of generative AI in financial services is still largely nascent. The most active use cases revolve around cutting costs by freeing employees from low-value, repetitive work. Companies have begun deploying generative AI tools to automate time-consuming, tedious jobs, which previously required humans to assess unstructured information.
There is extensive experimentation on potentially more disruptive tools, but signs of commercial deployment remain rare. Academics and banks are examining how generative AI could help in impactful areas including asset selection, improved simulations, and better understanding of asset correlation and tail risk—the probability that the asset performs far below or far above its average past performance. So far, however, a range of practical and regulatory challenges are impeding their commercial use.Legacy technology and talent shortages may slow adoption of generative AI tools, but only temporarily. Many financial services companies, especially large banks and insurers, still have substantial, aging information technology and data structures, potentially unfit for the use of modern applications. In recent years, however, the problem has eased with widespread digitalization and may continue to do so. As is the case with any new technology, talent with expertise specifically in generative AI is in short supply across the economy. For now, financial services companies appear to be training staff rather than bidding to recruit from a sparse specialist pool. That said, the difficulty in finding AI talent is already starting to ebb, a process that would mirror those seen with the rise of cloud and other new technologies.
More difficult to overcome may be weaknesses in the technology
By: MIT Technology Review Insights
Title: Finding value in generative AI for financial services
Sourced From: www.technologyreview.com/2023/11/26/1083841/finding-value-in-generative-ai-for-financial-services/
Published Date: Mon, 27 Nov 2023 01:00:00 +0000
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