Generative AI technologies have demonstrated significant potential across diverse applications. This study provides a comparative analysis of credit score modeling techniques, contrasting traditional approaches with those leveraging generative AI. Our findings reveal that current generative AI models fall short of matching the performance of traditional methods, regardless of the integration strategy employed. These results highlight the limitations in the current capabilities of generative AI for credit risk scoring, emphasizing the need for further research and development before the possibility of applying generative AI for this specific task, or equivalent ones.

Data serves as the fundamental foundation for advancing deep learning, particularly tabular data presented in a structured format, which is highly conducive to modeling. However, even in the era of LLM, obtaining tabular data from sensitive domains remains a challenge due to privacy or copyright concerns. Hence, exploring how to effectively use models like LLMs to generate realistic and privacy-preserving synthetic tabular data is urgent. In this paper, we take a step forward to explore LLMs for tabular data synthesis and privacy protection, by introducing a new framework HARMONIC for tabular data generation and evaluation. In the tabular data generation of our framework, unlike previous small-scale LLM-based methods that rely on continued pre-training, we explore the larger-scale LLMs with fine-tuning to generate tabular data and enhance privacy. Based on idea of the k-nearest neighbors algorithm, an instruction fine-tuning dataset is constructed to inspire LLMs to discover inter-row relationships. Then, with fine-tuning, LLMs are trained to remember the format and connections of the data rather than the data itself, which reduces the risk of privacy leakage. In the evaluation part of our framework, we develop specific privacy risk metrics DLT for LLM synthetic data generation, as well as performance evaluation metrics LLE for downstream LLM tasks. Our experiments find that this tabular data generation framework achieves equivalent performance to existing methods with better privacy, which also demonstrates our evaluation framework for the effectiveness of synthetic data and privacy risks in LLM scenarios.

When a customer overdraws their account and their balance is negative they are assessed an overdraft fee. Americans pay approximately \$15 billion in unnecessary overdraft fees a year, often in \$35 increments; users of the Mint personal finance app pay approximately \$250 million in fees a year in particular. These overdraft fees are an excessive financial burden and lead to cascading overdraft fees trapping customers in financial hardship. To address this problem, we have created an ML-driven overdraft early warning system (ODEWS) that assesses a customer's risk of overdrafting within the next week using their banking and transaction data in the Mint app. At-risk customers are sent an alert so they can take steps to avoid the fee, ultimately changing their behavior and financial habits. The system deployed resulted in a \$3 million savings in overdraft fees for Mint customers compared to a control group. Moreover, the methodology outlined here can be generalized to provide ML-driven personalized financial advice for many different personal finance goals--increase credit score, build emergency savings fund, pay down debut, allocate capital for investment.

When credit card giant American Express began offering bank accounts for the first time last year, it had a foundation of fraud detection to bring to an entirely new product arena. That meant in some cases, the company could port over AI and machine-learning models used to spot phony identities or dodgy transactions for its credit card products to its consumer and business checking accounts. But it's been a process, and now, AmEx plans to invest in bringing additional AI techniques used to protect against credit card fraud to its banking products. "We have models which run to detect whether it's you or whether somebody else is logging into your account. Very straightforwardly, we transferred it to the banking product," said Abhinav Jain, vice president for Global Fraud Decision Science at AmEx, who is responsible for the company's fraud detection models.

Artificial intelligence is still a somewhat new technology, but it already has the capacity to offer real-world business outcomes for financial institutions in the form of productivity improvement, cost savings and happier customers. Despite all of AI's potential, banks and credit unions remain cautious when it comes to this technology, mostly because they misunderstand the process, investment and outcome. As experts in banking AI, we know that AI has come a long way and can dispel even the ardent disbeliever. All of these advantages explain why Gartner found that, throughout the COVID-19 crisis, many organizations have actually increased their investments in AI. Yet, banking institutions still want to better understand all the factors at play when making a decision.

While traditional banks advertise a 0.05% interest rate for deposits in their savings accounts, a growing number of consumers have turned to a new crop of mobile apps--automated or "self-driving" savings apps--to help them save. In a recent consumer study, Cornerstone Advisors found that savings apps like Acorns, Digit, and Qapital help consumers save an average of $600 a year above and beyond their regular level of savings--and one in five users saves more than $1,000. At a 0.05% interest rate, you'd need $1.2 million in a savings account in order to earn $600 in a year. These new apps help consumers figure out how much they could save (above and beyond what they're already saving)--and then take the money out of the user's checking account and put it in a savings account. The popularity of these tools shouldn't come as a surprise.

The ranks of fintechs using Banking-as-a-Service to attract small business users to their sophisticated apps just got a jolt. An elephant by the name of Intuit just entered the room. But it's not just fintechs that are looking over their shoulder. Thousands of community financial institutions that consider small business relationships their bread and butter are -- or should -- be taking notice. Intuit, with its popular QuickBooks accounting software, is arguably the best-known brand among the small business community.

A sign of Google is seen at Google's stand during the annual meeting of the World Economic Forum ... [ ] (WEF) in Davos, on January 21, 2020. In an article titled Amazon's Impending Invasion Of Banking, I wrote: "Amazon has no incentive to cut banks out of the lending or deposit business. Amazon can make more money by providing technology services to help financial institutions underwrite, process, and service loans. Banks will gladly pay for this, because Amazon will do it for a lower cost that what banks incur to do it today." My argument then, as it is now, is that Amazon is poised to be a vendor--not a competitor--to financial institutions.

Alphabet is using its dominance in the search and advertising spaces -- and its massive size -- to find its next billion-dollar business. From healthcare to smart cities to banking, here are 10 industries the tech giant is targeting. With growing threats from its big tech peers Microsoft, Apple, and Amazon, Alphabet's drive to disrupt has become more urgent than ever before. The conglomerate is leveraging the power of its first moats -- search and advertising -- and its massive scale to find its next billion-dollar businesses. To protect its current profits and grow more broadly, Alphabet is edging its way into industries adjacent to the ones where it has already found success and entering new spaces entirely to find opportunities for disruption. Evidence of Alphabet's efforts is showing up in several major industries. For example, the company is using artificial intelligence to understand the causes of diseases like diabetes and cancer and how to treat them. Those learnings feed into community health projects that serve the public, and also help Alphabet's effort to build smart cities. Elsewhere, Alphabet is using its scale to build a better virtual assistant and own the consumer electronics software layer. It's also leveraging that scale to build a new kind of Google Pay-operated checking account. In this report, we examine how Alphabet and its subsidiaries are currently working to disrupt 10 major industries -- from electronics to healthcare to transportation to banking -- and what else might be on the horizon. Within the world of consumer electronics, Alphabet has already found dominance with one product: Android. Mobile operating system market share globally is controlled by the Linux-based OS that Google acquired in 2005 to fend off Microsoft and Windows Mobile. Today, however, Alphabet's consumer electronics strategy is being driven by its work in artificial intelligence. Google is building some of its own hardware under the Made by Google line -- including the Pixel smartphone, the Chromebook, and the Google Home -- but the company is doing more important work on hardware-agnostic software products like Google Assistant (which is even available on iOS).

For anyone who has followed CES 2020, one of the announcements that created the most buzz was Samsung's NEONs (neo-humans), or AI assistants that resemble humans. These NEONs are extremely lifelike--so much so that when you look at them, it can be hard to believe they aren't real people on the other end of the video. But will lifelike AI assistants really be the future? What practical use will they have if they are? And will these AI assistants find their way into the enterprise or just become nothing more than a consumer-focused gimmick?