This semi-monthly column highlights news, government documents, NGO/IGO papers, industry white papers and reports, academic papers and speeches on the subject of AI’s fast paced impact on the banking and finance sectors. The chronological links provided are to the primary sources, and as available, indicate links to alternate free versions.

NEWS:

PC Tech – OP-ED, August 22, 2024: Artificial Intelligence the Key to Greater Financial Inclusion. With the integration of artificial intelligence and machine learning, that people’s banking experiences will be so customized as to have different experiences between customers.

Yahoo via Bloomberg, August 23, 2024. Mortgage giant Rocket seeks AI-fueled rebound after bumpy period – The past year has been a tumultuous one for Rocket Cos., one of the nation’s largest mortgage lenders: the firm reported its first annual loss as a public company, navigated a historic drop-off in mortgage originations and replaced its chief executive officer. But for new CEO Varun Krishna, the downturn has turned into a chance to reshape how Rocket does home lending. His bet, an expensive one, is that artificial intelligence can fix a process that makes half of buyers cry and turn his company into the dominant force in a market that has long seen cutthroat competition between hundreds of firms. Plenty stands in the way of that ambition. The plunge in refinancings last year dropped Rocket to third place in the closely watched mortgage rankings, and rivals are also pouring money into technology in a bid to win over customers. Many factors are out of the firm’s control, as stubbornly high rates and home prices threaten an extended slowdown in the mortgage market.

VentureBeat. August 25, 2024. AI and employment: Echoes of the past or a new paradigm?

PC Tech – OP-ED, August 22, 2024: Artificial Intelligence the Key to Greater Financial Inclusion. With the integration of artificial intelligence and machine learning, that people’s banking experiences will be so customized as to have different experiences between customers.

CoinTelegraph, August 20, 2024. Hong Kong launches generative AI Sandbox for finance sector. Hong Kong’s Gen A.I. sandbox is a platform to test AI’s potential applications in finance, including risk management, anti-fraud, customer services and process re-engineering. Carmen Chu, executive director at HKMA, explained that the Gen A.I sandbox provides a controlled environment for financial institutions and technology companies to jointly experiment with generative AI applications and obtain specific supervisory feedback. She added:

Explaining the initiative in further detail, Eddie Yue, the chief executive of the HKMA, said that the Gen A.I. sandbox will empower banks to pilot their novel Gen A.I. use cases within a risk-managed framework, supported by essential technical assistance and targeted supervisory feedback. He further urged banks to “make full use of this resource” and adopt generative AI tools into their business processes.

PAPERS:

Journal of Financial Economic Volume 160, October 2024, 103910; From Man vs. Machine to Man + Machine: The art and AI of stock analyses. Sean Cao, Wei Jiang, Junbo Wang, Baozhong Yang. https://doi.org/10.1016/j.jfineco.2024.103910 An AI analyst trained to digest corporate disclosures, industry trends, and macroeconomic indicators surpasses most analysts in stock return predictions. Nevertheless, humans win “Man vs. Machine” when institutional knowledge is crucial, e.g., involving intangible assets and financial distress. AI wins when information is transparent but voluminous. Humans provide significant incremental value in “Man + Machine”, which also substantially reduces extreme errors. Analysts catch up with machines after “alternative data” become available if their employers build AI capabilities. Documented synergies between humans and machines inform how humans can leverage their advantage for better adaptation to the growing AI prowess.

Springer Link. Blockchain for Ethical and Transparent Generative AI Utilization by Banking and Finance Lawyers. Swati Sachan, Vinicius Dezem & Dale Fickett. Conference paper. First Online: 10 July 2024. Generative AI tools powered by Large Language Models (LLMs) have attracted significant attention from the banking, finance, legal, and technology sectors due to their ability to generate and articulate coherent human-like text and images. Legal firms have raised ethical concerns regarding LLM’s ability to emulate legal reasoning, accountability of erroneous outcomes, and security and privacy of confidential legal data. To address these challenges, this research paper proposes a blockchain-based monitoring framework that ensures the responsible and secure application of Generative AI in drafting legal decisions by utilizing the anonymized output from an existing eXplainable Artificial Intelligence (XAI) algorithm within a law firm, which assists in legal decision-making. The lawyers are expected to comprehend explainable algorithmic decisions expressed in terms of probabilities and feature importance instead of textual explanations. The immutability and decentralization of blockchain technology form the basis of a transparent and tamper-proof record-keeping system. It ensures consistent and tamper-resistant responses by generative AI, which has been used by lawyers in the past. A case study on data security and tort liability claims on banking data breaches is presented to demonstrate the practical application.

Journal of Investment, Banking and Finance, April 2024. The Transformative Impact of AI in Finance and Banking DD Douglas. The transformative impact of artificial intelligence (AI) in finance and banking is pro7 found, revolutionizing the way financial institutions operate, interact with customers, and make 8 decisions. This article explores the technological landscape that has paved the way for AI adoption, including falling data storage costs, data availability, advancements in machine learning, cost reduction, regulatory compliance, competitive advantage, risk management, customer experience, fraud detection, increased connectivity, and rapid advances in AI technologies. It discusses how AI is enhancing customer support, improving security through fraud detection algorithms, and enhancing credit scoring accuracy through machine learning. The value creation potential of AI in banking includes unlocking up to $1 trillion of incremental value annually through personalized services, cost reduction via automation, and uncovering new opportunities. Additionally, it provides examples of AI applications in banking, including personalization, automation, and insight extraction, showcasing how AI is transforming the industry. Despite these advancements, the article acknowledges challenges such as a lack of clear strategy, legacy systems, and fragmented data assets and proposes solutions like promoting a growth mindset and responsible AI deployment. It also addresses current issues in the banking sector, such as cyberattacks, voice cloning, and fraud, emphasizing the importance of AI in addressing these challenges. Overall, the article highlights the transformative potential of AI in finance and banking, urging banks to embrace an AI-first mindset for sustained growth and innovation.

NBER – Government as Venture Capitalists in AI. Martin Beraja, Wenwei Peng, David Y. Yang & Noam Yuchtman Working Paper 32701 DOI 10.3386/w32701 Issue Date July 2024. Venture capital plays an important role in funding and shaping innovation outcomes, characterized by investors’ deep knowledge of the technology, industry, and institutions, as well as their long-running relationships with the entrepreneurship and innovation community. China, in its pursuit of global leadership in AI innovation and technology, has set up government venture capital funds so that both national and local governments act as venture capitalists. These government-led venture capital funds combine features of private venture capital with traditional government innovation policies. In this paper, we collect comprehensive data on China’s government and private venture capital funds. We draw three important contrasts between government and private VC funds: (i) government funds are spatially more dispersed than private funds; (ii) government funds invest in firms with weaker ex-ante performance signals but these firms exhibit growth rates exceeding those of firms in which private funds invest; and (iii) private VC funds follow government VC investments, especially when hometown government funds directly invest on firms with weaker ex-ante performance signals. We interpret these patterns in light of VC funds’ traditional role overcoming information frictions and China’s unique institutional environment, which includes important frictions on mobility and information.

IMF – Impact of AI on Singapore’s Labor Market: Singapore August 13, 2024 Khan, A Shujaat. Singapore is well-prepared for AI adoption but stands highly exposed to the increasing use of artificial intelligence (AI) technologies in the workplace, due to a large share of skilled workforce. While half of the highly exposed segment of the labor force stands to benefit from the appropriate use of AI to complement their tasks, potentially boosting their productivity, the other half may face greater vulnerability to AI’s disruptive effects due to lower levels of AI complementarity. Estimates suggest that women and younger workers are more exposed to the effects of AI, which, in the absence of appropriate policies, could worsen income inequality in Singapore. Targeted training policies, leveraging on the existing SkillsFuture program, can harness AI’s potential. Additionally, focused upskilling can mitigate the disruptive impact of AI on vulnerable workers.

ArXiv, April 2024. Integrating Generative AI into Financial Market Prediction for Improved Decision Making. Chang Che, Zengyi Huang, Chen Li, Haotian Zheng, Xinyu Tian. This study provides an in-depth analysis of the model architecture and key technologies of generative artificial intelligence, combined with specific application cases, and uses conditional generative adversarial networks ( cGAN ) and time series analysis methods to simulate and predict dynamic changes in financial markets. The research results show that the cGAN model can effectively capture the complexity of financial market data, and the deviation between the prediction results and the actual market performance is minimal, showing a high degree of accuracy.

Finance and Accounting Research Journal, Vol. 6 No. 3. 2024. Integrating AI With Blockchain for Enhanced Financial Services Security. DOI: https://doi.org/10.51594/farj.v6i3.855
Olubusola Odeyemi Independent Researcher, Chicago USA
Chinwe Chinazo Okoye Access Bank Plc, Nigeria
Onyeka Chrisanctus Ofodile Sanctus Maris Concepts, Nigeria Ltd
Omotayo Bukola Adeoye Independent Researcher, Chicago, IL, USA
Wilhelmina Afua Addy Independent Researcher, Maryland, USA
Adeola Olusola Ajayi-Nifise Department of Business Administration, Skinner School of Business, Trevecca Nazarene University, USA

Integrating artificial intelligence (AI) with blockchain technology presents a transformative approach to enhancing security in financial services. This fusion leverages the strengths of both AI and blockchain to mitigate various security risks, including fraud, data breaches, and identity theft, thereby bolstering trust and confidence in financial transactions. This abstract explores the synergies between AI and blockchain, highlighting their combined capabilities, applications, and potential benefits for the financial services industry. AI algorithms, including machine learning and natural language processing, empower financial institutions to analyze vast amounts of data in real-time, identifying patterns, anomalies, and suspicious activities indicative of fraudulent behavior. By integrating AI-powered fraud detection systems with blockchain-based transactional networks, organizations can enhance security and transparency throughout the entire financial ecosystem. Blockchain’s immutable ledger ensures the integrity and traceability of transactions, while AI algorithms provide advanced analytics and predictive insights to detect and prevent fraudulent activities effectively. Furthermore, AI-driven identity verification and authentication systems enhance security in digital transactions by accurately verifying user identities and detecting unauthorized access attempts. By integrating AI-based biometric authentication with blockchain-based identity management solutions, financial institutions can streamline customer onboarding processes, enhance security, and protect sensitive information from unauthorized access. Moreover, AI-powered smart contracts automate and enforce the execution of contractual agreements, reducing the risk of fraud, errors, and disputes in financial transactions. By combining AI-driven smart contract platforms with blockchain technology, organizations can facilitate secure, transparent, and tamper-proof transactions, eliminating intermediaries and reducing transaction costs. The integration of AI with blockchain also offers opportunities for regulatory compliance and risk management in financial services. AI-powered regulatory compliance solutions analyze vast amounts of regulatory data, identify compliance risks, and ensure adherence to regulatory requirements. By integrating these solutions with blockchain-based regulatory reporting systems, financial institutions can enhance transparency, auditability, and regulatory oversight, fostering trust and compliance in the financial ecosystem. In conclusion, the integration of AI with blockchain technology holds immense potential for enhancing security in financial services. By harnessing the combined capabilities of AI and blockchain, organizations can detect and prevent fraudulent activities, enhance identity verification and authentication, streamline transaction processes, and ensure regulatory compliance. This abstract underscores the transformative impact of integrating AI with blockchain on security in financial services, paving the way for a more secure, efficient, and trusted financial ecosystem.