AI, which is about the ability of machines to perform intelligent tasks, such as recognizing faces, understanding speech, and playing games, has been around for a while. Generative AI is breaking new ground with its ability to create new and human-like content, such as images, music, text, videos, or bespoke financial services for the underbanked and unbanked.

While conventional AI may primarily focus on recognizing and interpreting patterns within large datasets to perform specific, predefined tasks, generative AI not only recognizes patterns and trends from large datasets but also uses this understanding to create new data that is similar to the actual data it learned from. So, it provides additional capabilities that can build on and supplement existing conventional AI technologies rather than replace them and, in so doing, improve the user experience.

The McKinsey report highlights the banking industry as one of the sectors that could see the most significant impact of using AI. They estimate that it could generate value from increased productivity of 2.8% to 4.7% of the industry's annual revenues or an additional $200 billion to $340 billion.

On top of that impact, generative AI tools can enhance customer satisfaction, improve decision-making and employee experience, and decrease risks through better monitoring of fraud and risk.

With this new technology, financial institutions can tailor services to individual needs, enhance financial education, broaden credit access, and make serving marginalized communities more cost-effective.

Customer verification is a vital entry point into financial services, and AI can enhance this process with technologies such as facial recognition, voice verification, and document scanning. Generative AI can further break down barriers by providing multilingual text-based or voice support, thus simplifying the onboarding for those who speak other languages or have disabilities.

AI-driven chatbots and virtual assistants can guide newcomers through initial procedures, offering always-on, real-time responses and support. One of the strengths of generative AI is its ability to analyze customer data to craft tailored financial products such as loans, insurance, and savings accounts.

This customization is achieved through innovative design and prototype testing, which boosts customer satisfaction and extends services to those traditionally excluded from banking. MIT Technology Review Insights reports that such chatbots not only augment the productivity of customer service agents but also significantly improve consumer satisfaction.

Moreover, AI expedites information processing from customer inputs, thereby accelerating account setup and reducing the need for extensive manual labor. It can also evaluate transaction patterns to generate risk profiles, ensuring adherence to anti-money laundering and know-your-client directives.

AI-powered credit scoring systems provide lending to underserved people in emerging markets by using machine learning and data science to create personalized and dynamic credit scores for each customer. Such solutions can also be designed to include generative AI features to improve the credit scoring system.

The credit scoring system can be enhanced with generative AI, which will help create diverse, challenging scenarios for better stress testing. This leads to better model validation using synthetic data, less bias, and clearer explanations. Additionally, it ensures more balanced datasets, with improved proportions of default and non-default cases, and enhances data quality and outlier detection.

Using such information will allow financial institutions to identify new patterns and anomalies with associated risks at a micro level—such as the potential for an individual to default—and a broader one—such as market trends. 

In the realm of security, while facial and voice recognition technologies verify transactions and user identities, generative AI can amplify fraud detection by creating synthetic data that mimics genuine fraud patterns. This enhances the detection capabilities of machine learning tools by providing them with a richer dataset to learn from.

Financial literacy is a pivotal aspect of both individual and societal prosperity, equipping people with the necessary awareness to manage finances. Generative AI can craft dynamic and bespoke financial education resources, guiding users through the fundamentals of financial management.

When combined with engaging strategies like gamification, generative AI can produce and deliver content and financial services that are both enjoyable and tailored to client needs, including those who are underbanked and unbanked.

Financial institutions can utilize their extensive financial data, combined with the capabilities of generative AI, to provide tailored guidance, nudge customers towards eco-friendly spending habits, and monitor their progress in reducing carbon emissions. They can also incentivize investments in sustainable energy and efficient technologies through benefits like reduced interest rates or extended loan repayment schedules.

While generative AI represents a significant advancement, it also introduces challenges and risks that must be mitigated.

The effectiveness of generative AI depends on the availability of extensive and diverse datasets for training and evaluating its models to produce accurate and pertinent outputs. Nonetheless, such data may be scarce or of poor quality in developing regions due to issues like fragmentation, gaps, inconsistencies, inaccuracies, and lack of standardization.

Gathering and distributing data can lead to ethical and legal dilemmas. One pressing concern is data ownership. Determining who has the right to use, share, or monetize data can be complex, especially when multiple parties are involved. Informed consent is another ethical issue. In many cases, cultural or language barriers may complicate the process of obtaining meaningful consent.

Privacy is also a significant issue, even for benevolent intents, such as improving financial inclusion. Protecting sensitive information and complying with data protection measures are becoming major concerns. Like all technology enablement, it should be introduced with a competent risk management framework, combined with a privacy impact assessment.

Furthermore, generative AI can pose security and trust issues for financial service providers and their clients, as it can potentially create fraudulent or manipulative content, such as sophisticated forgeries, counterfeit identities, and fabricated news. They can accelerate and refine the execution of scams, by making it easier to send phishing messages, create online footprints that mimic real users through false identities, and streamline the replication of another individual's actions for deceit and the collection of confidential and sensitive data. This could harm the reputation and reliability of financial institutions and their customers, breaching ethical norms and legal requirements.

There is also the risk that generative AI may perpetuate and exacerbate existing prejudices and systemic discrimination found within the datasets and algorithms it employs, leading to biased outcomes like unequal pricing, access, and service. This could have discriminatory effects on various groups based on gender, race, ethnicity, or income level.

And it may complicate regulatory compliance and oversight for financial institutions and governing bodies. This can include producing content that contradicts or circumvents current laws, such as those about intellectual property, consumer rights, and anti-money laundering statutes. Or introduce novel and intricate risks that could threaten the financial sector's stability and robustness, including systemic vulnerabilities and the potential for widespread impact across the industry.