Part 2: Generative AI Playbook — For Banking: Generative AI Overview

Published in

AI for Diversity

5 min readMay 9, 2024

Written by Aruna Pattam, Head — Generative AI Analytics & Data Science, Insights & Data, Asia Pacific region, Capgemini.

In the fast-paced world of technology, the emergence of artificial intelligence (AI) has been a game-changer, shaping the future of various industries and revolutionizing the way we interact with digital environments. The journey of AI, from its inception in the 1950s to the present day, reflects an evolutionary path marked by significant milestones, each building upon the last to create increasingly sophisticated systems.

This article delves into the evolution of AI, highlighting key developments from basic AI to Machine Learning (ML), Deep Learning (DL), and the groundbreaking realm of Generative AI. We will explore how these technologies differ from traditional programming and classical machine learning, shedding light on their unique capabilities, particularly in the banking sector.

Join me as I unravel the intricacies of Generative AI, its types, and its transformative impact across various applications, marking a new era in artificial intelligence.

Generative AI Overview

Artificial Intelligence (AI)

In 1950s the term “Artificial Intelligence” was coined, marking the beginning of AI as a formal field of academic research. This marks a pivotal stride in technology, characterized by the creation of machines designed to mimic human intelligence. This innovation is foundational in developing systems that can execute tasks typically requiring human cognition, such as understanding natural language and recognizing patterns. An exemplary instance of AI deployment is the AI-powered chatbots in banking to automate customer service, offering 24/7 support for queries like account details and transactions.

Machine Learning (ML)

The concept of “Machine Learning” emerges in 1980s, a subset of AI, takes the concept further by empowering machines to learn from data and improve their performance over time without being explicitly programmed for each task. For example, Machine Learning (ML) in banking can personalise customer experiences by analysing spending patterns, account balances, and transaction histories to recommend tailored financial services. This approach improves customer satisfaction, boosting engagement and loyalty with offers that align closely with individual financial needs.

Deep Learning (DL)

In 2000s, Deep Learning starts gaining prominence, an advanced branch of ML, utilizes complex neural networks to model and interpret vast amounts of data. DL is instrumental in applications requiring the identification of intricate patterns or predictions, such as fraud detection systems. These systems scrutinize transaction data for irregular patterns, helping financial institutions minimize risk and protect consumers from fraudulent activities.

Generative AI

Late 2010s saw the emergence of Generative AI, which encompasses DL techniques, and is at the forefront of creating new, original content, including texts, images, and videos. In the realm of marketing, this capability allows for revolutionizing banking marketing by producing original content such as personalized financial advice videos, customized promotional images, and targeted social media posts.

Large Language Models (LLM)

Large Language Models, a core component of Generative AI, have significantly advanced text generation and understanding. In banking, large language models exemplifies this by powering chatbots that provide customers with coherent, context-aware financial advice, answer banking queries, and even generate informative articles on financial literacy.

How is it different from Traditional Programming?

Generative AI represents a significant leap beyond traditional programming and classical machine learning, transforming the way machines generate and process content.

Traditional programming relies on explicit instructions provided by humans to perform specific tasks. In this paradigm, developers write detailed code that dictates every action the computer must take, with outcomes entirely dependent on predefined rules. For example, a calculator program is coded with explicit instructions on how to perform arithmetic operations based on user input.

Classical machine learning, meanwhile, moves a step forward by enabling computers to learn from data and make decisions with minimal human intervention. This approach involves feeding data into algorithms that learn patterns and make predictions. A spam filter is a classic example, where the algorithm is trained on a dataset of emails to identify and filter out spam based on learned characteristics.

Generative AI diverges radically from these approaches by not just making predictions or decisions based on data but by creating entirely new data that resembles the input data it was trained on. It uses complex models, such as deep neural networks, to generate text, images, or music that can be indistinguishable from content created by humans. For instance, Generative AI can craft realistic virtual financial advisors that provide personalized advice, simulating human interaction.

This evolution reflects a shift from deterministic outputs based on rigid rules, through pattern recognition and prediction, to the autonomous creation of new, original content, marking a new era in the capabilities of artificial intelligence.

Refer to this link to learn more about how Generative AI.

Generative AI Types:

The landscape of Generative AI is rich with diverse models, each tailored to unique tasks and capable of mimicking real-world data in remarkable ways.

Generative AI Types — Among the key players in this field are Auto-Encoders, Generative Adversarial Networks (GANs), Diffusion Models, and Transformer Models, all of which have carved out their niches by leveraging distinct mechanisms and applications.

Auto-Encoders excel in refining images, particularly in medical imaging, by compressing and reconstructing data.

GANs, through a generator-discriminator duo, craft highly realistic images, enhancing data sets for training and testing.

Diffusion Models generate intricate visuals by systematically reducing noise.

While Transformer Models, using self-attention, adeptly handle language, understanding and generating text with nuanced context.

These models collectively advance AI’s ability to create, clarify, and comprehend data across multiple domains.

Conclusion:

The trajectory of artificial intelligence from its foundational stages to the sophisticated prowess of Generative AI signifies a landmark transformation within the banking and financial sector. This evolution transcends mere technological progress, heralding a fundamental shift in the manner in which financial institutions leverage AI to understand data, learn from it, and innovatively generate new content.

As the banking industry delves deeper into the potentials of Generative AI, it approaches the threshold of a new era — a future where AI’s capacity to emulate, enhance, and even surpass human ingenuity in creating solutions is virtually limitless.

The path forward for AI in banking is not solely about automating tasks or enhancing operational efficiency; it’s about charting new territories in personalized banking experiences, securing financial data, and crafting services that were once deemed futuristic.

With Generative AI, the sector is poised to redefine the boundaries of what is possible, venturing into realms of innovation and service delivery that promise to transform the very essence of banking as we know it.