The generative AI market is expanding at a tremendous speed, no doubt about it. In 2022, it accounted for USD 10.5 billion and is projected to reach USD 208.8 billion by 2032. With so many new AI tools popping up regularly and fresh innovations happening every day, it’s hard to even keep track. At the beginning of 2023, there were 14,700 artificial intelligence startups in the U.S. alone. 

According to Gartner, generative AI will eventually become a general-purpose technology with an effect similar to that of the internet, electricity, and steam engines. While the initial excitement might fade as practical realities set in, the impact of generative AI will keep growing as people and businesses find new and imaginative ways to use this technology in their daily lives and work.

What is generative AI?

 

AI has opened up a world of possibilities, but generative AI has taken things to a whole new level, reshaping our understanding of what’s achievable. But before we delve deeper, let’s start with the generative AI definition.

Generative AI is a type of artificial intelligence technology that uses advanced algorithms and models to produce various types of content, including text, imagery, audio, and synthetic data. This technology creates content so surprisingly authentic that it often blurs the line between artificial and human-made.

Unlike traditional AI, it goes a step further—it doesn’t rely on pre-existing rules and patterns. Instead, it learns from a variety of data sources and transforms that knowledge into brand-new outputs. All you need to do is choose the right tool, input a prompt, and wait for GenAI to generate high-quality content in the blink of an eye.

It’s therefore not surprising that generative AI finds practical applications across industries, changing how people approach tasks. Yet, the intriguing question remains: How did it all begin?

The evolution of GenAI: How it all started

The journey of generative AI is an intriguing one. It has its roots in the 1950s when the foundation of artificial intelligence was laid.

1950s – 1970s: Early days and the first chatbot

1950: Alan Turing proposed the concept of “machine intelligence” and introduced the Turing Test for measuring a machine’s human-like intelligence.

1952: A.L. Hodgkin and A.F. Huxley developed a mathematical model showing how the brain uses neurons to create an electrical network, which later inspired advancements in AI and NLP.

1956: Experts from various fields came together to explore the idea of machines simulating reasoning, intelligence, and creativity. During a workshop called Dartmouth Summer Research Project, AI was born as a new field of research. And that was when the term “Artificial Intelligence” was coined.

1957: Linguist Noam Chomsky published Syntactic Structures, a book that established rules for translating natural language sentences into a format that computers can understand and use.

1964-66: At MIT, Joseph Weizenbaum developed ELIZA—One of the earliest and most well-known examples of genAI models. ELIZA, a chatbot imitating a psychotherapist, engaged users by asking questions and generating responses based on their input.  Operating on a basic pattern and identifying keywords, ELIZA generated predetermined, generic responses, giving the illusion of understanding human speech. Despite its primitive nature, ELIZA set the stage for subsequent advances in Natural Language Processing and the transformative journey of generative AI.

1980s – 1990s: The decades of neural networks

Over the years, AI had its highs and lows. However, in the 1990s and 2000s, as computing capacity expanded, AI experienced significant growth. The internet’s rise led to a data explosion, and by the 2000s, computers became powerful enough to handle this data influx. 

This period brought about new technologies like machine learning, neural networks, and deep learning, paving the way for smarter and more responsive systems.

1982: John Hopfield developed the Hopfield network, a recurrent neural network (RNN) capable of learning and remembering patterns. It provided insights into how human memory works.

1986: Geoffrey Hinton and colleagues published a groundbreaking paper on backpropagation, a crucial algorithm in training neural networks.

1997: IBM’s Deep Blue, an AI chess-playing computer, made history by defeating world chess champion Garry Kasparov, marking a triumph for AI.

1998: Yan LeCun and colleagues showed how convolutional neural networks (CNNs) can be used to recognize images.

2000s – 2020: The millennium developments

It wasn’t until the mid-2010s, with the advent of OpenAI’s GPT (Generative Pre-trained Transformer) models, that generative AI truly took off. 

GPT models, starting with GPT-2 and evolving into more advanced versions like GPT-3, marked a turning point in NLP and content generation. These models demonstrated unprecedented language understanding and creative output, showcasing the potential GenAI holds.

2003: Yoshua Bengio and his team created the first feed-forward neural network language model, predicting the next word in a sequence of words.

 2014: Ian Goodfellow pioneered the generative adversarial network (GAN), allowing machines to generate new data by learning from existing sets, such as creating realistic photos.

2017: Google’s Ashish Vaswani proposed the Transformer, a new simple network architecture relying solely on attention mechanisms.

2018: OpenAI launched GPT (Generative Pre-trained Transformer), setting a new standard in language models for natural language processing tasks.

2019: OpenAI unveiled the full version of GPT-2, trained on a dataset exceeding nine million documents, encompassing text from Reddit URLs with a minimum of three upvotes.

2020s: ChatGPT and AI revolution

2020: OpenAI introduced GPT-3, a powerful language model with the ability to handle various language tasks.

2021: OpenAI unveiled DALL·E, a neural network system capable of generating images based on textual descriptions.

2022: ChatGPT released GPT-3.5, reaching 100 million users in just two months.

2023: The generative AI competition took off. Microsoft integrated ChatGPT into Bing, offering it to all users. Google introduced Bard, its own generative AI chatbot, and OpenAI introduced GPT-4 as a premium option.

The generative AI tech stack

The generative AI technology stack integrates various components to create, evaluate, and implement generative models. It’s a complex blend and each element is crucial.

 

Application frameworks: The cornerstone of GenAI’s stack

Application frameworks stand as the cornerstone of generative AI, offering specialized software libraries for machine learning development. These frameworks simplify model construction by providing preconfigured functions and structures. Well-known examples include TensorFlow, PyTorch, and Keras, which serve as the architectural pillars that make it easier to create and manipulate generative AI models. 

Models: The brain of GenAI

At the heart of the Generative AI stack are the Foundation Models (FMs), often referred to as the ‘brain’ of the system. These models, whether created by big organizations like OpenAI, Anthropic, or Cohere, or developed by individuals, have the remarkable ability to reason in a way that’s quite human-like. 

What’s intriguing is that developers can train their own models, optimizing applications by using various FMs. Deploying these models on servers or directly installing them on devices and browsers not only enhances security but also reduces delays, making processes more cost-effective.

Data: Feeding GenAI with knowledge

The quality and quantity of data used to train a model greatly affect how well the model can generate high-quality outputs. The more varied and extensive the dataset, the better the generative AI model can be. Thus, collecting relevant data, cleaning it to remove errors, and organizing it in a way that models can comprehend are crucial steps.

Evaluation platform: Measuring and monitoring performance

Keeping the right balance between how well the model works, its cost, and how quickly it responds is a big challenge in Generative AI. Developers use different tools to find the best prompts, experiment online and offline, and keep an eye on how the model is doing in real time and where improvements are needed. This back-and-forth process is important for refining the model to get the results you want.

Deployment: Moving applications into production

It involves putting the trained model into an application or system where it can do its job. Whether it’s creating new content, making predictions, or other tasks, deployment can be a complex process. It includes optimizing the model, configuring servers, and developing interfaces so that the model can be used effectively within real-world contexts.

Different generative AI models

There are different types of generative AI models. Structured as neural networks, they take in different types of information and convert them into numerical representations called vectors. This conversion process is managed by the underlying software.

Once the data is in numeric form, the model can use it to generate various outputs like pictures, and texts, or even imitate voices through processes like deep fakes. The essence lies in the model’s ability to understand and work with these numeric representations to create meaningful and diverse outputs. Let’s explore these generative AI models in more detail below.

GANs (Generative Adversarial Networks)

GANs are a powerful framework for generative modeling. They consist of two main components: a generator and a discriminator. The generator is responsible for creating new data samples from random noise, while the discriminator tries to differentiate between real and generated data. Through an adversarial training process, the generator aims to generate data that is indistinguishable from real data. The discriminator, on the other hand, learns to become more accurate in its classification.

The result? GANs can produce visually appealing and realistic outputs, especially in the domain of image generation. They have been used to create realistic human faces, generate artwork, and even simulate realistic environments for video games. The competitive nature of GANs during training allows them to capture intricate patterns and details, resulting in high-fidelity generated data.

VAEs (Variational Autoencoders)

VAEs are another popular approach to generative modeling. They’re based on the concept of autoencoders, which are made up of two parts: an encoder and a decoder. The encoder’s job is to compress input data into a smaller, more manageable format known as a ‘latent space representation’. The decoder then takes this compressed data and reconstructs the original data from it.

What sets VAEs apart is their probabilistic nature. Instead of encoding data into a fixed representation, VAEs learn the probability distribution of the latent space. This means they can generate new data points by randomly selecting from this learned distribution. By exploring this latent space, VAEs can produce a variety of unique outputs.

They have been used in a range of areas, including creating images, text, and music. While outputs they generate might not be as visually sharp as those made by GANs, VAEs are excellent at capturing the underlying structure of the data, and can easily generate a wide range of plausible examples.

Autoregressive models

Autoregressive models create new data points by learning the probability of each point based on what came before. They’re trained to predict the next point from past context and generate new instances during inference.

Used in text generation and music composition, autoregressive models excel at capturing dependencies in sequences, producing coherent and contextually relevant outputs.

In machine learning, autoregressive models predict the next part in a sequence by analyzing past inputs and leveraging probabilistic connections. For instance, in training, an autoregressive model processes multiple English sentences and recognizes the consistent pairing of “there” and “is,” allowing it to generate a new sequence containing “there is.”

Flow-based models

Flow-based models in generative AI are used to generate new data that mimics real-world examples. Think of it like an artist starting with a blank canvas (simple random noise) and then gradually adding layers of detail (a series of changes or “flows”) until a full picture emerges.

The key advantage of flow-based models is that they are reversible, which makes it easy to understand how likely a particular outcome is. They are particularly useful in areas like image and speech synthesis where generating realistic data is important. For example, they could help create new images for a virtual reality game or synthesize speech for an AI assistant.

Transformer-based models 

Transformer-based models are a type of machine learning model used in natural language processing. They handle data in any order, not sequentially, making them effective for tasks needing full context understanding like language translation, text completion, question answering or summarization. 

Examples include Google’s BERT or GPT (Generative Pre-trained Transformer) which have demonstrated impressive abilities in generating coherent and contextually relevant text once given a prompt. They work by paying ‘attention’ to different parts of the input to capture complex relationships within the data. 

How does GenAI work?

Now that we’ve grasped the generative AI meaning, its models, and brief history, let’s explore how it actually functions. Generative AI operates by using models that learn from existing data patterns. It then applies this knowledge to produce new, similar data. 

However, this process is by no means a simple one. It involves advanced algorithms and requires a significant amount of computing power. Usually, the process unfolds through the following steps.

Data collection and preprocessing

The first step involves collecting a large amount of data which is similar to the type of content that AI will be expected to generate. This could be text, images, audio, or any other type of data. The effectiveness of the generative model relies on the quality and diversity of the data. 

Before feeding data into the model, the collected data needs to be preprocessed to make it suitable for use by the AI model. This involves cleaning the data to remove errors, inconsistencies, or irrelevant information. 

Learning and understanding patterns

After the data is collected and preprocessed, it’s fed into a machine-learning algorithm. The algorithm examines the data and learns to identify patterns and features. For example, it might learn what mountains typically look like, their common colors, shapes, sizes, and so on. 

Generating new content

Once the algorithm understands the patterns in the training data, AI can begin generating new content. It does this by randomly selecting a point in the learned data distribution and then decoding that point into a new piece of content. For instance, a GAN generator might create realistic images, and a text-based model could produce coherent paragraphs based on the learned language patterns.

Evaluation and refinement

Just creating new data isn’t enough; the quality of the generated outputs needs assessment. This is where evaluation comes in. Depending on the evaluation outcomes, adjustments to the model may be necessary. This may involve adjusting parameters, changing the training data, or modifying the model architecture to improve the quality of generated content.

How can GenAI contribute business value?

 

GenAI presents a revolutionary approach to enhancing revenue, reducing costs, increasing productivity, and managing risk effectively. It’s set to become a key differentiator in competitive markets. Gartner has outlined three main areas where GenAI can add value to your business.

1. Revenue opportunities: GenAI accelerates the development of innovative products, from new drugs to faster and better diagnoses, creating fresh revenue streams. Gartner’s research indicates that companies with mature AI adoption are likely to see more significant revenue benefits.
2. Cost and productivity opportunities: GenAI enhances worker capabilities, streamlining tasks like content creation and software code verification. It fosters a productive symbiosis between humans and AI, boosting employee skills and broadening their capabilities. Moreover, GenAI can mine untapped content for valuable insights, transforming workflows.
3. Risk management opportunities: GenAI’s analytical abilities provide a broader, deeper view into data, such as customer transactions and software code, facilitating early risk detection. Additionally, it aids enterprises in complying with sustainability regulations, mitigating asset risks, and integrating sustainability into decision-making and design processes.

Generative AI’s impact on industries

There’s no denying the transformative power of generative AI, which is currently causing a significant shift in businesses, workforces, and consumers worldwide. Each sector carries a unique narrative regarding how this intriguing—yet slightly unsettling—new technology will reshape systems and workflows. In a recent Google Cloud study, an astonishing 82% of organizations believe that generative AI will revolutionize their industry.

Which industries are most likely to face disruption? Well, there is a wide range of sectors that GenAI may impact to a bigger or lesser extent. Some are already feeling the profound effects of generative AI and have started redefining their usual business practices. Let’s take a look at the industries that have the highest potential for growth in the near term:

• Manufacturing: Generative AI is changing the face of manufacturing. How? By automating product design, optimizing processes, addressing skill gaps, and even enhancing risk management. But to make this all possible, the right storage infrastructure is needed. It’s the foundation that allows for a smooth deployment of this transformative technology. 
• Media and entertainment: The influence of GenAI in the media and entertainment sector is impressive. It’s speeding up content creation, from generating new scripts to creating ad content in no time. Beyond writing, it’s also reshaping image creation and editing, leading to a more streamlined and cost-effective process. According to Deloitte, 72% of M&E leaders believe AI will play a crucial role in the next five years.
• Marketing: A 2023 study conducted among US marketers revealed that 73% of them used AI tools for creating marketing copy and personalized advertising. By 2025, it’s anticipated that 30% of marketing messages from large companies will be AI-generated, a rise from just 2% in 2022.
• E-commerce and retail: As 60% of shopping starts online and buyers are smarter and more connected, AI helps retailers meet customer needs, create a more personalized experience, and stand out in a highly competitive market. According to BCG, brands using advanced digital tech for personalization see revenue growth 6% to 10% faster. Currently, 40% of retailers are exploring generative AI, and 21% are actively investing in implementation for the coming year.
• Pharma and healthcare: Better diagnosis, precision, and patient care. On top of that, by 2025, the use of generative AI will help find more than 30% of new drugs and materials, compared to none today. This is great news for the drug industry as it could lower costs and speed up the discovery of new drugs.
• Banking and financial services: GenAI may streamline fraud detection, automate customer interactions through chatbots, and enhance personalized financial advice. The technology has the potential to enable quicker decision-making, improve security measures, and augment the overall customer experience.
• Education: Generative AI may be used to deliver personalized learning experiences, create tailored content, and automate some administrative tasks. From customized lesson plans to automated grading, AI enhances efficiency, engagement, and accessibility in education.
• Energy: While it might be too early to fully define the impact that generative AI has on the energy industry, it’s clear that it’s expected to transform the energy industry through predictive maintenance, smart grid management, renewable energy forecasting, and improving grid security.

The influence of GenAI is undoubtedly widespread, touching countless industries. However, to gain a more focused and comprehensive understanding, let’s shift our lens onto specific sectors and delve deeper into retail, banking, and fintech. These areas, leading the charge in technological innovation and significantly influencing our everyday lives, offer an insightful perspective on the pros and cons that come with GenAI.

What benefits can generative AI bring in retail?

From personalized product recommendations to consumer trend prediction, GenAI is enabling retailers to create more engaging and efficient customer interactions. 

Almost half (48%) of retail leaders have their sights set on AI and ML as the top technologies shaping the retail landscape in the next 3-5 years. And a substantial 60% of them are planning to adopt these technologies within a year. The goal? To enhance both in-store and online customer experiences. McKinsey forecasts that through improving digital customer interactions, generative AI could bring in an extra $310 billion for the retail sector.

While GenAI is still an emerging field, the future of retail with this technology by its side indeed looks promising.

Personalized shopping experience

As more and more consumers expect brands to understand their preferences, GenAI is stepping up as a serious game-changer. Just think about it—75% of retail customers are more likely to buy again from brands that tailor their experience. It becomes clear that GenAI isn’t just a passing trend, it’s shaping up to be a crucial player in the business world.

It can analyze a customer’s browsing and purchasing history, making product recommendations that are specifically customized to their tastes and preferences. This not only simplifies the shopping process but also makes it more enjoyable for customers, guiding them towards making purchase decisions.

For example, The North Face, the outdoor product company, uses a conversational AI platform, powered by IBM’s Watson, to provide personalized recommendations to customers. The system asks shoppers about their needs and uses their responses to suggest suitable products. 

Amazon is another prime example of how generative AI is being used in the retail sector. The company leverages advanced AI algorithms and machine learning to deliver extremely accurate selection of product recommendations in the online shop. This improves customer experience and increases sales.

Competitive edge with virtual try-ons

Virtual try-ons, driven by the powerful combination of AI and AR technologies, have become increasingly popular in the retail industry, particularly in fashion, beauty, and eyewear sectors. Gone are the days of static images; customers can now actively visualize how a product looks or fits before making a purchase. Retailers in these sectors are adopting virtual try-on technology to enhance customer experience, reduce costs, and gain a competitive edge.

Take, for instance, Warby Parker—an eyewear brand, which features a cutting-edge virtual try-on capability in its app. This use of AR technology allows customers to see how different glasses styles complement their face. 

Similarly, IKEA has revolutionized furniture shopping with its Augmented Reality app, Ikea Face. With this app, customers can see how certain pieces of furniture would look inside their apartment before making a purchase. All they need to do is to use their smartphone to place 3D models of furniture inside their home to get an exact image. 

Better product development

Generative AI is playing a leading role in product discovery, design, and customization. With GenAI, retailers can understand consumer needs, popular trends, and sales data better and develop products faster and in a more cost-effective way. 

Online jewelry maker J’evar has used the power of generative AI to accelerate its design process. The AI tool takes in details about a product’s materials and specifications, and then generates an image of the proposed product, saving weeks of manual work. The AI refers to a knowledge bank filled with metrics, images, and crucial details like gold and silver weights before creating an image.

Smart inventory management

Using generative AI, retailers can analyze past sales, store receipts and returns to evaluate purchases in real-time. This data also helps them understand market trends and predict product demand and future shopping habits. As a result, retailers streamline their inventory processes, avoid overstock and stock shortages, and stay ahead of the curve. 

Take Walmart as an example. The retail giant employs AI and machine learning-driven systems for inventory management, ensuring timely and cost-effective supply to customers. By combining historical data with predictive analytics, Walmart can strategically distribute items, which enhances customer satisfaction and operational efficiency.

Streamlined customer support

GenAI is a critical tool for customer service. It aids customer service reps with real-time responses to queries and improves other operational areas. For instance, AI-powered virtual assistants can reduce the load on call centers and help generate fresh FAQ content for the website.

Considering that around 75% of customers engage through multiple channels throughout their journey, generative AI ensures consistent, high-quality service across all platforms. This includes digital self-service to agent-assisted options. By offering immediate, personalized customer service, GenAI enables customers to quickly and effortlessly obtain the information they need.

So, it’s not just about improving customer service—it’s also about increasing productivity. According to a recent report, applying GenAI to customer care functions could potentially increase productivity value between 30% to 45%.

Tailored marketing campaigns

Who enjoys receiving marketing messages that don’t resonate with their interests? Probably no one. GenAI brings a fresh approach to marketing by analyzing current trends and following brand guidelines. With GenAI tools, retailers can optimize product titles and descriptions, as well as adapt their digital ad content to the unique habits and needs of consumers in a scalable way.

Think of Starbucks which uses its AI platform, Deep Brew, to scrutinize customer preferences and buying history. This information is then used to customize marketing communications, resulting in heightened customer engagement and more effective promotional campaigns.

Generative AI benefits in fintech and banking

Generative AI’s footprint in the fintech and banking industry is unmistakable. Consider this—the fintech AI market is set to soar to $31.71 billion by 2027, growing at an impressive rate of 28.6%. It’s not just a glimpse into the future. Artificial intelligence is already a key player, with 90% of global fintech firms leveraging it, according to the Cambridge Centre for Alternative Finance.

As for generative AI, the forecast is equally optimistic. Its value in the fintech market is predicted to leap from $865 million in 2022 to a staggering $6.2 billion by 2032. That’s a remarkable increase, isn’t it? 

In the banking sector, GenAI could add between $200 billion and $340 billion in value annually, which accounts for 9%-15% of banks’ operating profits. A survey reveals that 47% of banking executives have begun exploring the potential of GenAI in their operations, with many in the proof-of-concept stage. This signifies a growing recognition of the transformative power of GenAI in banking as well.

Lots of numbers and statistics. True. But they aren’t just figures; they tell a story of GenAI’s influence in the financial industry. So, let’s now go beyond the figures and explore the tangible benefits that generative AI might bring to the table.

Customer service automation

In the financial sector, top-notch customer service is crucial. However, maintaining 24/7 support can be a challenge for many companies due to various operational constraints and resource limitations. With finances at stake, customers demand quick solutions, no matter the time.

Generative AI can automate customer service tasks, including the use of virtual assistants, to help address this issue. Gartner predicts that by 2026, GenAI could automate nearly 30% of such tasks.

While not all interactions can be automated, especially those involving sensitive information, GenAI can handle routine tasks and simple queries. This frees up human agents to tackle more complex issues.

One real-world example is Bank of America’s AI assistant, Erica. Integrated into their mobile banking app, Erica provides users with human-like responses using natural language processing. As of October 2022, over 32 million customers have used Erica, making it a popular AI tool in US banking.

Process automation

Process automation is another field where generative AI is used as an advanced tool. It can automate routine, data-intensive tasks, such as transaction processing, compliance reporting, and client onboarding. These processes include several steps and take a significant amount of time. Generative AI not only reduces the time and cost associated with these tasks but also minimizes human error.

For instance, in transaction processing, GenAI is used in solutions like JPMorgan’s COIN program. It automates the review of commercial loan agreements and can save 360,000 hours of work each year. 

When it comes to compliance reporting, generative AI can streamline the process, as seen with Suade Labs’ AI-driven platform which automates regulatory reporting. Fenergo, on the other hand, is a company specializing in client lifecycle management software, and uses AI to streamline and automate the client onboarding process.

Automated financial advice

Generative AI offers automated, personalized financial advice based on an individual’s unique financial data and current market trends. It can suggest ways to boost savings, streamline debt repayment, and identify opportunities for cost reduction, thereby facilitating smarter financial decisions. A recent study by the CFP Board found that nearly one-third of American investors trust AI-generated financial advice. 

Robo-advisors like Betterment and Wealthfront are already leveraging this technology to offer automated investment advice. Through GenAI, these platforms can provide tax-efficient investing strategies, automatic rebalancing, and retirement planning advice. The AI technology enables them to handle complex tasks and calculations, which simplifies investing and makes it more accessible for clients. 

Brex Finance Assistant, set to launch in 2023, is another example of generative AI’s ability to deliver tailored financial guidance. It acts as an intelligent tool for CFOs, providing insights into budgeting and expenses. With Brex Finance Assistant, CFOs can make data-driven decisions for their business growth.

JPMorgan Chase’s IndexGPT, expected to be released between 2026 and 2027, is also worth noting. This platform will help customers explore investment options and offer guidance on future investments.

Financial forecasting

Another generative AI benefit lies in its impact on financial forecasting. GenAI uses data-driven and probabilistic models that learn from large datasets to predict financial trends. According to a KPMG survey, 83% of respondents use AI for financial planning, including predictive forecasting, scenario creation, and budget insights. 

By generating synthetic financial data that mirrors real-world data, GenAI allows for more extensive and diverse analysis. It goes further and forecasts global economic trends. McKinsey estimates that GenAI could add between $2.6 trillion to $4.4 trillion annually across various use cases.

Moody Analytics is worth a mention here. The company provides economic research and forecasting tools that analyze current market trends and economic indicators. Businesses and financial institutions use them for predicting economic cycles, regional economic conditions, and potential impacts on their operations and investments.

Risk assessment 

In the dynamic world of finance, both traditional banks and fintech companies need to stay ahead of possible risks. With generative AI, it might become easier.

For banks, GenAI is revolutionizing the evaluation of creditworthiness. It analyzes various data points such as income, credit history, and loan repayment history, to make informed decisions about lending. For instance, JPMorgan Chase employs AI to predict loan defaults, making their evaluation process more efficient and accurate.

Fintech companies, particularly those offering lending services or investment platforms, are also embracing the power of GenAI. Beyond assessing credit risks, they use it to obtain deeper insights into financial markets and measure the levels of uncertainty associated with their transactions and investments. Importantly, GenAI can simulate multiple financial scenarios, providing these companies with the foresight to manage potential hazards effectively.

A prime example of this is Upstart, a fintech company that’s using AI in an innovative way to assess risk. Instead of just looking at a person’s FICO score, Upstart uses AI and 1,600+ non-traditional variables, like education and job history, to predict creditworthiness. This leads to lower loss rates, better matching, and better lending rates. 

Enhanced fraud detection and prevention 

The need to secure digital financial systems has never been more pressing. With a worrying 15% increase in identity-related issues and an alarming 92% surge in attempted payment fraud cases from 2021 to 2022, the financial industry is facing serious challenges. Fortunately, GenAI emerges as a powerful ally in this battle. 

It supplements traditional analytics with models that can help detect unusual patterns in large volumes of transactions and alert banks in time to take action. For example, Bank of America uses generative AI to identify fraudulent credit card activities. It does so by analyzing customer behavior and billions of financial interactions daily. 

Similarly, fintech companies, especially those dealing with digital payments and transfers, need robust fraud detection systems. Generative AI can monitor financial activities in real time, identify suspicious activity, and prevent fraudulent transactions.

For instance, Visa’s Advanced Authorization (VAA) system uses AI to assess the likelihood of a financial interaction being deceptive in just milliseconds, preventing approximately $27 billion in fraudulent transactions. Mastercard’s Decision Intelligence is yet another real-time authorization decision-making solution that leverages AI to enhance the accuracy of fraud detection.

Streamlined loan processing

Loan processing is rather a tedious task filled with paperwork and manual labor. Fortunately, it can be streamlined, adding to the array of generative AI benefits discussed earlier. By evaluating financial data and credit scores, GenAI quickens the procedure and reduces costs. 

AIO Logic’s AXIS, a commercial loan management platform, uses GenAI to assess risks and customize loan structures, resulting in time and cost savings. Meanwhile, Ocrolus employs GenAI for document processing in digital lending, accelerating analysis and reducing human workload.

As you can see, the benefits of generative AI in the financial sector are vast, and the potential for innovation is limitless. Imagine these advancements tailored specifically for your needs. That’s precisely where Neontri can give you a helping hand.  

What generative AI challenges can you come across?

Every coin has two sides, and generative AI is no exception. Alongside its promising benefits, there’s also a flip side—a set of challenges and risks that need careful consideration. Let’s take a look.

Insufficient expertise and resources

Deficiency in resources when it comes to staffing generative AI initiatives is a common problem across various organizations. For example, 55% of banking decision makers say that the lack of internal expertise is a major hurdle in setting up a dedicated team for GenAI.

Cost and budget constraints

Developing and training GenAI models can be costly due to the need for vast amounts of data and specialized technology. The ongoing maintenance of these systems also adds to the expenses, requiring skilled staff.

However, the challenge goes beyond just upfront costs. Predicting the ROI of GenAI implementation can be tricky. Its success largely depends on how well it’s integrated into existing business processes and whether users embrace it. This uncertainty makes budgeting for GenAI a careful balancing act, considering both initial investments and the ongoing costs for maintenance and potential risks. 

Explainability and interpretability challenge

The issues of explainability and interpretability in GenAI adoption are frequently overlooked. Generative AI models often function as black boxes, making it difficult to understand how they produce outputs. This lack of transparency and interpretability can hinder their adoption, especially in industries where transparency and accountability are crucial. 

For example, within the financial sector, regulatory bodies and auditors may demand explanations for the decisions taken by AI models that formulate financial projections or investment plans.

Lack of internal capabilities

GenAI models, particularly those based on deep learning architectures, demand substantial computing resources for training and deployment. Some organizations, however, still rely on outdated and highly modified tech systems filled with temporary fixes and inefficient data sharing, creating barriers to AI integration.

As a result, a considerable number of bankers express doubts that their organizations have the right technological structure and abilities to implement GenAI. 37% of them aren’t confident in their organization’s internal resources, including technology infrastructure, regulatory controls, and talent, needed to put GenAI use cases into action.

Data privacy issues

Keeping data private and secure is a challenge, especially in the highly regulated financial sector. Data protection is a top priority. That’s why financial institutions and fintech companies invest considerable resources and effort to safeguard their own data as well as their clients’.

Unfortunately, GenAI does raise significant concerns regarding data privacy. Data leaks from training sets, the potential for unmasking anonymized data or revealing sensitive information through AI/ML output…These are a few out of many risks. For example, in 2019, Capital One faced a data breach that exposed the personal information of over 100 million customers including customers’ accounts and credit card applications. 

Publicly available GenAI systems present unique challenges as they continuously use user inputs for training and fine-tuning, which can lead to data leaks. While enterprise-level GenAI is being developed to enhance data security, issues persist due to its ability to process a wide range of data formats, with the risk of unintentionally collecting personal information. 

Possibility of misinformation

The accuracy of GenAI models and their results have been flagged as a potential risk. Some systems might produce content that seems trustworthy but is, in fact, incorrect—a situation sometimes called a ‘hallucination’. 

Hallucinations can be a serious issue for organizations relying on GenAI for decision-making, content creation, or research. If false information is presented as fact, and people act on it without knowing it’s incorrect, it can lead to damage to reputation and legal issues. 

Imagine this: A financial institution relies on a GenAI system for investment advice. The system creates a persuasive report recommending a particular stock, but it’s a hallucination derived from training data, not the stock’s actual performance. A client follows the misleading report, which might result in substantial financial losses when the stock doesn’t perform as suggested. This can result in reputation damage, financial setbacks, legal troubles, and compliance challenges for the institution.

Risk of harmful content

Generative AI systems work by processing vast amounts of internet data and producing new content based on patterns they identify. However, they can unintentionally produce harmful content from the toxic data they process. Despite AI providers’ efforts to introduce filters, there’s still a risk of generating damaging content.

This problem becomes critical when businesses use AI tools for customer interaction. For example, Microsoft’s Bing chatbot once made an inappropriate suggestion to a user, presenting a threat of serious brand damage.

Algorithmic bias

If the data used to train the GenAI models is biased, it can lead to unfair outcomes. This might be a serious problem in the banking industry, where decisions about creditworthiness, for example, must be fair and transparent. 

Apple Card, for instance, faced backlash when its credit limit algorithm was accused of gender bias.

Cybersecurity challenge

GenAI comes with a cybersecurity challenge—the potential for creating misleading content, known as “deepfakes.” These sophisticated manipulations convincingly mimic behaviors, content styling, images, and voice audio to create realistic phishing traps impersonating important figures. Some advanced models, like DarkBERT, can even integrate with everyday tools like Google Lens, opening the door to the misuse of mainstream images and texts.

Beyond this, GenAI is also being used to boost and amplify existing threats, making traditional attacks more frequent. Some people employ it to generate and modify malware code, which pinpoints and exploits vulnerabilities in a company’s cybersecurity infrastructure. And as if this wasn’t bad enough, generative AI plays a role in developing more complex and targeted phishing emails and campaigns.

Regulatory uncertainty

Navigating the implementation of generative AI in businesses faces a significant hurdle: regulatory uncertainty. In a recent KPMG survey, 77% of CEOs from large companies revealed that uncertainty about the regulatory environment is the primary barrier to deploying GenAI. Consequently, some of them are pausing deployment for three to six months to assess the regulatory landscape. 

The intricate legal and regulatory challenges posed by GenAI, such as intellectual property rights, privacy concerns, and liability issues, create a hesitancy among organizations to embrace these solutions. For instance, implementing this technology in financial scenarios raises concerns about compliance and potential legal consequences.

Job displacement

With AI automating tasks, there’s a fear of job displacement. As more tasks become automated, the need for human intervention decreases. The result? Potential job losses. For instance, Wells Fargo predicted a job loss of 200,000 in the banking industry due to AI. 

The best practices for using generative AI 

As more and more companies embrace generative AI to enhance diverse processes, it seems that this tool is here to stay. So, what key practices should businesses take into account while opting to incorporate this technology into their operations?

1. Assess your organization’s readiness for AI: Before implementing genAI, evaluate your organization’s technical capabilities, infrastructure, and readiness for a change. 
2. Define your GenAI implementation strategy: Have a clear plan about how genAI will be integrated into existing processes and who will be responsible for managing it.
3. Build a cross-functional team: While several companies have dedicated AI teams, depending solely on them can result in bottlenecks, reduced productivity, and interdepartmental disconnects or silos. Rather than relying on a solitary AI team, companies should consider forming a cross-functional support team comprising members from diverse backgrounds.
4. Establish data privacy guidelines: Given the sensitivity around data privacy, it’s important to establish clear guidelines about how customer data will be used and protected.
5. Ensure transparency: Clearly label multiple times that individuals, whether they are employees or customers, are engaging with a machine. Be open and honest about the use of automation in interactions.
6. Vendor selection process: Choosing the right GenAI vendor is critical. Seek the one with a proven track record, exceptional customer support, and a product that meets your specific requirements. Evaluate factors like pricing, scalability, and post-implementation support during the selection process.

Considering incorporating GenAI solutions into your business, but unsure where to begin? Reach out to Neontri. With over a decade of experience in various technologies, including Machine Learning, we go beyond being service providers; we’re your strategic partners. Our skilled team understands the intricate aspects of this cutting-edge technology and is ready to support you with a tailored approach, ensuring successful integration.

Generative AI future: What’s there to come?

Unlike other technologies that have come and gone, Generative AI looks set to stay and become an even more important part of the business landscape. The potential it holds for companies striving to boost efficiency and foster innovation is nothing short of groundbreaking. 

However, in the middle of all the benefits and possibilities offered by GenAI, a gentle reminder emerges—companies must proceed with care. Every technology can be used in a morally doubtful way, and GenAI is no exception. What’s coming next? Well, that’s the intriguing part—let’s wait and see.