What Is GenAI? How Generative AI Is Changing Everything

GenerativeAI (GenAI) isn’t a buzzword. In just a few short years, it’s transformed from a radical new concept to see widespread adoption across multiple businesses and platforms. Research from Feedzai shows 96% of global banks are already implementing GenAI. As with any major new trend or technological advancement, criminals have been quick to adopt GenAI and make it part of their tactics. Losses from GenAI-related crime could reach $40 billion by 2027, according to forecasts by Deloitte. 

In this article, we’ll break down how GenAI works, its most promising use cases, new financial crime challenges that it’s creating, and the opportunities for banks and businesses to embrace their own GenAI solutions.

Key Takeaways

• Generative AI (or GenAI) is a form of advanced AI capable of creating original content or datasets based using deep learning models.
• In financial services, GenAI has several use cases. These include fraud detection, scam prevention, investigations, case manager, and data creation.
• GenAI also presents important ethical considerations for organizations, especially regarding data. It’s important that data is kept secure, private, that consent is obtained, and to watch for hallucinations. 
• GenAI is evolving rapidly with new models being developed and released. Recent models include Frontier and Multimodal Models like GPT-4.5, agentic AI, and open source innovations like LLaMA 3.

What Are the Latest Trends in Generative AI Development?

Generative AI continues to evolve at a rapid pace, with 2025 alone bringing a wave of new model architectures, tools, and breakthroughs that are reshaping how businesses and individuals interact with technology. 

Here’s what’s making headlines in GenAI right now:

• Frontier and Multimodal Models: New models like GPT-4.5, Gemini Ultra, and Claude 3 are capable of integrating text, images, audio, and video. This ability opens the doors to numerous applications, including real-time customer support to dynamic content creation.
• Agentic AI: Intelligent agents are now capable of operating autonomously, handling complex workflows, and making decisions with minimal human input. This shift toward “agentic AI” is expected to automate a significant portion of daily business operations in the next few years. Gartner predicts that 15% of daily work decisions will be made by agentic AI by 2028, compared to 0% in 2025. 

What Is Generative AI and How Does It Work?

Generative AI, also called GenAI for short, is an advanced form of artificial intelligence that is trained using deep learning models to generate new content. While AI classifies or predicts patterns based on existing data, GenAI learns the underlying structure of the data to produce new multi-modal content, insights, and ideas from existing data from scratch. 

What does this look like in action? It can write original, coherent text, craft original artwork, or even create new datasets based on realistic patterns. GenAI is trained by deep learning models like Generative Pre-trained Transformers (GPTs) and diffusion models, which are trained on massive datasets to learn patterns and find relationships in the data.

What Are the Most Impactful Use Cases of GenAI in Banking?

GenAI is already making waves across industries, including in financial services. Some real-world examples include:

• Fraud Detection: GenAI can simulate fraudulent transactions, helping banks train their systems to spot new and emerging threats that haven’t been seen before. GenAI can also be used to simulate fraudster behavior or fraud ring patterns.
• Scam Prevention: GenAI can also be used to help customers protect themselves from scams before money is lost. For example, customers can share screenshots of a tempting advertisement that can be rapidly reviewed for red flags.
• Investigations and Case Management: Investigations were one of the top GenAI use cases cited in the 2025 AI Trends in Fraud and Financial Crime report. GenAI can streamline the fraud investigation workflows by gathering evidence, prioritizing cases based on risk, and highlighting key insights for investigators, allowing teams to resolve cases faster and focus on the most urgent threats. 
• Research and Intelligence Gathering: Another top use case cited in Feedzai’s research, GenAI-powered tools can scan a wide range of sources (including risky digital ecosystems areas like the dark web) to uncover emerging fraud schemes, compromised data, and new criminal tactics. 
• Enhanced Data Analysis: GenAI models can quickly sift through massive volumes of transaction data in real time, identifying subtle anomalies or suspicious patterns that might slip past traditional systems. This results in faster fraud detection and fewer false alarms.
• Personalized Financial Advice: AI-powered chatbots and robo-advisors can produce tailored investment recommendations based on a customer’s unique goals and risk profile.
• Document Automation: GenAI can rapidly draft reports, fill out regulatory paperwork, and even summarize lengthy financial documents, saving teams hours of manual work and review time.
• Synthetic Identity Detection: By generating and testing fake identities, GenAI helps financial institutions strengthen their defenses against identity fraud, including manufactured personas.
• Customer Communication: From crafting personalized emails to generating responses for customer support, GenAI ensures interactions feel human and relevant.

GenAI is also capable of learning patterns and nuances within the large volumes of training data. Using deep learning, it can uncover hidden patterns into how different pieces of data connect to each other. With these insights, it can understand how a typical transaction looks different from a suspicious one. 

“GenAI is essentially lowering the hurdles to scams while simultaneously raising the rewards, bringing even more players into the fraud and scams game. This raises the already-serious stakes of AI-fueled fraud.”

– Dan Holmes

With this training completed, GenAI technology can generate highly realistic outputs based on synthetic transaction data. This can be used to test fraud detection systems or how a new product might perform under different market conditions.

How Can Businesses Benefit from Generative AI?

The global market for B2B generative AI is poised to surge in the coming years. In 2023, the GenAI market for financial services was estimated to be valued at $1.7 million, according to Grand View Research. By 2023, the market is projected to reach $16 billion, a CAGR of 39.1% from 2024 to 2030. North America is the largest market for GenAi while the Asia Pacific region represents the fastest-growing market.

GenAI’s expansion promises to bring notable changes to financial services normal operations. Some of the most notable changes to watch for include:

• Tailored customer experiences: GenAI enhances customer satisfaction and loyalty by personalizing financial advice, investment strategies, and customer service.
• Automate complex processes: Various financial processes, from loan underwriting to investment management, can be enhanced using GenAI to automate tasks, minimize manual efforts, and improve precision.
• Enhance risk management: GenAI can analyze large datasets in real time, uncovering unusual patterns and predicting new risks before they escalate, enabling businesses and financial institutions to proactively address emerging threats.
• Increased operational efficiency: AII-powered chatbots and virtual assistants have been used to address basic questions from customers. Using GenAI, banks can upgrade chatbots to address more complex questions, freeing up human staff to tackle essential priorities.

What Are the Challenges and Ethical Considerations of GenAI?

GenAI is a powerful tool. But it has transformative capabilities that banks must consider as they look to include this technology in their roadmap. These considerations include how to responsibly handle data used to train both GenAI and AI models as well as essential privacy and security questions. 

Here are some of the most important questions to consider when implementing GenAI.

• Keep Data Private, Ensure Consent: Data privacy concerns were the top issue identified in the Feedzai  2025 AI Trends in Fraud and Financial Crime report. AI and GenAI require large volumes of data for training. It’s essential that data is managed in a way that follows strict data privacy and security regulations like GDPR and CCPA.
• Data Management and Accuracy Challenges: Questions over how to handle large volumes of data was the top issue cited in Feedzai’s survey of global financial services professionals. As digital banking transactions rise, 87% said they were concerned about how to manage data accurately. Issues like incomplete, inaccurate, or improperly handled data can result in poor model performance. Concerns over data handling were cited by 59% of respondents as a reason their bank has not adopted AI yet.
• Data Hallucinations: While GenAI is powerful, it can sometimes “hallucinate,” producing information that sounds plausible but is actually incorrect or nonsensical. These inaccuracies can result in costly mistakes, damage customer trust, and regulatory issues. Ensuring the factual accuracy of GenAI’s output is critical for reliable and responsible GenAI deployment.
• Ethical AI and Bias: Respondents also voiced concerns over ethical AI and bias. Biased data can lead to unfair outcomes for customers, including incorrectly flagging a transaction as a false positive or making discriminatory risk decisioning.   
• Explainability and Transparency: Organizations must be prepared to justify decisions made by AI. Ensure your models are explainable in order to justify decisions to any relevant regulator or authority.
• Job Displacement: AI promises to make life easier for many people. However, there is sometimes a trade-off that does not work well in other peoples’ favor. Feedzai’s research found 26% of respondents are concerned about losing their jobs to AI. Nearly half (46%) believe the technology will replace many roles and positions. 

How Does GenAI Differ from Other Types of AI?

Traditional AI and machine learning focus on recognizing patterns and making predictions. Generative AI goes a step further by creating new, realistic outputs—making it especially powerful for tasks like simulating fraud or generating customer communications.

What Are the Latest Trends in Generative AI Development?

Generative AI continues to evolve at a rapid pace, with 2025 alone bringing a wave of new model architectures, tools, and breakthroughs that are reshaping how businesses and individuals interact with technology. 

Here’s what’s making headlines in GenAI right now:

• Frontier and Multimodal Models: New models like GPT-4.5, Gemini Ultra, and Claude 3 are capable of integrating text, images, audio, and video. This ability opens the doors to numerous applications, including real-time customer support to dynamic content creation.
• Agentic AI: Intelligent agents are now capable of operating autonomously, handling complex workflows, and making decisions with minimal human input. This shift toward “agentic AI” is expected to automate a significant portion of daily business operations in the next few years. Gartner predicts that 15% of daily work decisions will be made by agentic AI by 2028, compared to 0% in 2025. 

“Agentic AI should not displace human engagement. People must still be included in the loop to ensure the model’s outcomes match the customer’s intent. However, it can free human agents to focus on complex customer interactions and investigations.” Andy Renshaw, SVP of Product Management, Feedzai.

• Open-Source Innovation: Platforms like Hugging Face, Meta’s LLaMA 3, and Mistral have made it easier for developers to build, share, and customize generative AI models. The open-source movement is fueling rapid experimentation and democratizing access to advanced AI capabilities.
• Creative Collaboration Tools: AI is now a co-creator, working alongside humans in fields like design, music, and writing. Tools such as Midjourney, DALL·E, and Suno empower creatives to ideate and iterate faster than ever before.
• Generative Virtual Worlds: AI models can now generate interactive virtual environments and simulations on the fly, revolutionizing gaming, training, and robotics. For example, Google DeepMind’s Genie 2 can turn a single image into a playable game world, while startups are using large world models (LWMs) to create immersive experiences.

• Synthetic Data and Simulation: Generative AI is widely used to create synthetic datasets for training and testing, especially in finance, healthcare, and autonomous vehicles. This approach enhances privacy, simulates rare events, and improves model robustness.
• Real-Time AI: Advances in edge computing and connectivity (like 6G) are enabling real-time generative AI applications, such as instant language translation, live video content creation, and proactive customer service. This ability can both improve customer experiences and offer them new services at the right moment.

How Feedzai Uses Generative AI to Enhance Fraud Prevention

Feedzai is putting GenAI on the frontlines against fraud and scams with several of our solutions. This includes: 

• Empowering Customers with ScamAlert: ScamAlert is Feedzai’s flagship GenAI tool. It empowers customers to protect themselves from scams by harnessing the rapid power of GenAI. When a customer comes across a suspicious-looking ad for a too-good-to-be-true offer, an invoice, text message, or other communication option. Using a screenshot, ScamAlert quickly analyzes the image for red flags, like unrealistic offers or urgent pressure tactics. From there, it recommends next steps, such as verifying the sender or using only secure payment methods. This helps customers avoid scams before authorizing risky transactions, turning them into active defenders rather than passive victims.
• TRUST Framework: Feedzai launched the TRUST Framework, a guide for banks to ethically develop and deploy AI from the beginning. TRUST is based on five key pillars, Transparent, Robust, Unbiased, Secure, and Tested. This framework ensures that AI systems are explainable, resilient, fair, and secure, and that they’re rigorously tested before deployment. It’s a proactive stance that turns ethical imperatives (including privacy, fairness, and explainability) into competitive advantages for financial institutions.
• Case Summary Agent: Fraud analysts spend too much time sifting through data to understand an alerted transaction. Key information can easily be missed because subtle patterns aren’t always obvious. Feedzai’s Case Summary Agent streamlines this process by identifying what’s truly unusual in an alert, highlighting the specific areas that require a closer investigation. It also provides clear explanations for why patterns are considered suspicious.
• Rule Creator: Manually creating rules can be prone to errors and is often a time-consuming process, especially for those who aren’t experts in the language  of rules. Rule Creator changes that by generating new rules based on a simple user prompt. It interactively guides users through the rule creation process, minimizing redundancies and eliminating inefficient rules, resulting in faster rule creation, quicker time to value, and enhanced team efficiency.

WIth 96% of global banks adopting the technology, GenAI has moved beyond a buzzword to a critical business asset. Banks and businesses can use this advanced technology to stay secure, efficient, and ahead of the curve. As GenAI becomes more widespread, the key is to balance your organization’s innovation priorities with responsibility, ensuring that AI serves both your institution and your customers safely and ethically.

Resources

• Blog: From Black Box to Agentic AI
• Report: 2025 AI Trends in Fraud and Financial Crime Prevention
• eBook: Generative AI and the Future of Scams
• Solution: ScamAlert: Empower Customers to Stop Scams Before They Happen