A guide to enhanced operations and outcomes

Generative AI (GenAI) is proving to be a powerful tool in many industries. For the financial services industry, especially credit unions and banks, it will be a game-changer.

It’s reported that 6 out of 10 bank leaders are prioritizing GenAI this year, and for good reason. It helps with better customer service, reporting, risk, competitive analysis, and so much more.

It’s clear that GenAI is no longer a “nice-to-have” for financial institutions but should be considered a “must” in order to stay competitive and relevant. This guide explores the popularity of GenAI, tracing its historical development and examining its associated risks and opportunities.

Key Takeaways:

• Financial institutions should look to adopt GenAI to stay competitive, as it enhances its financial services as well as ancillary operations like marketing, customer service, and reporting.
• GenAI democratizes data analysis, allowing lenders to extract valuable insights without specialized coding skills.
• GenAI is not meant to replace humans, but rather frees up time to focus on what really matters: customers and business growth strategies.

Why embrace generative AI over traditional tools?

First, you may ask, what is GenAI? In simple terms, it’s a subset of Artificial Intelligence (AI). It takes input data and creates new content. This content can be anything, including images, text, code, or analysis.

It is rising in popularity because it can improve and automate tasks that require a lot of data, such as generating financial analysis, regulatory compliance, and business operations reports. Typically, these tasks would take days or weeks, but now they can be done in seconds.

Furthermore, it is democratizing data analysis. In the past, extracting meaningful insights from complex datasets often required specialized coding skills. Now, with GenAI, users can easily analyze data and find important trends using simple prompts (more on that in a second).

So, how did it start?

A brief history of a rapidly evolving technology

The evolution of GenAI has been nothing short of incredible. It’s unfolding at a pace unlike any other technological advancement in history. It all started with the release of GPT-1 in 2018, which showcased the early potential of generating coherent text. Then, in 2021, DALL-E was released, capturing imaginations as it created images from text. Finally, in 2023, GPT-4 was released and quickly became the fastest-growing consumer app in history.

This explosive growth underscores the transformative power and increasing accessibility of GenAI and why financial institutions need to pay attention and adapt. Before diving in further, let’s briefly look under the hood.

How does generative AI work?

It’s normal to be hesitant to change, especially when the old ways don’t seem broken. However, learning about how generative AI works can clear up the mystery and make it more approachable. Plus, you learn about the vast opportunities waiting to be reached.

1. Training: Many popular GenAI models are trained on massive datasets, including a significant portion of the internet. During this training phase, the models learn patterns and relationships in the data. They build complex models to understand the structure of the information they use. It’s important to know that these models don’t have set answers; they learn what is likely to come next in a sequence. Think of it as a highly advanced form of “autocomplete.”
2. Prompting: The utility of the output is heavily influenced by the quality of the prompt. Best practice requires clarity and specificity in your request; make sure to provide relevant context to help the model, and modify your prompts as needed to improve the results.
3. Response Generation: The process of generating a response involves several key steps. First, the input prompt is broken down into smaller units called tokens. Then, it uses its training and learned patterns to predict the next token in the sequence. This prediction is based on a probability distribution learned during training. This process continues iteratively, token by token, until a complete response is generated.

The rise of reasoning models

A significant leap in the evolution of language learning models (LLMs) has been the development of reasoning models. These models represent a substantial improvement in usability for real-world, complex tasks such as financial reporting and analysis.

• Non-Reasoning Models are good at fast, fact-based tasks. They mainly use pattern matching to predict the next text in a sequence. For example, asking for the official language of a specific country would result in a rapid and direct answer.
• Reasoning Models use step-by-step logic and take longer to generate. The reason for the lag is that it mimics structured thinking in order to answer complex questions. For example, it can analyze data over the last fiscal year and produce reports and suggest future strategies.

Now that the basics are covered, let’s explore how GenAI can transform a financial services organization.

Why financial institutions need to pay attention

It’s predicted that 75% of banking jobs will be affected by AI in some way––a clear indication that AI cannot be ignored. It’s also reported that 16% of banks and 36% of credit unions have already started to implement GenAI, transforming their operations significantly.

It’s common for institutions to rely on in-house teams of data scientists and analysts, as well as different business intelligence (BI) tools and dashboards, to collect data, develop reporting, and surface insights. However, with GenAI, lenders are now empowered to:

• Modernize operations: Lenders can streamline workflows, automate tedious tasks, and accelerate processes. This means teams are freed to focus on what really matters: taking action to meet business goals and better serve customers.
• Gain a competitive edge and agility: Access to accurate and current data enables lenders to personalize customer experiences and innovate faster than the competition.
• Break down barriers: As mentioned earlier, AI democratizes data analysis, making insights easily accessible for lenders without specialized coding skills.

The potential for artificial intelligence to enhance operations is impressive. However, it still needs human oversight to check data and outputs. This is why choosing the right AI vendor to partner with is critical for success (and peace of mind).

Why choosing the right AI vendor matters

Choosing a vendor with expertise in both artificial intelligence and lending is crucial. They should have a proven track record of understanding the needs and requirements of lenders. This includes the ability to develop custom, purpose-built AI models while ensuring outcomes are accurate and free of misleading results or “hallucinations.”

Vendors must also be transparent about what security measures they have to reduce risk and protect sensitive data.

Which brings us to the next point: AI is not a “set it and forget it” solution. Partnering with a vendor that performs ongoing servicing, maintenance, and monitoring of models is paramount. This includes providing thorough onboarding and training for lending teams and access to their experts for ongoing assistance.

What’s Next?

Just like the internet, artificial intelligence is here to stay. The evolution of generative AI has demonstrated that it has transitioned from being a novelty to a necessity. To stay competitive, lenders must act quickly and strategically to adopt this technology, as it will enhance daily operations.

To continue learning about GenAI and how it can help your business, check out the 5 ways GenAI is reshaping lending. If you’re ready to get started, we invite you to reach out to our friendly team of AI-in-lending experts. We are here for you!