How To Use GenAI for Finance?

Artificial Intelligence took the world by storm and quickly became a breathtaking technological advance.

Remember how the Jetsons aimed to predict machines solving daily problems?

Think of how the different branches of AI help users and companies in a myriad of industries! 

The age of Digital Transformation has placed huge importance on users’ autonomy.

This encloses daily operations from AI in retail to EdTech or online education.

Yet, there is still an intersection that, while explored, still holds huge potential.

We're talking about the confluence of Generative AI and the finance industry.

From a business standpoint, how can tech-driven, disruptive firms in the finance sector harness GenAI?

We’ll uncover it today!

What is GenAI?

Also called GenAI, Generative AI defines any type of Artificial Intelligence able to learn patterns from training data.

With the learnt patterns, it can create new outputs based on the same statical properties.

Generative AI models rely on prompts and transfer learning to make more efficient content generation.

In fact, you may have already interacted with examples of GenAI!

GenAI includes programs and platforms like Midjourney, Sora and the newly launched LumaLabs, among others. 

While often referred to as something new, GenAI has a long history, starting with chatbots in the 60’s.

Generative AI models were often built and developed considering only specific applications.

That was the case until the rise of Generative Adversarial Networks and other Machine Learning algorithms.

A great example of this scope is Google’s DeepDream, which was built to produce image-based visual effects.

However, it was not originally able to replicate its processing structure to other data types. 

Currently, many Generative AI models have become multimodal to keep up with more demanding times.

As a result, a single model can handle different data prompts and types to deliver convincing results.

These include texts with different intents and answering questions comprehensively.

It also extends to describing images via text, and translating text data from one language to another. 

What is Fintech?

An abbreviation of Financial Technology, Fintech refers to the use of technology to optimize and enhance financial operations.

Its applications go from our well-used banking applications to more complex blockchain integrations. 

The term first rose as a way to describe specifically financial institutions’ tech systems, like banks.

Yet, over the last few years, Fintech's scope has extended to enclose all customer-oriented financial services.

Today, we can see Financial Technology applied in industries like retail e-commerce and fundraising.

Likewise, the term also currently includes cryptocurrencies and other digital assets.

What is GenAI in Finance?

The Fintech industry has grown immensely in the last couple of years, but what competitive advantages does GenAI represent?

While Fintech is already a tech-driven field, there is an increasingly fast race to adopt Generative AI. 

A survey released by EY in Dec 2023 reveals that “77% of executives view GenAI as an overall benefit to the financial services industry in the next 5-10 years.”

Likewise, EY states that almost 50% of interviewed executives stated data processing risk reduction as the main benefit.

Other reasons include hyper-personalized marketing (38%) and improved data management (37%).

However, the same survey states that, in terms of implementing GenAI in the finance sector, companies face several challenges.

Some include lack of data infrastructure (40% of polled business leaders), lack of technology infrastructure (35%), and unclear governance framework (33%).

So, how is GenAI used in Finance, and how can finance leaders and teams ensure they make the most out of it?

The applications of Generative AI in the financial sector seem endless!

From anomaly detection to sentiment analysis, GenAI can customize products, enable transparency, and automate operations.

How do these seemingly general applications work on Fintech? Let’s explore three main applications of GenAI in Finance: 

1. Risk Assessment

A great example of the power of GenAI in finance is risk assessment.

Banks can use Machine Learning models and algorithms to determine loan eligibility.

This also helps provide options based on financial transactions and credit history, along with other aspects. 

A great example is JP Morgan Chase, one of the US’ largest banks.

The venture blends Data Science and industry expertise to make informed decisions on who to lend money to and under what conditions.

The firm even has an AI Research Publications division to share its AI knowledge and application procedures. 

2. Fraud Detection

GenAI can also enhance the financial sector by providing a robust framework for fraud detection.

Specifically in credit card fraud, GenAI can analyze user patterns, such as behavior, location, and habits.

This helps trigger alarms for fraudulent activities or if something doesn't seem right. 

This procedure is particularly useful given the rise of e-commerce and online retail.

With it, companies can offer strong safety-focused procedures and action courses.

There have also been AI-driven efforts regarding the battle against money laundering.

A study from Ayasdi concluded that its GenAI-based solutions cut over 20% on investigative workload.

3. Cyberattack Prevention

While customer satisfaction in the finance sector is often associated with customized assistance.

That's a key element to ensure long-term user loyalty is cyberattack prevention.

Yet, according to several studies, over 70% of data breaches are human-made. 

Generative AI has the power to analyze data patterns to alert companies of discrepancies.

Clients can rest assured in their data and assets safety.

What's more, they can trust the quick course of action institutions can take if they detect unusual activity. 

Benefits of GenAI in Finance

When discussing the benefits of GenAI in Finance, a great start is to discuss automation.

Generative AI streamlines and automates workflows to ease decision-making and upgrade service delivery. 

In the priorly-named application regarding cybersecurity, GenAI can analyze  network traffic to automate safety.

The benefits can also be seen in Conversational AI and chatbots.

Here, users can get answers in a user-friendly way at all times of the day.

What’s more, GenAI can also help companies achieve operational efficiency.

AI is key in reducing both data and document processing and its potential human-made mistakes. 

It’s always safe to pay attention to automated procedures.

Yet, freeing team members from repetitive tasks leads to a more productive workforce.

A study posted in March 2023 and made by students of MIT shows an increase of 37% in productivity.

This number states differentiates workers supporting their work with GenAI versus those who didn’t. 

Efficiency can also be seen in both the speed and availability GenAI has to offer in finance.

There’s no denying that AI is able to process information, find trends, and uncover data connections more quickly than humans. 

Companies can enhance faster data-driven decision-making in a wide range of tasks, from management to modeling.

Further, firms can gain a competitive advantage by presenting themselves as ever-available for users.

This helps them complete finance-related routine tasks in as many scenarios as possible.  

Conclusion

The future of finance with GenAI holds great potential for users and businesses alike!

For users, Generative Artificial Intelligence allows customized, 24/7 interactions with products that solve everyday problems.

It also ensures all procedures are being taken care of with cybersecurity as a priority.

For firms, it guarantees smarter decision-making procedures to ensure streamlined, data-driven operations.