In fintech circles, generative artificial intelligence (Gen-AI) is already very prevalent, and for good reason. Experts in finance, banking and technology have already expressed their excitement about GenAI's potent ability to obtain predicted insights and solve challenging problems.

Though Gen-AI has many benefits, it also presents a number of unique challenges that are changing the way financial institutions (FIs) function.

There are conditions associated with implementing any new technology in the financial industry, as every CFO or fintech leader knows. Let's get to the bottom of all the challenges that GenAI presents in finance.

6 Gen-AI challenges in finance you should know

Generative AI is transforming industries left and right, but in financial services, things aren't going so well. Almost every bank and financial institution (FI) values GenAI's predictive powers, but as a finance executive or CXO, you should be aware that Gen-AI introduces a new set of difficulties.

Here are some of them.

1. Limited data and privacy concerns

When it comes to finance, good data is essential for GenAI's predictive analysis. However, unlike other sectors, the availability of public financial datasets is quite limited. Why? Because financial information is both sensitive and brittle.

Nobody wants their personal financial information out in the open, and financial institutions are naturally concerned about data protection.

Consider BloombergGPT, for example. It's one of the few GenAI models designed specifically for finance, trained on massive amounts of both proprietary and public data.

However, not every organization has access to this level of data. Even if you do, data privacy concerns and legislation make it difficult to migrate data to the cloud for Gen-AI training.

So, is there a suitable solution? Experts suggest that synthetic data could help by providing false data that resembles real financial patterns while protecting privacy.

However, synthetic data has disadvantages as well such as accuracy and overfitting. Essentially, the more you hunt for excellent data for Gen-AI, the more privacy concerns arise.

1. Unique nature of financial data

Financial data poses distinct issues for generative AI models. Compared to other areas, financial data frequently deviates from the basic statistical assumptions that underpin many AI algorithms.

• Non-Gaussian distribution

AI often assumes data follows a normal (Gaussian) distribution. However, financial data, particularly stock prices, frequently does not—it contains "heavy tails" and skewness. This means it is more susceptible to abrupt, dramatic changes, which regular AI models may struggle to manage effectively.

For instance, stock values can fluctuate abruptly owing to crises or noteworthy developments, which AI algorithms may overlook based on traditional statistical assumptions.

• Complex interconnectedness

Financial markets are also highly intertwined. Economic variables, political events and company-specific news/development can all have an impact on worldwide markets. Traditional AI algorithms may overlook these subtle connections.

Geopolitical events, such as trade wars, can cause global volatility. Understanding complicated political and economic relationships is required to predict its influence, which goes beyond simple numbers.

• Time-Series nature

Financial data is a time series, implying every data point is dependent on the previous one. This sequential structure makes it difficult to accurately capture trends, especially when dealing with volatile and noisy data.

Identifying long-term trends in stock prices can be challenging for investors due to daily fluctuations.

Finally, you also have to consider the financial data quality and noise. These data may contain inaccuracies or missing numbers. Cleaning things up is necessary but difficult. If not done correctly, AI algorithms may produce inaccurate predictions.

1. The high cost of fine-tuning Gen-AI for finance

There is no one-size-fits-all approach to AI in the complex domain of financial transactions. Extensive fine-tuning must be performed for data-hungry Gen-AI models to comprehend the nuances and specific nomenclature of the financial industry.

One of the most significant challenges is the requirement for high-quality, human-labeled financial data. This data is frequently private, sensitive, and subject to stringent laws. Acquiring and processing such data can be costly and time-consuming.

CTOs and financial professionals working in banking and financial institutions often have to shoulder the enormous costs and hassles involved with this procedure.

Techniques such as Low-Rank Adaptation (LoRA) can be useful for fine-tuning financial models. However, applying these techniques necessitates specialized technical knowledge.

Plus, financial phrases might have different meanings depending on the context. For example, the word "bank" might refer to a financial institution, a real bank or a verb that means to rely on something. Fine-tuning the model to grasp these nuances complicates the procedure.

1. Enormous computation costs associated with GenAI models

Gen-AI models are computationally heavy, which makes them costly to train and operate. The sheer volume of financial transactions completed each day exacerbates the cost.

Training large-scale Gen-AI models from scratch requires a significant amount of resources and can cost billions of dollars. This is because training these models on large datasets requires a significant amount of processing power and energy.

Even after training, using these models to make predictions or generate content, often known as inference, can be costly. In finance, where billions of transactions take place every day, operating Gen-AI models at scale can be prohibitively expensive.

In short, the high price tag burden might be a severe limitation, especially for smaller financial institutions.

1. Regulatory compliance issue

Gen-AI models in finance also carry their own set of risks, such as data bias, data leakage and even "hallucinations" - when an AI generates false information. Consider the consequences of a Gen-AI model providing faulty financial advice based on incorrect data.

Bias is another issue. Different regions have different monetary legislation, and Gen-AI models may accidentally reflect these biases. This raises ethical difficulties, particularly when Gen-AI is utilized in credit rating or fraud detection. You do not want your AI to make biased decisions based on regional data idiosyncrasies.

Finally, privacy rules, such as GDPR in Europe, mandate stringent data protection. Even if data is destroyed, some models may keep remnants of it, posing a regulatory danger.

1. Getting the right talent

The skills gap is a major obstacle that many financial institutions have when adopting generative AI. Despite being masters in their fields, many finance professionals might not have the technical know-how needed to deploy and oversee AI systems successfully.

Additionally, you may experience difficulties finding top-notch GenAI expertise in finance who understand the complex subject matter of machine learning, data science and AI ethics.

For startups or small fintech companies, the cost of recruiting and training such expertise can also become unaffordable.

What’s the bottom line?

GenAI is opening up electrifying possibilities in the finance sector, but it’s not without its challenges. From data dilemmas to cost concerns, there’s a lot to ponder.

While many banks and FIs are very optimistic about the potential of generative AI in back-office operations, it is essential to approach its adoption with caution.

Staying ahead as a financial expert requires more than simply knowing the markets; you must also connect with top-tier talent, be aware of developing issues and invest proactively in the quality of your data. Continuous monitoring is essential for detecting and mitigating biases.

Finally, collaboration with finance subject matter experts (SMEs) and talented AI engineers can help you gain a competitive advantage.