The Challenges of Using AI for Financial Research

In 2012, a popular American financial services firm, Knight Capital, lost $461.1 million due to a tiny technical mistake. As they implemented new software, it conflicted with older code that was supposed to be deleted. With no extra supervision, the company experienced massive losses. 

The event occurred before AI even existed. Now just think what would happen if people start trusting AI for financial research without any verification or clarification systems in the loop?

There is no doubt that AI has become advanced and is improving every day. The financial industry has already started using AI in financial analysis, planning, predictions, risk management, and research.    

With enough supervision and verification, using AI in financial research is fine for reducing time and focusing on highly complicated tasks. 

But it is important to remember that, in financial research, calculations and workflows, even a trivial mistake or typo in numbers, codes, or text can turn an organized financial plan into a huge fiasco, whether it is for personal purposes or organizational cash flow.      

So, before implementing AI in financial operations, you need to know the drawback of it. Here are the significant challenges and demerits of AI in financial research.      

Confident Hallucination

The most malicious feature of AI is hallucination. AI hallucinates facts and information, which leads to false research outcomes with fabricated facts and made-up data.  

Undoubtedly, this kind of false information and data is dangerous in any kind of activity involving finance. 

For example, if you are conducting research to predict future trends in a certain industry or market for budgeting, and the AI outcome shows false historical data or a false financial trend. Your whole calculations for future predictions and budgeting will be ruined if you use the AI outcome. 

These kinds of mishaps can lead you and your organization to colossal financial damage, just like Knight Group.    

To get rid of these kinds of huge mishaps, you can include an efficient AI detector in the workflow. Still, human oversight is essential to ensure a 100% accurate outcome and calculations.  

Output Quality 

As we are talking about hallucinations, another significant disadvantage of using AI is poor or unreliable output quality. 

The research output of AI solely depends on the data it is fed. It can only produce outcomes by analysing the sheer amount of data that the programmers input. 

Sometimes the programmer may input flawed data as well, which consequently affects the outputs.

But financial research requires very specific and accurate data and suggestions, which it may not provide.  

So, the output AI provides against a financial research may turn out to be shallow, surface-level, and biased. 

For example, if someone asks for deep insights or actually viable actions for debt management or retirement planning, that needs real human experience; AI will provide outcomes that the user might already know.  

Or AI provides a biased and one-dimensional solution that may not be applicable to that user. 

So, outputs like calculations, suggestions, or predictions AI provides are unreliable and insufficient, which raises a question about its output.  

Black Box Problem 

Another significant challenge you might face when you attempt to use AI for financial research is that it will not explain a certain suggestion or output, which technically refers to the black box model.  

A black box model produces a solution, or you can find a solution by analysing financial resources or information that was input, without justifying the rationale and process behind it. So, just like its name, the users remain in the dark or in a black box while using this model. 

By using this model or implementing an output by AI, the user will not be able to understand the process. So, the outcome might not be suitable for that particular issue, which may lead to a bad decision.    

For example, if an investor relies on black box AI and makes the decision to reject a loan approval for a small business venture, they will never know exactly which factor led to the disapproval.

Whereas the business venture might have lots of potential and could drive significant profit. In such cases, the investor cannot even justify the rejection or call it a fair decision. 

So, in financial decision-making or believing in predictions, trusting AI blindly is a big no-no.  

Data Privacy 

Financial data, whether it is personal or organizational, is always very confidential and sensitive. 

When you provide any bank account statement, annual revenue, cash flow of your company, and an income summary, you are exposing the information to an AI platform or finance management tool, which may use the information for multiple purposes. 

Moreover, you are making the information vulnerable to cyber attackers, putting it at risk of leakage, unless an AI system has a really solid process for data privacy. 

But the majority of them cannot ensure that they will keep the information safe or will not use it in AI model training. 

So, saving your financial data from getting compromised or becoming vulnerable is a difficult challenge you will face if you use AI assistance in financial management and research.      

Regulatory Compliances

Not to forget that every financial institution or multinational company has a set of regulations, compliance requirements, and ethical and legal boundaries.

These regulations and protocols sometimes conflict with AI usage in financial calculations and decision-making. On top of that, some of the AI platforms and tools may not meet the regulatory standards. 

For example, some institutions may have a regulation that they cannot disclose the financial information of their own company and their customers. But the moment you put an AI in the loop, you are required to provide the information to the system, which creates an obstacle. 

Again, in some companies, justifying certain decisions and operational processes is mandatory. But when you accomplish something with AI, you cannot always find the reasoning or justification behind some actions and decisions.   

So, these kinds of policies appear to be an obstacle to implementing an AI system for financial operations, for which you cannot use AI tools at all. 

Final Thought 

Financial decisions are the most important ones, for both personal reasons and organizational operations. So do financial research.  

You cannot compromise accuracy and certainty when you are doing research and analysis for a financial decision. So, solely depending on AI without proper supervision and verification is quite risky.  

But you can always use AI tools for some manual tasks with enough human verification in the loop, keeping the points mentioned above in mind.