You are currently viewing AI in finance, maximizing opportunity and mitigating risk

AI in Finance, Maximizing Opportunity
and Mitigating Risk

5 min Read

Powerful, Transformative
Divisive, Risky?

AI is indeed an evocative term, encompassing a wide spectrum of implications. Its influence is increasingly felt across global industries, from operations planning and finance to the legal sector. In this article, we aim to provide a comprehensive examination of AI, exploring its potential utility and the associated pitfalls. Specifically, we will delve into how AI can be effectively utilized to enhance financial day-to-day operations and inform long-term strategic planning decisions.

Overview:

The Good

  • AI is best used in conjunction with large quantities of data.
  • AI offers some insights that may help make financial decisions.
  • AI automation helps reduce manual tasks in financial processes.

The Bad:

  • AI is not immune to making mistakes.
  • AI can be confidently wrong.
  • No technology is immune to software bugs.

The Good:

  • AI Shines At Data Processing:

AI rapidly processes large amounts of data to aid decision-making or fulfill the end user's requests. These capabilities have proven valuable across various sectors, notably with hedge funds increasingly favoring quantitative analysis over fundamental business analysis, accompanied by the rise of complex algorithms over the last decade.

Organizations that effectively leverage these data processing strengths can capitalize by integrating analytical capabilities into their informed decision-making processes for business operations.

Given the scale and velocity of their information flow, enterprise-level companies and global conglomerates are ideally positioned to harness AI for comprehending, aggregating, and making decisions based on continuous streams of operational data and revenue.

  • AI Can Be Helpful When Decision Making:

Because AI can process such large quantities of data so quickly, it recognizes trends and patterns at a rate that outpaces humans.

By speeding up this process of pattern recognition, organizations can examine the short-term impacts of business decisions, and the current state of operations in a timeframe that is closer to the now. By expanding analytical capabilities beyond the retrospective lookback, new doors to business agility are opened.

  • Automation Reduces Manual Tasks:

Automation exists outside the world of AI, but it is a feature worth considering. AI can automate manual processes and tasks, which is positive because manual tasks both impede time spent on analysis and increase the risk of error. AI is likely to perform these tasks more quickly than a human can. AI solutions in this area aren't required to be generative or holistic. Reduction of manual tasks through use of AI can be leveraged to increase productivity of the user as a point solution toward speeding up the modeling and analysis processes.

There are risks of error when using AI, but the automated aspects of artificial intelligence should be considered a net positive for the efficiency and capability they provide.

The Bad:

  • Mistakes & Wrong Information:

Humans know we make mistakes. While not ideal, this realization inspires vigilance in our work processes to limit or eliminate errors. In the world of finance, the effect of a single mistake can ripple out and be felt for years to come.

When an AI makes a mistake, it doesn’t always catch it, and we must be cautious about the trust we place in AI’s outputs. The same applies to information that is explicitly wrong. Both mistakes and inaccurate outputs are presented as answers and, therefore, must be viewed with caution.

The persuasiveness of AI was seen as a detractor in a recent study from BCG. Participants who used GPT-4 to engage in a business problem-solving exercise were observed to perform less well than their counterparts who did not use the platform. This exercise was explicitly designed for the platform to fail and isn't totally reflective of GPT-4's abilities as a whole. However, the study's findings speak volumes about the critical thought that must be applied, both to AI-presented output and to the question of 'Is this problem one that is best solved by AI?'

Given the complexity of AI, it’s difficult for users to delve into its inner workings and understand what went wrong.

  • Data Dependent (To A Fault):

Users must also consider that AI is reliant on data, and the ability to feed it data is dependent on their capabilities and infrastructure. The benefits of AI mentioned in this article are contingent on the ability to leverage reliable current and historical data that can be consistently updated.

If users don’t have the infrastructure to support their AI efforts, it may become another technical point of failure, complicating their financial processes.

Additionally, if the AI has been trained on a subset of data, there may be limitations depending on the parameters of that data. Factors such as the data source and the duration of data available for processing can affect the AI's perspective.

  • Bugs:

Like all other code-based technologies, bugs are bound to happen. The point to consider with AI is that its outputs are most often static, rarely revealing the methodology behind what is displayed.

No technology is completely immune to experiencing an error, and analysis may need to be done to double-check the work, which is counter-intuitive based on the main selling point of AI being speed and efficiency.

Running An Experiment (An AI Microcosm)

As an exercise we’ve asked an AI to compose a short outline of a similar article: here’s what it produced. The following is presented exactly as it was output.

Using AI to Write This Article:

The mention of regulatory compliance is a great addition; it’s an important consideration and a good use case for AI. The complexity and text-rich nature of regulation make it a great candidate for AI analysis.

The AI used to write this article had web access capabilities, it was asked to cite its sources for the article and provide links, but failed to do so. When prompted to reference the previous output and cite the mentioned sources, the AI generated a new output of the same article, but with source links. In the new output, sources were cited, with a mix of correct and incorrect links, including a 404 error. These references are highlighted in the article in blue.

While AI possesses redeeming qualities, it may not always provide optimal solutions. We've found that an effective strategy for utilizing AI is to establish a framework. AI excels at organizing structured data in response to specific prompts, which can be immensely beneficial for creating a foundation. However, this foundation will require scrutiny and fine-tuning. Nevertheless, it remains advantageous.

Is An AI Solution Right for You?

AI platforms vary in price and purpose, offering a wide range of options. From smaller plugins for spreadsheets to generative software capable of responding to text-based commands for financial operations, users have a variety of choices available.

Users must assess whether their needs require the use of AI and consider the qualitative aspects involved. Some of these aspects may not have been considered by even the most intelligent machines.

While AI has many functionalities, there are certain qualitative elements that it cannot currently replace. Creative iteration and design thinking, augmented by scenario analysis, allow finance professionals to explore novel ideas and find solutions that are successful but may not be explicitly data-driven. AI can still play a role in this process, such as automating manual tasks, but the primary driver of innovation in this example remains human.

Currently, AI is predominantly being advertised as a component of various platforms, aiding financial processes instead of being responsible for building a complete financial model. Is using an AI platform for operational and financial processes right for you? The answer as it stands is inconclusive. AI is not a new technology; robo-advisors have been in use for some time. However, it may still be premature to adopt a predominantly AI-driven financial solution.

For organizations with larger operational data sets or those whose financial day-to-day operations rely heavily on fast-moving financial data, AI may be a solution. This is particularly evident in fields where complex quantitative analysis has become a critical component of success.

Whatever you choose, make your decisions with more confidence and consensus by leveraging both your knowledge and a flexible financial model, regardless of how much AI plays a role. We don’t know if the perfect solution will come along tomorrow, or in five years. What we do know is that AI is an ever-evolving technology, so it is important to stay informed and aware of its capabilities, while making sure that the tools in your hands today are plenty capable themselves.

See what Synario can do for you

We started Synario for the same reason many of our clients started using it: we were tired of struggling with spreadsheets and their shortcomings. We needed a solution that was dynamic, adaptable, and promoted cross-team collaboration.

To answer this need, we created Synario: the agile modeling software organizations rely on to forecast and visualize their financial futures.

Are you ready to see for yourself what Synario can do for you?