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3 Ways Human Expertise Plays a Role in AI-Driven Finance

While AI unlocks formidable analytical capabilities, the human capacity for judgment, ethics and values remains irreplaceable.

By Kody Myers.

The most valuable asset in an organization’s adoption of artificial intelligence is its own people. While AI unlocks formidable analytical capabilities, the human capacity for judgment, ethics and values remains irreplaceable. Is your business incorporating these key human elements into your AI-driven processes?

Businesses Hope to Maintain Human Judgment Amidst AI Adoption

Finance has seen a transformative shift by integrating AI in predictive analytics and risk assessments. In fact, 57 percent of businesses have adopted AI and machine learning tools, according to a 2023 Paro Future of Finance Survey, with many businesses using AI for data-driven planning. This can include:

  • Financial forecasting: Predicting revenue and cost trends based on historical performance.
  • Budgeting: Evaluating capital investments and ensuring the optimal allocation of resources.
  • Credit risk assessments: Providing a more nuanced understanding of a company’s financial health.

This technology has equipped finance professionals with greater precision, speed in decision making and the ability to handle large amounts of complex financial data. However, while businesses agree that AI is an important tool, senior finance executives report that their greatest concern after data security is the loss of human judgment. An over-reliance on AI can have serious consequences for businesses, as AI models may lack certain qualitative information or values-based perspectives.

Human Expertise Remains Essential to Decision Making

As AI becomes more intertwined with finance, human interpretation becomes even more critical. Beyond the data, human cognitive abilities like common sense reasoning and contextual understanding are critical when interpreting AI outputs. These human traits ensure that financial decisions, while informed by AI, are also grounded in realities and nuances of the business landscape.

Consider a hypothetical example, using a scenario involving a multinational corporation’s investment strategy. After analyzing years of market data, the AI model suggests a significant investment in emerging markets, predicting high returns based on recent growth trends. However, the company’s CFO, with decades of experience and domain knowledge, recalls past volatile cycles in these markets and recent geopolitical tensions that the AI might not have factored in. Using their intuition and judgment, they decided to diversify the investments, allocating only a small portion to the emerging markets and the rest to more stable investments.

Months later, a sudden political upheaval in one of the major emerging markets led to significant economic downturns. Thanks to the CFO’s decision, the company’s investments remain largely unaffected, showcasing the value of human expertise in interpreting and actioning the AI’s prediction.

How Humans Augment AI-Driven Finance

There are three key areas where human expertise plays a vital role in this AI-human partnership:

Domain Knowledge

Finance experts have a depth of knowledge beyond what the numbers and algorithms capture. Contextual understanding is paramount in finance. Recognizing the broader implications of economic events or understanding the subtleties of market shifts can significantly influence corporate financial strategies.

Domain-specific insights play a crucial role in refining AI models. By integrating human domain knowledge, AI systems can deliver more accurate and contextually relevant predictions.

Experience and Intuition

Implicit knowledge, developed over years of hands-on experience, guides the most effective financial decisions. This understanding can be the difference between a winning and losing financial strategy, as illustrated in the example above.

While AI models offer robust analytical capabilities, they can begin to fail when presented with unprecedented market events like the COVID-19 pandemic. In these situations, historical data is lacking, which amplifies the importance of human intuition. It’s in these moments that the implicit knowledge of financial experts shines, providing valuable insights beyond what the raw data can offer.

Ethical Judgment

While AI can provide predictions and recommendations, the ultimate responsibility and accountability lies with humans. It’s the finance experts who need to make the final decision, weighing the AI’s output against their own expertise.

Human judgment ensures financial strategies align with ethical standards and company values. Blindly following AI outputs can misalign these values. Human judgment is crucial in avoiding potential missteps, ensuring that AI recommendations are assessed ethically before any final decisions are taken.

Effective AI-Human Collaboration

To harness the best of both worlds, organizations leveraging AI must integrate human insights into their processes. Combining AI algorithmic predictions with human oversight offers a balanced interpretation rooted in the reality of the current situation. This partnership ensures that AI provides data-driven insights and human experts validate and contextualize these findings.

Business should also establish a regular feedback loop between AI and the professionals interpreting it. AI is an iterative process that should allow for the continuous refinement of models, ensuring they remain relevant to your business.

Invest in Human Partnerships With AI

As finance continues to embrace tech advancements, the onus is on professionals to ensure that decisions are both data-informed and human-actioned. Organizations can not just simply invest in AI without having also invested in the human expert to develop the AI. 


Kody Myers, Senior Director of Product at Paro, brings a decade of product management experience fueled by a passion for AI-driven solutions. Kody thrives in the ambiguous environment of early-stage, high-growth startups, developing long-term product and data strategies. His entrepreneurial and financial background, coupled with the analytical rigor developed during his time in market research, positions him at the forefront of AI product innovation in finance.