Data Scientist Speaks: Debunking the Myths of AI in Finance

AI has the potential to do wonders for the finance industry. Already, financial institutions are using it for critical applications, like detecting fraud and identifying identities. However, it’s surrounded by hype, misconceptions, and unrealistic expectations – and any of these alone could derail projects. It’s important that we debunk these myths so that AI adoption in finance goes smoothly. 

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Myth 1: All Data Is Readily Available in a Single Source

In all large organizations, data is spread across business processes and departments. This means that there is no single source of truth. Here, most turn to relational databases, which rarely convey the whole picture. In practice, this data is still siloed and context-dependent. 

For example, banks and funds grapple with data spread across front, middle, and back-office systems, leading to inconsistencies and inefficiencies. What one department considers “the number” may differ from another’s definition. In fact, “truth” in business is rarely absolute – it is contextual. 

A risk analyst’s dataset might not match the marketing team’s customer metrics, and both can differ from what the CFO sees at an enterprise level. Rather than chasing a mythical single source, finance firms must integrate data sources and govern data with context in mind.

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Myth 2: AI Can Solve Every Problem (AI Is a Magic Bullet)

AI isn’t a magic bullet. The technology has an upper hand in automating repetitive tasks. It can reveal insights from large datasets that a human analyst may not – and if they can, it will take them five times longer than AI. When a technology starts doing wonders, it creates a flawed perception of what is possible.

For AI to be effective, it needs clear use cases and quality data. Rather than one omnipotent AI, companies often need multiple specialized models: one for fraud detection, another for customer service, another for risk modelling, etc. Each model then requires training on relevant historical data and careful tuning. This reinforces the first point: there is no single source of truth when it comes to data. 

In enterprises, AI models work best when they’re designed to perform a certain task. A model that is trained to assess credit risk will not generate a marketing strategy without explicit design. The model is powerful within the bounds of its domain of training, but it’s not a silver bullet for all the challenges. 

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Myth 3: Modelling Is All That Matters; Domain Expertise Isn’t Needed

Another common misconception is that a skilled data scientist can build an accurate model without deep knowledge of finance, relying purely on algorithms. AI model development is much more than data and algorithms, where human expertise in the finance domain is intertwined with the expertise of the data scientists. 

Domain experts, like traders, risk managers, or loan officers, provide context that pure data science alone may miss. They know which factors truly drive outcomes and can spot data issues that an algorithm might treat as noise. For instance, financial data is notoriously messy and non-stationary; “you can’t just apply deep learning to raw data and expect reliable results” – domain experts must clean and curate the data first. 

To make the models successful, pair data scientists with finance professionals. The former would bring modeling techniques, while the latter would guide feature engineering and verify that results make sense in the real world. This makes the model mathematically sound as well as economically and legally sound. 

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Myth 4: Reducing Data (Compressed Feature Space) Ruins Model Performance

During the training, providing AI with each detail sounds logical on paper. However, more data isn’t always better – intelligently reducing or compressing the feature space can improve models by removing noise and redundancy.

Dimensionality reduction techniques (like principal component analysis or feature engineering) “preserve essential features of complex data sets” even while reducing the number of input variables. In fact, simplifying the input space often increases a model’s generalizability by focusing on the signal and avoiding the “curse of dimensionality” (where too many features can cause overfitting and poor predictions). 

For example, an AI-driven investment model might start with over 10,000 raw financial indicators but distill them into ~250 engineered features that still “capture the nuances” of stock behavior. Those 250 features carry the core information needed for prediction, leaving out thousands of less relevant inputs. In short, quality beats quantity.

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Myth 5: AI is Objective & Unbiased 

Suppose you’d like to remove human bias from human decision-making, loop in algorithms. This statement sounds punchy and possibly seems appealing to leaders, especially in financial institutions. If we could loop in algorithms to make lending decisions and be completely objective, who wouldn’t want that! 

Unfortunately, that is where the old adage ‘garbage in, garbage out’ comes into play. If the algorithms are trained on flawed data, they will perpetuate the same flaws, whether they are racial or demographic. We need absolutely pristine data to ensure AI algorithms make decisions devoid of any bias. However, the caveat is that regulations hinder data sharing. Most of the data remains in the confines of banks. 

That’s the reality we face right now! If we don’t have the right data, we cannot make AI algorithms objective. To get the right data, we need to bypass regulations. Even when we deploy models in financial institutions, we must do it on-premises so that the data never goes to the cloud. 

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Conclusion 

The misconceptions surrounding AI in finance have clouded what is and isn’t possible. Understanding the myths can help see through the mist to deploy AI for actual use cases. We have reiterated the importance of data throughout this article. And while data is important, it alone cannot create models worthy of solving critical problems. 

We also need domain expertise to understand the intricate workflows, which then helps curate the models worthy of deploying in real-world contexts. Let’s start setting realistic expectations, so organizations can focus on the real work. It’s imminent that AI will be able to do everything that we’d like it to, but presently, dispelling these myths will help reap the true benefits of AI. 

At Arya AI, we have inculcated domain expertise to build production-ready AI solutions for finance. Connect with us if you’re looking for Enterprise AI systems for your bank or financial institution.