AI Revolutionizing Fintech: Trends Reshaping the Financial Landscape

Kaja Grzybowska

AI Revolutionizing Fintech: Trends Reshaping the Financial Landscape

Generative AI has recently dominated media attention, particularly due to the remarkable yet sometimes hallucinatory capabilities of ChatGPT. However, Artificial Intelligence (AI), Machine Learning, Natural Language Processing (NLP), and Computer Vision have been instrumental in the business world for years. In industries overwhelmed with data, such as FinTech, these technologies are crucial for predicting trends, detecting fraud, analyzing consumer behavior, automating routine tasks, and streamlining operations. Their potential is significant and widely applicable across various sectors.

Artificial Intelligence, especially evident through the popularity of ChatGPT, is currently experiencing a surge in interest. ChatGPT is renowned for its ability to mimic human interactions impressively. However, a year after its debut, we have become acutely aware of its limitations, including its propensity for creating hallucinations and the challenge of its lack of explainability. These issues somewhat temper the initial enthusiasm for its wholesale adoption in business processes.

Nevertheless, ChatGPT has precisely highlighted the broader scope of AI - not just Generative AI - as more than just a novel tool to impress investors. It underscores AI's critical role in enhancing and transforming business operations.

Generative AI captured media attention, while Predictive AI, Computer Vision, NLP, and - above all - Machine Learning have been slowly but steadily reshaping business environments, industry by industry.


The Evolution of Fintech and AI

The financial industry is at the forefront of this technological race, implementing intelligent solutions such as advanced analytics, process automation, robo-advisors, and self-learning programs. This movement started long before the generative capabilities of AI were fully realized.

In 2021, nearly half (49%) of financial services firms have full-scale AI deployment.

source: Zipdo

Initially, AI-related innovations focused on human-made, rule-based frameworks for storing, sorting, and manipulating data. This early form of rule-based automation eventually evolved into robotic process automation (RPA), which garnered significant appreciation from banks, insurance companies, telcos, and utility firms. Despite the growing prominence of Generative AI, RPA remains a vital component in the industry.

A 2020 survey by Deloitte states that 53% of respondents had started to implement some form of RPA, and this percentage was projected to increase to 72% by 2022.

This is expected to increase. If this continues at its current level, RPA will have achieved near-universal adoption within the next five years.

The ability to integrate legacy systems is the key driver for RPA projects. By using this technology, organizations can quickly accelerate their digital transformation initiatives, while unlocking the value associated with past technology investments.

- said Fabrizio Biscotti, research vice president at Gartner.

What is RPA?

Robotic Process Automation (RPA) is a form of business process automation that utilizes software bots to automate repetitive tasks traditionally performed by humans. These tasks can include data extraction, form filling, moving files, copying data between applications or workflows as required.


While RPA can incorporate elements of AI, it is significantly distinct from it. RPA is primarily rule-based, focusing on 'doing' tasks according to predefined scenarios, whereas AI and machine learning involve more 'thinking' and 'learning.' This distinction makes RPA excellent for automating straightforward, rule-based workflows but less effective in tasks requiring learning, adaptation, and complex decision-making.



(Robotic Process Automation)

(Machine Learning)





Repetitive, rule-based

Complex decision-making, data interpretation




Key feature

No learning abilities

Learning from data over time


Tech arm race in FinTech

The finance industry, particularly sectors like investment banking and fintech, is known for its data-intensive nature. This has driven a focus on increasingly innovative data analysis methods. Consequently, technologies like RPA, Machine Learning, and more recently, Generative AI, have garnered significant interest in these sectors.

Moreover, following the 2008-2009 global financial crisis, which underscored the consequences of inadequate risk management, financial institutions realized that many firms' IT infrastructure for information was insufficient to accurately monitor risks. There were suggestions for an urgent need to shift from intuition-based to data-driven approaches.

Banks faced the challenge of reinventing themselves while also confronting the new FinTech competition. Instead of simply competing with disruptors, many of them chose collaboration. Financial institutions started embracing new technologies not only to streamline operations and improve customer experiences but also to reduce costs and compete with emerging, agile startups offering alternative financial services.

This race towards innovation has cultivated a culture that continuously seeks to enhance internal and external processes with increasingly sophisticated automation.

Key Drivers of AI Integration

Data Abundance

AI models, especially those based on Machine Learning, require large amounts of high-quality data for effective training. The quantity and quality of this data directly impact the accuracy of predictions, the relevance of recommendations, and the ability to automate processes. Before implementing AI solutions, a company must assess their feasibility, and one of the first steps involves evaluating the data flows within its processes.

AI - regardless of which subset of AI we are talking about - is only as good as data that "feeds" it.

This kind of evaluation takes into account several factors such as:

  • Consistency

This refers to a data format that should be aligned across different systems. Inconsistency leads to problems when trying to analyze or use that data.

  • Accuracy

Inaccurate data can come from many sources, including human error, system glitches, or data transfer problems.

  • Completeness

The gaps in dataflows can happen for a variety of reasons, including data not being collected, data being lost in transfer, or data being accidentally deleted.

  • Duplication

Duplicate data entries can cause a lot of confusion and can lead to incorrect analysis. This often happens when data is merged from different sources without proper checks for duplicates.

The financial industry due to its document-heavy nature, corporate procedures, and solidly established workflows was The financial industry due to its document-heavy nature, complex corporate procedures involving many repetitive and standardized tasks, and solidly established workflows was typically well prepared to embrace AI. well prepared to embrace AI. 

Enhanced customer experience

Large volumes of data within financial companies present opportunities that banks and insurance businesses are eager to exploit, particularly since the benefits of AI directly enhance customer experience, mainly through personalization. 

According to a McKinsey report, 76% of consumers say they’re more likely to purchase from brands that invest in personalization

This personalization can be achieved, for instance, through Natural Language Processing (NLP), augmented by Large Language Models (LLMs).

These technologies enable companies to understand the context of customer queries and provide accurate, tailor-made responses in a natural, human-like manner. AI-powered chatbots can effectively handle parts of conversations typically managed by frontline employees, such as call center agents, freeing up their time to address more complex issues.

The operational cost savings from using chatbots in banking will reach $7.3 billion globally by 2023, up from an estimated $209 million in 2019, according to Juniper Research.

Computer vision can also significantly expedite the KYC (Know Your Customer) process by analyzing documents like IDs, passports, and utility bills. These algorithms verify document authenticity and ensure regulatory compliance, not only reducing processing times but also enhancing accuracy. This enables financial institutions to onboard customers swiftly and securely.

Risk Management and Fraud Prevention

Computer vision and machine learning serves as a powerful tool also in fraud prevention and risk management. Computer vision algorithms excel in scrutinizing transaction trends, monitoring user actions, and processing live data, enabling the identification of irregular and dubious activities. 

American Express improved fraud detection accuracy by 6% with deep learning models and BNY Mellon improved fraud detection accuracy by 20% with federated learning.

source: Nvidia

Furthermore, the application of facial recognition for identity authentication provides an additional safeguard. The adeptness of computer vision in pinpointing irregularities within extensive data sets empowers financial entities to reduce risk and protect their resources.

Efficiency and Automation

And last but not least - efficiency is boosted by AI-driven automation. Machine learning applications bring cost reductions surpassing 10% upon the successful integration of ML into their business operations, according to Nvidia via Crata AI

RPA and later Machine Learning were perceived as the techniques able to replace the most mundane, manual tasks while minimizing errors and increasing productivity. By leveraging automation and AI, organizations could optimize their operations, reduce costs, and enhance their overall performance. 

IBM reports that organizations can save up to 95% of costs by automating infrastructure management using AI. 

source: Utilities One

Ethical Considerations 

The integration of Artificial Intelligence (AI) can bring numerous benefits and is certainly a game worth playing. However, there are several serious ethical considerations that must be taken into account. FinTech, in particular, processes a vast amount of sensitive information and must exercise thorough caution.

AI systems inherently require extensive, high-quality data to deliver valuable insights. As such, they often contain sensitive personal information, which raises concerns about privacy rights. Users inherently own their data and have the right to know how and why it is being used by companies. With manual processing or even RPA-driven automation, explaining the data processing is relatively straightforward: we can trace how the data is processed and identify what exactly impacts the final output.

However, with more advanced AI or Machine Learning, this 'explainability' becomes difficult. The more complex the AI algorithm we are dealing with, especially the deep learning algorithms that power Large Language Models, the more challenging it is to trace its 'train of thought'. Consequently, these algorithms are hardly transparent, and decisions based on their suggestions cannot be fully explained.

The European Union unveiled its first draft of the AI Act in April 2021, which recommended the implementation of different levels of regulatory oversight for AI systems depending on their intended uses. According to this proposal, AI applications in high-risk sectors like law enforcement would require procedures such as risk assessment and the adoption of mitigation measures.

Navigating Tomorrow's Financial Landscape

The financial industry is undergoing in-depth and fast-paced changes due to the advent of AI, and it seems that we are on the brink of a real revolution. However, with this promising technology comes great responsibility. In the case of financial institutions, building trustworthy AI is definitely key.

Trust is the bread and butter of the fintech industry, and as we witnessed during the Global Crisis, it is not a given. Even though reducing costs, streamlining operations, and enhancing efficiency might be the ultimate goals for decision-makers, they must also pay close attention to security, privacy, and transparency while embracing AI.

Implementing AI on a large scale can be challenging and costly. It may necessitate the use of on-premise solutions, requiring the building and maintenance of robust data infrastructure powered by significant computing power. The alternative, such as using a closed AI API provided by entities like OpenAI, carries risks related to security and can be cost-prohibitive due to a pricing model based on usage (although it may be suitable for Proof of Concept (PoC), closed API is questionable for production-ready solutions).

Building on-premise solutions, on the other hand, demands AI experts specialized in technical, legal, and business areas. However, as we are dealing with emerging technologies, there is a talent shortage in the market.

And one more aspect - R&D culture. Investing in AI demands a specific, scrappy attitude with built-in openness to innovation and risk acceptance. Sometimes it is difficult to foresee the exact ROI, making it challenging to convince shareholders to approve such projects.


The rapid integration of Artificial Intelligence (AI) in the financial sector is significantly reshaping its landscape. Key to this transformation is the deployment of AI-driven automation, including technologies like Robotic Process Automation (RPA) and Machine Learning, which streamline operations, minimize errors, and enhance efficiency. However, the adoption of AI brings with it ethical challenges, especially concerning data privacy, security, and transparency, given the sensitive nature of the data involved.

Kaja Grzybowska avatar
Kaja Grzybowska