Even though the world took some time to recover from the pandemic and the Ukraine-Russia war, the BFSI sector is booming.  

According to the business research company, the global banking, financial services and insurance (BFSI) security market size grew from $53.23 billion in 2022 to $59.79 billion in 2023 at a compound annual growth rate (CAGR) of 12.3%.  

And therefore, banks, insurance firms, and financial service providers are aiming to leverage Artificial Intelligence and Machine Learning to foster added productivity, better customer service, and overall reduction in the cost of operations.  

Though the use of predictive AI and the available open AI tools are meant to simplify BFSI customer targeting, EU even takes aim at tools like ChatGPT and Google Bard to get a handle on AI use.  

In other words, the integration of all these technologies have raised the question on AI (Artificial Intelligence) ethics, data governance, legality, and trust. Considering the use of customer data and meeting the privacy goals, New EU AI act has identified and flagged the AI systems for high-risk possibilities that BFSI organizations need to watch through integrated BFSI testing solutions and effective compliance management.  

So, let’s explore the relation between AI, ML, and Big Data while walking through existing regulations surrounding their use. Besides, we will map out the new EU rules for trustworthy AI and how organizations could prepare for it.  

What EU AI ACT Says 

Though there are no new rules rolled out or exercised, the EU has published a draft on AI act in April 2023. It is a legislative framework that controls the use of AI and is set on public trust. The act is originally defined to address the organizations working on AI systems and is likely to show its implications on BFSI firms across the European boundaries. Some of the possible outcomes that we can witness with the new EU rules include:  

  • With the new EU AI act, the commission aims to protect the tech users against the wide set of complications or harm that may occur with the misuse of personal information.  

  • Defined on a risk-based approach which puts all the AI used to check on past credit history, work performance, and behavior of the user under high-risk category. Also, there are certain AI systems that are likely to be prohibited with the new AI act, but these systems are likely to have or show minimum application to BFSI.  

  • Based on the regulations, any organization that develops or uses AI-based high-risk systems are likely to witness strict rules that will be subject to conformity assessments as well as requirements to register.  

  • The rules defined with EU AI act would be complicated as they will address risk management, accuracy, data quality, documentation, human oversight, robustness, security, and transparency.  

  • Above all, the inability to align with the defined regulations will accommodate hefty fines for any incidence of non-compliance, which might be 6 percent of global turnover of the organization or up to €30 million. 

Even if the implementation has not been finalized, the available set of guidelines and information need BFSI firms to prepare well in advance. 

How Can EU AI Draft Impact BFSI Firms? 

It's important to note that the specific provisions of the EU AI Act and its impact on the BFSI sector may evolve or change before it becomes law. It's advisable to refer to the latest version of the act or consult legal professionals and industry experts for the most up-to-date and accurate information. However, some of the few important factors that BFSI firms need to understand in context to EU AI act includes: 

  1. Regulation of AI systems: The EU AI Act aims to classify AI systems into different risk categories, with higher-risk systems subject to stricter regulations. In the BFSI sector, where AI is already being used for applications such as risk assessment, fraud detection, and customer service, there could be increased scrutiny and requirements for ensuring the safety, reliability, and transparency of these AI systems. 

  1. Ethical considerations: The EU AI Act emphasizes ethical principles such as human oversight, non-discrimination, and transparency. This means that AI systems used in the BFSI sector would need to adhere to these principles, potentially impacting areas such as credit scoring, loan approvals, and insurance underwriting. 

  1. Data governance: In the BFSI sector, where data is crucial for risk assessment, fraud prevention, and customer insights, financial institutions may need to ensure compliance with data protection regulations and establish robust data governance frameworks. This could involve implementing privacy-preserving techniques, obtaining appropriate consent for data usage, and ensuring the security of sensitive financial information. 

  1. Increased regulatory oversight: The EU AI Act proposes the creation of a European Artificial Intelligence Board and a network of national supervisory authorities. These bodies would be responsible for monitoring compliance, conducting audits, and enforcing the regulations. 

  1. Market impact: Some financial institutions may need to re-evaluate their AI strategies, invest in compliance measures, and potentially modify or replace existing AI systems to meet the new requirements. This could result in a temporary slowdown in AI innovation and deployment until regulatory compliance is achieved. 

Establishing The Relation Between AI, ML, and Big Data 

Since AI and ML are becoming the new normal with more and more organizations adopting the technologies, the use of these technologies along with Big Data is quickly expanding. While Big Data becomes the base for training AI algorithms, the tech enthusiasts are harnessing AI to understand big data better.  

Besides, it is necessary that Big Data is used wisely and must meet the GDPR compliance goals, to ensure lawful and fair use of the information. On the other hand, it is equally important to create more reliable AI and ML systems that could complement more trustworthy decisions.   

Aligning The Trio For The Best 

To yield the best of automation, predictability, and reliable output, AI, ML, and Big Data must be well integrated. Though the use of these technologies comes as challenge to BFSI firms, the outputs driven through the process could enable businesses to thrive in the right direction.  

On top of that, AI and ML paired with Big Data create space for opportunities in the BFSI sector by simplifying the transactions for consumers, reducing the effort, and ultimately adding to the revenue generation. For instance, a user paying electricity bills through a payment management portal could be advised on savings through personal recommendations to switch electricity suppliers.  

However, the process further needs to ensure that all the recommendations generated must clearly state to a user that the given recommendations are made in regard to the best price options in the market or are shared considering the best for the user from a list of preferred suppliers.  

Modern Financial Data Privacy Laws And The EU AI Act 

Talking about the present situation, there are no straightforward regulations that are made to secure and improve the use of AI technologies. Though GDPR addresses several concerns surrounding use of personal data or the development of AI systems, the other regulations developed around the use of AI and ML contain so many gaps and inconsistencies. These regulations have not only confused the business rules but even affect the confidence of using AI. 

Wondering How AI Has Enabled Transformation Of Challenger Banks? 

Read Here: Artificial Intelligence: A Game Changer for Challenger Banks 

Modern financial data privacy laws, such as the General Data Protection Regulation (GDPR) in the European Union and other similar regulations worldwide, play a significant role in protecting the privacy and security of personal and financial information. When considering the potential impact of the EU AI Act on the BFSI sector, it's important to understand how these data privacy laws intersect with AI regulations. 

  • Data protection and user consent: Financial data privacy laws emphasize the protection of personal data and require explicit consent from individuals for its processing. The EU AI Act is likely to reinforce these principles by placing additional obligations on organizations using AI systems. Financial institutions operating in the EU would need to ensure that their AI applications comply with the GDPR and other relevant data protection regulations, including obtaining proper consent, implementing appropriate security measures, and providing transparency about data usage. 

  • Data minimization and purpose limitation: Financial data privacy laws promote the principles of data minimization and purpose limitation, meaning that organizations should only collect and process the data necessary for specific legitimate purposes. The EU AI Act may align with these principles by requiring organizations to demonstrate that their AI systems are designed to minimize data collection and usage, ensuring that AI applications are focused on specific purposes and not used beyond their intended scope. 

  • Algorithmic transparency and explainability: Financial data privacy laws don't explicitly require algorithmic transparency or explainability. However, the GDPR includes provisions on individuals' rights to understand the logic behind automated decision-making processes. The EU AI Act builds on this by emphasizing transparency and explainability of AI systems. Financial institutions using AI algorithms for customer profiling, credit scoring, or other automated decision-making processes may need to provide explanations and ensure that individuals have the right to challenge and understand these decisions. 

  • Cross-border data transfers: Financial data privacy laws, including the GDPR, impose restrictions on the transfer of personal data outside the European Economic Area (EEA) unless appropriate safeguards are in place. The EU AI Act may introduce similar provisions to ensure that personal data used by AI systems is adequately protected when transferred across borders. This could impact financial institutions operating globally or utilizing cloud-based AI services that involve the transfer of customer data outside the EEA. 

  • Data governance and accountability: Financial data privacy laws emphasize the importance of data governance, accountability, and security measures to protect personal and financial information. The EU AI Act is likely to align with these principles by requiring organizations to implement appropriate safeguards, conduct risk assessments, and demonstrate accountability in the development and deployment of AI systems. Financial institutions would need to ensure compliance with these requirements, including implementing measures to secure AI models, prevent data breaches, and maintain comprehensive records of AI system operations. 

It's essential for financial institutions to assess the potential impact of the EU AI Act on their operations, particularly with regard to data privacy laws. Organizations should stay informed about any updates or revisions to the EU AI Act, consult legal experts, and conduct thorough assessments to ensure compliance with both AI regulations and financial data privacy laws. 

Preparing For The EU AI Act 

Just like EU’s GDPR guidelines, the impact of upcoming EU AI act is very likely to be far-reaching. Therefore, any organization that is developing, deploying, or using AI-based solutions must begin with risk assessment on all their high-risk AI systems. It will not only enable the organizations to learn the time and effort needed to improve the situation.  

However, with limited guidance available, the organizations need to leverage self-regulation to confirm that any AI technology or module in use must be properly governed. Besides, the development and use of AI testing solutions, both will require the operations departments and boards to understand who owns the knowledge to verify decisions made using AI.  

Preparing For EU AI Act

Here are a few steps that could enable business organizations as well as IT brands to prepare for the EU AI act: 

  1. Understand the regulations: The first step for any organization is to understand the proposed regulations. Read the EU AI Act in detail to identify any potential implications for your business operations. 

  1. Assess your AI applications: Identify which of your AI applications fall under the scope of the EU AI Act and assess their compliance with the proposed regulations. This includes assessing the level of risk associated with each application and identifying any potential ethical concerns. 

  1. Establish a governance framework: Develop a governance framework that aligns with the EU AI Act's principles and guidelines. This includes developing policies and procedures for the development, deployment, and use of AI applications within your organization. 

  1. Ensure transparency: Ensure that your organization is transparent about the use of AI applications and that stakeholders are informed about the intended benefits and potential risks associated with their use. 

  1. Implement accountability measures: Establish accountability measures to ensure that your organization is responsible for the actions and decisions made by your AI applications. This includes developing processes for handling complaints, providing redress, and ensuring that AI applications do not perpetuate bias or discrimination. 

  1. Invest in training and education: Ensure that your employees are adequately trained on the principles and guidelines of the EU AI Act. This includes providing education on the ethical implications of AI and training on the responsible use of AI applications. 

By taking these steps, business organizations can prepare for the EU AI Act and ensure that their AI applications are developed and used in a responsible and ethical manner. This can help build trust with stakeholders and ensure compliance with the proposed regulations. 

Concluding It All... 

When it comes to driving benefit to the banking operations, streamlining financial services, and complimenting insurers, the combination of AI, ML, and Big Data could deliver extraordinary stability and integrity. Also, Big data for digital enterprises as a tool holds massive potential transforming the operations, deliveries, and output.  

However, the process is likely to increase the competition with rising concerns on ethics, trustworthiness, and responsible use of these technologies.  

More importantly, the ever-increasing extent of the laws relating to AI and big data could make it difficult for BFSI firms to navigate. The gaps, inconsistencies, and overlaps within the current approaches by regulatory organizations could confuse the organizations globally while cutting the confidence business world seeks in AI.  

Nevertheless, the organizations that need to stay afloat in the competition will have to align with the rules and take over the potential possibilities for success. The process therefore requires assistance from quality engineering teams that are well-versed with the guidelines to meet compliance. Moreover, it is crucial that BFSI firms must embrace these regulations quickly to ensure they have all the right resources and intel to scale and improve with the changing legal circumstances. 

Good Luck! 

Need help testing your BFSI application for optimum performance while meeting the compliance goals surrounding AI, ML, or use of big data? Get the necessary assistance from our experts at BugRaptors.  

Reach our team through info@bugraptors.com  


Vivek Rana

With rich experience of more than 10 years in the industry, Vivek Rana is a QA enthusiast working as a Team Lead at BugRaptors. Starting his journey as a system Analyst, Vivek over the years not only developed a strong grip on manual and automation testing services in different domains and different testing types but also ensures the release of quality of software products and system and manage software test teams. His fun-loving approach and whole-hearted dedication make him a perfect team player. He is a highly driven expert to improve the QA process and loves to travel to mountains escaping the city hustle and bustle whenever he longs for some leisure.


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