Artificial intelligence (AI) is quickly becoming a cornerstone of innovation & growth within the financial services industry — fueling efficiency, deepening customer experiences, and enhancing data-driven decision-making.

With the increasing pace of AI adoption also comes new ethical challenges.

The consequences for missteps in this area can be severe: reputational damage, regulatory penalties, and a loss of customer trust.

In 2025, there will be greater visibility on all companies as it relates to the ethical application in artificial intelligence in banking & finance.

In part 2 (of 4) of our series on AI in Financial Services, we breakdown:

Ethics in AI as a business priority for companies and their clients;
Ethical challenges linked to AI in finance;
Regulators & industry groups proposing governance frameworks / guidelines;
Strategies for deploying AI systems that balance business goals AND ethics.

The Business Case for Ethical AI

Prioritizing the ethical use of AI in financial services is the ‘right thing to do’ — from a moral AND business perspective.

We discussed ways that artificial intelligence improves business activities by reducing costs, or generating revenue.

There are also qualitative benefits at the organizational level.

Transparent and fair AI enhancements build long-term customer loyalty, which can deepen the existing trust clients have with their provider.

Maintaining a proactive approach to identifying ethical issues reduces legal risk for a company (e.g. regulatory infractions).

The additional attention to detail creates sustainable & innovative solutions that deliver reliability and soundness.

Lastly, organizations committing to ethical AI adoption are able to strengthen their brand presence in comparison to peers that lack this level of prioritization.

Five Ethical Challenges of Applying AI in Finance

The concerns are rooted in AI-powered products & features magnifying an existing bias, breaching privacy protections, and/or systems lacking transparency.

The ‘black box’ stance of AI makes it even more challenging to address ethical challenges as some developers refuse to disclose data & criteria that go into a model/process.

This impacts trust and compliance obligations in an industry that needs to safeguard customers and adhere to regulations.

1. Removing Bias

Data is a critical input for AI-enhanced developments.

The type of data utilized to train AI models may carry a bias that creates an unfair or discriminatory outcome for users (such as customers or account holders).

Account screenings, loan applications, fraud review, and exception requests are all susceptible to negative outcomes related to biases.

In a loan example: historical data fed into a model was trained on demographic info in which certain applicants were declined by their zip code. This screening impacted customers who otherwise would be approved (damaging the reputation of financial institutions).

2. Clarity & Transparency

Deep learning components of AI systems are difficult to explain to everyday users and come with the label of ‘black box.’

Decision-making frameworks are opaque, which is a concern for banks that must be able to clearly explain processes to regulators (during an examination / review).

The data, rationale, and analysis of outcomes need to be transparent for financial institutions to ensure a high-level of trust and accountability (especially with clients).

3. Privacy and Data Security

Large volumes of actual data (i.e. user, transactional) are necessary to effectively train and improve AI models.

With more data comes the demand for more security against privacy breaches.

This leads to data security and privacy concerns becoming magnified for banks.

Data protections laws worldwide make it more challenging to stay in compliance with the latest regulations.

A key example is with marketing: leveraging AI to analyze customer spend and offer unique offers makes business sense. However, some clients may see this as a negative, invasive approach — leading to complaints.

4. Responsibility

A challenge that gets less visibility is what party (AI enabler, customer-facing business) is ultimately responsible for errors due to AI adoption.

In banking & finance, miscalculations in AI-powered decisioning can lead to large losses and penalties (most notably with credit & fraud-related processes).

The ‘black box’ of AI (discussed above) increases risk exposure even more for financial institutions and enterprises.

A foundational level of clarity with models and accountability in frameworks is necessary to minimize risk.

5. Automation

A broad concern (applicable to all industries) is AI’s ability to automate manual reviews cause humans to lose employment opportunities.

In job markets with minimal openings, job security becomes a heightened ethical concern for employers.

Efficiency and cost-cutting are key business goals, but must be balanced with what’s best for employees and the local job market.

To contextualize AI concerns with bias and transparency, here are two examples of industry leaders who struggled to leverage AI in everyday business operations.

Amazon’s Recruitment Model: The retail giant’s recruitment system was found to favor men in the hiring process based on historical data that discriminated against women;
Controversy with Apple Card’s Gender Bias: Within months of launch, the new credit card (managed by Goldman Sachs) drew heavy criticism from reports of women being granted significantly lower credit limits than men (who had similar credit standings).

These incidents highlight the need for rigorous bias testing and the importance of training AI models on diverse AND representative sets of data.

New Regulation in Favor of Ethics in AI

To support the need for ethical adoption of AI, regulatory agencies around the world are designing new laws and initiatives impacting the financial services industry.

Regulators are tasked with allowing for innovative growth while ensuring measures are in place to protect individuals and businesses from improper use.

The U.S. Algorithmic Accountability Act

Proposed legislation in the US prioritizes transparency and responsibility in any decision-making that is automated.

Impact assessments for AI systems would need to be conducted periodically to identify and mitigate potential negative outcomes.

The EU’s AI Act

The European Union takes a slightly different approach by focusing on classifying AI applications by risk levels.

The systems with the highest risk would have the most restrictive requirements (e.g. underwriting for lending use cases).

Industry Standards

Beyond regulators, industry organizations are designing their own frameworks for ethically deploying AI in their field — such as the Financial Stability Board (FSB) and Institute of Electrical and Electronics Engineers (IEEE).

Alignment on guidance and best practices ensures a uniform approach within a sector. These frameworks help institutions align with best practices.

How to Build Ethical AI in Financial Services

Addressing the ethical challenges of artificial intelligence requires a comprehensive approach.

Financial institutions & enterprises must prioritize ethical considerations in each stage of AI design, development, and deployment.

1. AI Design

Ethics should be a required part of the AI development lifecycle from the start, not just post-launch checkpoints.

Design processes should involve rigorous:

Bias testing: frequent testing is needed to ensure new systems remove bias and inclusive data is utilized for modeling.
Measuring for Fairness: Metrics must document ways to evaluate the outcomes of AI decisions for fairness, and remediate algorithms (or other inputs) as needed.
Manual oversight: Human review should not be completely replaced at the design level as it helps ensure proper decision-making, especially for sensitive deployments.

2. Transparent and Easy-to-Follow Models

Banks, startups, and enterprises need to ensure AI systems being built/utilized are clear and easily understood from the onset.

Optimize for simplicity: An emphasis on simpler models helps parties of all backgrounds (such as non-technical) to interpret processes and frameworks.
Pushing for Explainable AI (XAI): A growing concept in AI communities, XAI prioritizes tools and solutions that improve understanding when it comes to complicated AI systems.
Clear customer messaging: This should trickle down to everyday users, who are directly impacted by AI decision-making.

3. Robust Privacy & Security

Some of these seem straightforward, but as a combined framework help ensure proper safeguards with customer data, especially personal, sensitive details (e.g. Date of Birth, Social Security Number, Address, and Government ID info).

Reducing data capture: Minimizing data collection to only what’s necessary in performing a specific function;
Removing high-risk details (as applicable): Being able to anonymize data greatly reduces the chances of exposed personal data during privacy breaches;
Compliance guidelines: New regulations (such as GDPR, CCPA) must be followed. Ensuring controls are in place for adherence minimizes the likelihood of penalties.

4. Create Accountability Ecosystems

Innovative products with AI enhancements are great, but there are still checks & balances needed to monitor performance and address poor results due to errors.

Some structure of accountability can be created through:

Monitoring committees: A group of individuals dedicated to oversight of AI helps companies prioritize ethics & compliance throughout their deployments.
Audit records: Part of oversight includes frequent & documented inspections of decision-making processes that impact customers and an organization.
Service-Level Agreements (SLAs): Capturing liability (ownership) of AI-related errors uprfront (in SLAs) delivers prompt resolution of disputes, and increases customer satisfaction.

5. Organizational focus on Ethics & AI

There needs to be a top-down focus within an enterprise & financial institution of awareness and action towards ethical AI adoption:

Company-wide training confirms employees have a foundational knowledge of responsible use of AI, applicable to their role/ function at the company.

Culture of collaboration encourages feedback and improvements across teams in an organization — eliminating ‘blindspots’ in AI products & features development.

Gathering customer feedback from customers ensures their perspectives are being included in processes that directly impact their use of a company’s platform.

Finding the Right Balance is a ‘Work in Progress’

Artificial intelligence is transforming banking & finance globally — however, challenges lay ahead when it comes to ethical application.

Since AI is still in its early stages of adoption, there’s no set standard or examples to build off of.

For organizations, paving a path forward requires a focus on transparency, privacy, accountability, and fairness — in addition to innovation.

With 2025 being a critical year for enterprises & financial institutions to engage with AI, the callout is for company/business leaders to be proactive in pursuing innovation that’s in balance with ethics.

The desired outcome for the financial services industry is a sustainable ecosystem in which banking & finance activities thrive globally.

Stay connected with this series on AI as next up we explore part 3: “AI- Powered Risk Management Boost Banking Programs.”

Then, the finale (part 4): “AI and the Future of Finance: Opportunities and Challenges.”

For those that missed part 1: “How AI Redefines Financial Services in 2025.”

Responsible Innovation: Ethical AI Adoption in Finance (2 of 4)