What Steps Should Firms Take for the Deployment of AI in Finance?

Anurag Paul

12th Apr'24
What Steps Should Firms Take for the Deployment of AI in Finance? | OpenGrowth

AI is not just about time or money spending. Now, CFOs regard AI as a key to digital growth. 

"AI in finance is new." says Alexander Bant from Gartner.

"Early users often don't see expected returns.” he adds. To effectively achieve the business objectives, you should identify the right AI applications and focus on competitive improvement. On the other hand, you can prepare for autonomy in particular.

Recently, as per a report by NextMSC, 75% of people have started to use AI.

Here’s a quick fact for you to know the importance of the deployment of AI in finance:

The international market for AI was at $95.60 billion in the year 2021. By the year 2030, it may hit $1,847.58 billion. AI and ML play big roles in handling sensitive data. As AI spreads, we must consider its ethical challenges.


What are the Ethical Concerns With AI?

Here are our five main ethical AI issues:


AI decisions and their bias

AI models are driven by data and can adopt the biases within that data. This might result in AI systems showing bias to the data provided. It also reflects on the decision-making processes within the financial fields. 

A notable example is faulty facial recognition technology that incorrectly identifies people based on these traits.


No accountability

Determining who is responsible for business or deriving unethical decisions is a major issue. This complexity makes it hard to hold anyone accountable. 


Issues with privacy and protecting data

The heavy dependence of AI and ML on large volumes of user data raises concerns about privacy and surveillance. Collecting personal data heightens the chances of misuse or security breaches. 


Transparency is an Issue

Some AI and ML algorithms are so complex that their operations are not easily understood. This lack of understanding raises concerns about the responsible use of AI.


AI can be misused

AI technology could be used for malicious purposes like hacking or producing deepfakes. These malicious uses can result in identity theft, financial damages, emotional distress, and physical harm. 

Proactively addressing these potential abuses is crucial to prevent unethical uses of AI. Feeding the right financial terms into the AI system is also essential. So, make sure you know the investment terms as an entrepreneur.


Steps For Responsible Deployment of AI in Finance

Here are some steps to take for responsible deployment of AI in finance:


Define the problem

Start by identifying the issue that AI will tackle. Understand the issue's value proposition and whether AI can solve it. Consider its impact on stakeholders. 

AI in Finance


Choose the right tools

The issue decides the AI type you need to solve it. This might be machine learning, natural language processing, computer vision, or deep learning. Assess your data quality and the amount of data you have. 

Check your infrastructure's ability to scale and whether your systems are compatible with AI technology. 

Make sure your systems work well together. The right tools improve AI's work, speed, and trustworthiness. If your company deals with the stock market, analyze the risks of generative AI in the stock market. Then, you can choose the right tools to execute your desired outcome.


Build the team

Hire the right business, data, engineering, design, and ethics experts. Make sure they're skilled in AI tools and methods. Promote a learning culture, experimenting with new things, and giving feedback. 


Manage the risks

Handle the risks AI brings, like bias and privacy issues. Put an effective strategy into place that defines clear rules and governance. Check and review AI systems to control the risks efficiently. Besides, managing risks protects your good reputation and trust amongst your stakeholders.


Engage the stakeholders

Involve key people in your AI initiatives, including customers, partners, and regulators. Explain your AI goals and advantages with them. Ask for their thoughts, learn what they expect, and solve their concerns. Collaborate on developing solutions and share your knowledge with them. 


Learn from your mistakes

Test AI solutions before using them. After deployment, review how well they work. Learn from successes and failures to improve. Keep up with the new AI trends and emerging challenges. That’s the secret to financial success when you and your AI systems never stop learning.


Current Regulations and Guidelines for Ethical AI Deployment

Here are some regulations and guidelines currently in place for ethical AI deployment:


General Data Protection Regulation (GDPR)

The European Union (EU) introduced GDPR. It is responsible for protecting people's data privacy. Organizations must manage personal data carefully. GDPR provides more control over people's information. Its job is to ensure that AI and ML follow privacy rules and ethical standards.

Their guidelines also recommend that AI systems align with human values leaving no space for AI-based harm.


AI and ML Research Communities

AI and ML researchers’ communities are actively working on creating ethical guidelines. These initiatives are led by OpenAI and IEEE. They advocate for ethical AI, focusing on social and ethical consequences in research. These attempts motivate the researchers to look at wider ramifications.


Organization for Economic Cooperation and Development (OECD)

The OECD recommends responsible AI development. Its principles govern ethical AI deployment and focus on people-centric values, inclusiveness, and sustainability.


Other Government Initiatives

Many governments are introducing AI regulations. For example, the Algorithmic Accountability Act addresses AI system biases. These programs target developing ethical AI designs and applications that adhere to the regulations of the Act.

AI in Finance

Addressing Potential Biases in AI and ML Systems

To mitigate biases in AI and ML systems, businesses and organizations should take the following steps:

  • Ensure the diversity and representativeness of the training data.
  • Assess data sources for unintended biases.
  • Preprocess using techniques to reduce biases.
  • Carry out bias assessments, changing data weights as required.
  • Observe the real-life AI implementations to discover and eliminate biases.
  • Set up review boards to evaluate biases of AI systems.
  • Train employees in ethical AI for responsible understanding.
  • Promote diversity and inclusion in AI development teams.
  • Introduce transparency measures in AI decision-making processes.
  • Frequently refresh AI models to deal with changing biases.
  • Promote ethical reflections in the entire AI lifecycle.
  • Participate in industry-wide projects to exchange best practices and standards.
  • Look for independent audits or evaluations of AI systems for unbiased perspectives.



All in all, AI and ML bring about many opportunities; however, they also create moral and ethical issues. Dealing with these challenges involves reskilling in responsible AI practices. Through accountability in AI development, organizations can manage complexities and utilize AI for the benefit of society. 

Are you facing issues with the responsible deployment of AI in your financial institution?  Introducing Sameer Karulkar, your expert advisor for corporate development, investment strategy, and venture building. With over 25 years of experience in leadership roles spanning corporate development, private equity, and venture capital, Sameer brings unmatched expertise to the table. As a fractional expert, he offers strategic guidance to companies seeking cross-border growth, capital raising, and business monetization. Take your business to new heights with Sameer's expert advice!

Anurag has been writing content for over eight years, and he is dedicated to it and cannot see himself in any other industry. As a passionate writer, he is interested in business and entrepreneurship. An accomplished technologist and financial expert, he strives to empower others through entrepreneurship, leaving his comfort zone to explore entrepreneurship. Having worked in the Financial sector for more than five years as a full-timer, he also has a keen interest in Corporate Finance ...


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