Artificial Intelligence: Opportunities And Threats for Financial Institutions

What is Artificial Intelligence?

Artificial Intelligence (AI) has become one of the most popular topics in today’s technology space. It is also the subject of much debate as it presents amazing potential for advancements that can be used for commercial and social improvements, while at the same time facilitating criminal and terrorist activity. AI is already in widespread use and facilitates many of our daily activities. According to Chat GPT, the language model chatbot that was developed by OpenAI and is itself a form of AI, the definition of artificial intelligence is as follows: Artificial Intelligence (AI) is a branch of computer science focused on creating systems or machines that can perform tasks that normally require human intelligence. These tasks include learning from experience or data (e.g., Netflix recommends shows), understanding language (e.g., when Siri or ChatGPT answers you), recognizing images or sounds (e.g., facial recognition in phones), making decisions (e.g., self-driving cars choosing when to stop or turn), and problem-solving (e.g., playing chess or optimizing delivery routes). There are different types of AI, ranging from narrow AI (which does one task really well, like voice assistants) to general AI (which is designed to think and reason like a human).

Origin and History of Artificial Intelligence

Interestingly, the concept of artificial intelligence is mentioned in early Greek and Chinese mythology, but its roots are in the 1950s. Specifically, in 1956, a Dartmouth computer scientist, John McCarthy, coined the term “artificial intelligence.” He and others believed that human-level intelligence could be simulated by computers and this marked the formal start of AI research. Between 1960 – 1970, researchers built rule-based programs like ELIZA (a chatbot mimicking a therapist) and SHRDLU (an early natural language understanding computer program that allowed users to interact with a virtual "blocks world"). However, significant progress did not occur until the 1980s and 1990s when expert systems and smart algorithms were introduced. A famous example of early use is the victory of the IBM computer Deep Blue over chess champion Gary Kasparov in 1997. The next twenty years brought continued advancements with the widespread use of the Internet and advancements in machine learning with more computing power and larger datasets. Automated learning was initiated and neural networks evolved into deep neural networks which powered breakthroughs in image recognition, language, and game-playing. AI has since entered everyday life with the introduction of Siri, Alexa, Google Translate, self-driving cars, and facial recognition.

Artificial Intelligence Use by Financial Institutions

As artificial intelligence has become embedded in our everyday lives, it has also permeated the systems and operations of community financial institutions. Common examples include intelligent models and software for underwriting loans and evaluating investment portfolios. Security monitoring and response systems, including anti-malware applications and extended monitoring and response capabilities leverage artificial intelligence to learn new patterns and adjust responsive strategies accordingly.

Opportunities vs. Threats of AI for Financial Institutions

Opportunities for Financial Institutions

For community financial institutions, artificial intelligence offers a range of opportunities to improve efficiency, reduce risk, and enhance customer experience. Several areas where AI is already providing enhanced capabilities include the following.

Fraud Detection and Risk Management – AI models can analyze vast amounts of transaction data in real time to detect unusual patterns, reducing fraud and improving compliance with regulations.
Customer Service Automation – Chatbots and virtual assistants powered by AI can handle routine customer inquiries 24/7, cutting service costs and improving response times.
Credit Scoring and Underwriting – AI can assess creditworthiness using alternative data sources (like transaction history or behavioral data), enabling better decision-making and expanding access to credit.
Algorithmic Trading – AI systems can process market data at high speed to execute trades based on predictive analytics, helping firms capitalize on market trends more efficiently.
Personalized Financial Services – AI helps deliver tailored product recommendations and financial advice based on individual customer profiles and behavior.
Operational Efficiency – Automating back-office tasks (like document processing and data entry) with AI reduces manual workload and operational costs.

Threats for Financial Institutions

Financial institutions face several risks from the growing use of artificial intelligence. Specifically, AI can be used to accelerate the frequency and specificity of network attacks. Examples of potential AI threats include the following.

Fraud and Cyberattacks – AI can be weaponized by criminals to launch more sophisticated cyberattacks, such as deepfake-based social engineering or AI-driven phishing scams that are harder to detect.
Model Risk – AI systems, especially those using machine learning, can behave unpredictably. Poor training data, biased algorithms, or unexpected market conditions can lead to flawed decisions in lending, trading, or risk assessment.
Data Privacy Violations – AI relies on massive datasets. Misuse or mishandling of personal and financial data can lead to privacy breaches and regulatory penalties.
Regulatory and Compliance Risks – As AI evolves faster than regulations, institutions may unintentionally violate compliance rules or fail to meet transparency and explainability standards required by regulators.
Operational Vulnerabilities – Overreliance on AI for decision-making may reduce human oversight, leading to blind spots or breakdowns during unexpected situations where human judgment is still critical.
Market Manipulation – Malicious actors can use AI to exploit market signals or automate manipulative trading strategies at scale.
Third-Party Risks – Many financial firms use external AI services or platforms. If those vendors are compromised or flawed, the risks spill over into the institution.
Legal Risks – AI related legal issues have arisen that appear to both benefit and adversely affect financial institutions. Where once large teams of associates were required to sift through documents and find discrepancies, AI software can automate and enforce legal-hold obligations, ensuring that relevant documents, emails, and communications are retained in compliance with court orders and regulatory requirements. At the same time, financial institutions must ensure that litigation holds are meticulously maintained to avoid fines and legal exposure.

Strategies and Tactics for Addressing Artificial Intelligence

The impact of artificial intelligence on each financial institution will vary depending on their circumstances and the relevance of the various opportunities and threats discussed above. However, it is important for all institutions to understand their current position and the impact of artificial intelligence on their business operations. Specifically, an assessment of how the institution is directly or indirectly using artificial intelligence should be performed to ensure a full understanding of the potential future opportunities and the related risks. The following next steps are recommended to ensure that your institution is positioned to leverage AI technology appropriately and is also prepared for the external AI threat environment.

Assess Strategic Impact:
- Identify how AI affects core business areas (e.g. lending, fraud detection, customer service).
- Analyze both upside potential and risk exposure.
Develop a Clear AI Policy:
- Establish principles for ethical AI use (fairness, transparency, privacy).
- Define governance structure for AI oversight.
Invest in Talent and Infrastructure:
- Hire or upskill staff in AI, data science, and machine learning.
- Build or acquire the technology support needed for AI development and deployment.
Prioritize Use Cases:
- Focus on high-impact, low-risk applications first (e.g. automating routine tasks).
- Pilot AI solutions before full-scale rollout.
Strengthen Data Foundations:
- Ensure access to high-quality, compliant, and secure data.
- Improve data governance and integration across the organization.
Monitor and Mitigate Risks:
- Continuously assess risks like model bias, drift, and cybersecurity threats.
- Implement monitoring systems and internal audit processes.
Stay Aligned with Applicable Regulations:
- Track evolving AI-related laws and guidelines (e.g. EU AI Act, local financial regulations).
- Ensure models are explainable and compliant.
Engage Stakeholders:
- Communicate transparently with customers, regulators, and partners about AI use.
- Provide training and clarity to internal teams on AI initiatives.
Build for Long-Term Adaptability:
- Create flexible frameworks that can evolve with technology and regulations.
- Encourage innovation while managing risk.
Ensure Data Privacy and Security:
- Prohibit/discourage employee use of public AI services (e.g., cloud services) for business purposes, as their search and query activity may be monitored and saved.
- License tools and applications for secure in-house use of these services, if they are relevant for business functions.
Monitor Internal and External Environments:
- Continue to assess the effects of AI on the institution’s internal and external environments. Even if the institution is not directly using AI for internal purposes, consideration should be given to future opportunities. In addition, the external environment should be assessed on an ongoing basis for changes in potential threats and risks that warrant attention.

NETBankAudit has developed an Artificial Intelligence risk assessment methodology for community financial institutions which can assist your institution in evaluating its current risk posture and identifying related opportunities and threats. Please contact us for additional information.

 
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