Risk Watch with Alberta Quarcoopome: Benefits and challenges of AI in risk management

“Artificial intelligence will take all our jobs and that’s not necessarily a bad thing. Probably none of us will have a job,” – Elon Musk .

From the lenses of the risk telescope

In my regular interaction with young professionals, it is becoming apparent that young people are gradually getting apprehensive about their future. During a recent  chat with a thirty-four year old IT professional, (whose profession has always been known to be untouched by the AI revolution), he expressed concern about the rate at which AI is gradually making their profession extinct! But how, I asked him. He explained that even though AI was developed by IT, it is now capable of conducting research and churning out information which is making them replaceable. He shuddered and couldn’t even imagine what holds in store for him in the next five years. He was pitying the future of his daughter in the next twenty years. As a non-IT person, I am relying on various programs attended on the topic, together with relevant literature on the subject quoting the sources.  It is strictly an opinion piece.

A blessing or a curse?

Here are some predictions made from the McKinsey Global Institute in 2024.

• It is estimated that 97million jobs would be created by

the AI revolution

• Emerging report from LinkedIn indicated that demand for

AI related roles surged by 74%

• AI has eliminated 1.7 million jobs since 2000
• It is estimated by the World Economic Forum that AI will

take 85 million jobs by 2025

• At least 14% of employees globally would be replaced by AI.

The benefits of AI in banking

The benefits of AI are enormous. Gone are the days that one had to manually search for information in order to take a simple decision. Look at how risk identification has improved:

Risk identification:

? Data Analysis: AI can analyze vast amounts of structured and unstructured data, identifying patterns and trends that may not be immediately visible to humans.

? Early Detection: AI systems can detect potential risks early by continuously

monitoring data in real time, leading to proactive rather than reactive responses.

? Predictive Modeling: AI uses historical data to predict future risks, allowing

organizations to prepare for possible scenarios. This is particularly useful in areas like financial risk, supply chain disruptions, and cybersecurity.

? Scenario Simulations: AI can simulate various scenarios to test the resilience of strategies and make informed decisions under different conditions.

Risk Diversification

? Portfolio Management: In financial sectors, AI can assess the risk profiles of various assets and suggest optimal diversification strategies, minimizing potential losses.

Enhanced Decision-Making

? Real-Time Analytics: AI provides real-time insights, helping businesses make faster and more informed decisions about risk mitigation.

? Bias Reduction: AI can remove human biases from decision-making

processes by relying on objective data and algorithms, leading to fairer and

more consistent risk assessments.

Cost Efficiency

? Automation: Automating routine risk management tasks through AI reduces the need for manual intervention, lowering operational costs and freeing up resources for more strategic tasks.?

Fraud Detection: AI systems can efficiently detect fraudulent activities by

flagging suspicious behavior and anomalies, helping to minimize financial

losses.

Adaptability and Scalability

? Continuous Learning: AI algorithms can learn and improve over time by

analyzing new data, making them adaptable to evolving risks and regulatory

changes.

? Scalability: AI-powered risk management systems can handle increasing

volumes of data and complexity, making them suitable for both small

businesses and large enterprises.

Improved Compliance and Reporting

? Regulatory Compliance: AI helps companies stay compliant with ever

changing regulations by monitoring compliance metrics, tracking regulatory

changes, and automating reporting processes.

? Audit Trails: AI systems can maintain detailed audit trails, which is valuable for regulatory reporting and internal assessments, ensuring transparency in risk management processes

Challenges in using AI

The digital transformation has come to stay but there is a need to continue prioritizing AI, blockchain, and cloud adoption to stay competitive in customer experience and operational efficiency. Now let’s review some challenges being faced with the use of AI., 2024

Regulatory & Compliance framework

Cybersecurity Standards: As banking evolves with AI infiltrating all aspects of banking, cyber fraud continues to rear its ugly head, hitting hard at vulnerable financial institutions. For bank risk managers, new year reminders focus on proactively addressing an evolving risk landscape, improving operational resilience, and enhancing regulatory compliance and governance. Key areas include cybersecurity, the responsible integration of AI, managing interest rate and credit risk, and ensuring operational preparedness.

Before I continue, I will share an alarming report from Pulse Ghana about latest cyber frauds in Ghana. The latest major cyber fraud case in Ghana’s financial sector involved a coordinated crackdown on mobile money and bank-related scams, with 141 suspects arrested in December 2025. Authorities uncovered networks tied to business email compromise (BEC), bank transfer fraud, and mobile money fraud, many linked to some foreign nationals operating in Ghana

Comparison of Recent Cyber Fraud Cases

Case	Scale of Fraud	Arrests	Methods Used	Impact on Banks
Tabora & Lashibi Raids (Dec 2025)	Unknown (ongoing investigations)	141 suspects	Mobile money fraud, BEC, bank transfer fraud	Direct bank transfer fraud & MoMo scams targeting customers
Ghana-Nigeria Syndicate (Dec 2025)	$400,000 stolen	10 suspects in Ghana	Fake websites, digital extortion, ransomware	Undermined trust in online payments
BoG Fraud Report (2024)	GH¢56 million lost (2022–23)	Industry-wide	MoMo fraud, insider bank fraud	Significant financial sector losses

Sources: Pulse Ghana

The rest of this article merges some of the identified challenges with recommended solutions referencing and quoting data from Pro-IQ Consult Ltd.

Data Quality And Availability

? Challenge: AI systems rely on large volumes of high-quality data to function

effectively. Poor data quality or lack of sufficient data can lead to inaccurate

models, flawed predictions, and ineffective risk mitigation strategies.

? Solution: Ensuring the availability of clean, representative, and unbiased data is crucial, and techniques like data augmentation or synthetic data generation can help address gaps.

Cost and Resource Requirements

? Challenge: Developing, training, and maintaining AI systems can be resourceintensive, requiring specialized talent, infrastructure, and ongoing

investments.

? Solution: Leveraging cloud-based solutions, optimizing AI models, and

ensuring an ROI analysis are ways to balance cost and risk management

benefits.

Regulatory and Legal Concerns

? Challenge: The regulatory environment surrounding AI, particularly in risk

mitigation, is still evolving. Companies may face challenges complying with

data privacy laws.

? Solution: Staying informed on the latest legal and regulatory updates,

incorporating privacy-preserving techniques (e.g., differential privacy), and

working closely with legal experts are key steps.

• Bias and Fairness

? Challenge: AI models can inherit biases present in training data, leading to discriminatory outcomes, especially in risk-related areas such as credit scoring or fraud detection.

? Solution: Conducting thorough audits of the data, using bias detection

algorithms, and regularly monitoring AI outcomes for fairness and equality are essential.

Ethical and Moral Dilemmas

? Challenge: Using AI for risk mitigation in areas like healthcare, criminal justice,

or insurance may lead to ethical dilemmas, such as who bears responsibility

when AI fails or when its decisions negatively affect certain groups.

? Solution: Embedding ethical principles in AI development, ensuring human oversight, and applying ethical AI frameworks help address these concerns.

• Resistance to Change

? Challenge: Organizations and individuals may resist adopting AI-based risk

mitigation strategies due to fear of job displacement, lack of understanding, or mistrust in technology.

? Solution: Investing in change management, training, and demonstrating the value of AI to stakeholders can ease the transition.

• Interpretability and Transparency

? Challenge: Many AI models, particularly deep learning models, are often

considered “black boxes,” making it difficult to understand how they arrive at

specific decisions or predictions. This lack of transparency can hinder the

trust needed for risk mitigation.

? Solution: Techniques such as Explainable AI (XAI) and interpretable models (e.g., decision trees or rule-based models) help provide insights into AI decision-making processes.

• Overfitting and Generalization

? Challenge: AI models may perform well on training data but fail to generalize to new or unseen data, which is a major risk when applying AI in dynamic environments.

? Solution: Regularly retraining models with fresh data, validating performance on diverse datasets, and using techniques to prevent overfitting (e.g., crossvalidation) are critical.

My Final Remarks

Dear Bankers, as we have started the new year, lets not be overtaken by events which could have been prevented. Has your bank updated its cybersecurity frameworks to meet evolving regulatory standards? As regulators are tightening expectations, financial institutions need to update frameworks for resilience against ransomware and fraud. What is happening with your customer-focused innovation and culture?

• Personalized Banking: How are you leveraging AI-driven insights to deliver hyper-personalized financial products?
• Fintech Partnerships: Are you collaborating rather than competing with your partners? Partnerships can accelerate innovation in payments, lending, and wealth management.
• Financial Inclusion: As banks expand services to under-served communities, the focus should not be strictly for profitability, but rather on the social impact.

Customer-centric Culture: AI tools do not have emotions. The tools are to help and assist humans and not replace them, so a human-centered journey map benefits and enables customers have a seamless onmibanking experience.

Happy banking.

ABOUT THE AUTHOR

Alberta Quarcoopome is a Fellow of the Institute of Bankers, and CEO of ALKAN Business Consult Ltd. She is the Author of Three books: “The 21^st Century Bank Teller: A Strategic Partner” and “My Front Desk Experience: A Young Banker’s Story” and “The Modern Branch Manager’s Companion”. She uses her experience and practical case studies, training young bankers in operational risk management, sales, customer service, banking operations and fraud.

CONTACT

Website www.alkanbiz.com

Email:alberta@alkanbiz.com  or [email protected]

Tel: 233-0244333051/ 233-0244611343

 

 

 

 

 

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