Blogs / The Role of Artificial Intelligence in Reducing Financial Risks and Its Challenges

The Role of Artificial Intelligence in Reducing Financial Risks and Its Challenges

نقش هوش مصنوعی در کاهش ریسک‌های مالی و چالش‌های پیش روی آن

Introduction

Today's financial industry faces the most complex challenges in its history. Transaction volumes have reached billions of deals per day, fraudsters use advanced technologies to bypass security systems, and financial crises can spread globally within hours. In this turbulent environment, artificial intelligence has emerged as a strategic tool for risk management.
Recent statistics show that 90% of financial institutions worldwide use AI to combat fraud, and this number continues to grow. In fiscal year 2024 alone, the U.S. government was able to identify and prevent over $4 billion in fraud using machine learning technology. However, these successes represent only one side of the coin; the other side consists of challenges and risks that improper use of this technology can bring.

Intelligent Fraud Detection: From Simple Rules to Deep Learning

Traditional fraud detection methods, such as rule-based systems, no longer meet the volume and complexity of modern fraud. These systems often came with high rates of false positives, wasting security teams' time and frustrating customers.
AI has completely changed this equation. Machine learning algorithms can analyze millions of transactions in real-time and identify complex patterns that are invisible to the human eye.

Success Story: PayPal and Millisecond Fraud Detection

PayPal is one of the pioneers in using AI for fraud detection. The company's machine learning system analyzes millions of transactions daily and can instantly identify suspicious patterns. This system has not only increased fraud detection accuracy by 10%, but also reduced required server capacity by 8 times.
More interestingly, this system is self-learning; meaning that with every new fraud it detects, it learns new behavioral patterns and becomes stronger when facing future threats. This adaptability is the main difference between AI and old systems.

Detecting Coordinated Attacks

One of the major challenges for financial institutions is coordinated attacks by professional criminal gangs. 71% of financial organizations have identified this type of attack as their primary threat. These gangs can send hundreds of requests using stolen or fake identities within minutes.
AI tools like Alloy's Fraud Attack Radar, instead of reviewing individual requests, analyze an institution's entire request queue and identify suspicious patterns. For example, if multiple requests are sent from a shared IP address or with similar email formats, the system automatically alerts and blocks these attacks.

The New Threat: Deepfakes and AI-Powered Fraud

While AI is a powerful tool for fighting fraud, unfortunately fraudsters are also using this same technology against us. More than 50% of today's financial fraud involves the use of AI in some form.

The Hong Kong $25 Million Incident

In January 2024, an employee in Hong Kong transferred $25 million to fraudsters' accounts. He thought he was speaking in a video call with the CFO and other colleagues. But they were all deepfakes - fake images and voices created using generative AI.
This incident is just one example. According to reports:
  • 44% of financial professionals have reported that deepfakes are being used in fraud schemes
  • 60% consider voice simulation as a major concern
  • 59% have observed AI-powered text messages and phishing
Financial losses from fraud in 2024 reached $12.5 billion, a 25% increase from the previous year.

Predictive Analytics: From Reaction to Prediction

One of the most important changes AI has brought to risk management is transforming it from a reactive to a predictive approach. In the past, financial institutions would wait for a problem to occur and then react. But today, AI can identify a problem before it happens.

Predictive Modeling with Machine Learning

Deep learning algorithms can identify complex patterns that indicate future risks by analyzing historical data. For example:
BNY Mellon increased its fraud detection accuracy by 20% using NVIDIA DGX systems. This system can predict which transactions are more likely to be fraudulent by analyzing millions of data points.
JPMorgan Chase uses advanced AI for fraud detection that has not only increased security but also improved operational efficiency.

Market Sentiment Analysis with Natural Language Processing

Natural Language Processing (NLP) enables financial institutions to analyze market sentiment in real-time. These systems can:
  • Analyze financial news instantly
  • Review company earnings reports
  • Monitor analyst discussions and financial forums
  • Identify early changes in market conditions
This information helps risk managers adjust their risk parameters timely and take preventive actions before a crisis occurs.

Credit Risk Management: More Accuracy, Less Discrimination

Credit risk assessment is one of the most sensitive areas of the financial industry. Traditional methods primarily relied on credit scores and financial history, which was sometimes unfair and deprived certain groups of access to financial services.

More Comprehensive Analysis with More Data

Machine learning algorithms can analyze a wider range of data:
  • Daily transaction patterns
  • Payment behavior over time
  • Alternative data sources (such as bill payments, rent, etc.)
  • Informal economic activities
Intesa Sanpaolo, one of Italy's largest banks, uses machine learning to calculate the regulatory capital required for credit risk. This approach is not only more accurate but also fairer because it can evaluate people who are overlooked in traditional systems.

Regulatory Compliance: From Cost to Competitive Advantage

Regulatory compliance has always been one of the financial industry's major challenges. The enormous volume of regulations, frequent changes in laws, and the need for constant monitoring have made this process complex and costly.
AI can transform this area from a cost center to a competitive advantage. According to financial managers, "effective AI risk management has become a competitive advantage for companies. Organizations that manage this area properly can also grow more rapidly in other dimensions."

Reducing False Alerts

MoneyGram, using AI, has been able to significantly reduce false alerts. The company's CEO explains: "AI helps us focus on cases that truly need review. This has made our team much more efficient and effective."
In 2024, 83% of fraud prevention professionals planned to integrate generative AI into their systems.

Fundamental Challenges: What Must Not Be Overlooked

Despite all the benefits, using AI in financial risk management comes with serious challenges that ignoring them can have dangerous consequences.

1. The Explainability Problem

One of the biggest challenges is the explainability of AI decisions. Many advanced deep learning models, especially complex neural networks, operate as "black boxes." These models can deliver accurate results, but the why behind that decision cannot be easily explained.
This creates several problems:
  • Regulatory oversight: Regulatory bodies need to know why a specific decision was made
  • Customer trust: Customers have the right to know why their loan application was rejected
  • Accountability: In case of error, the cause must be identifiable
Explainable AI (XAI) solutions are being developed, but there's still a long way to go. Techniques like SHAP and LIME try to make complex model decisions understandable, but these methods are not yet mature enough.

2. Data Quality Dependency

"Garbage In, Garbage Out" - this golden rule of machine learning has double importance in financial risk management.
Common data problems include:
  • Incomplete data: If the history of financial crises is incomplete, the model cannot predict them
  • Inconsistent data: Different standards in different organizations
  • Outdated data: Financial markets change and old data may no longer be valid
  • Data bias: If training data includes historical biases, the model replicates them
One study showed that financial crises often stem from unexpected and unpredictable factors that don't exist in historical data. The 2008 financial crisis is a clear example - most statistical models couldn't predict it.

3. Model Risk and Governance

Model risk is one of the main concerns of regulatory bodies. If an AI model is not properly designed, tested, or monitored, it can lead to incorrect decisions and heavy financial losses.
Governance frameworks should include:
  • Continuous model validation: Models must be tested regularly
  • Human oversight: Critical decisions must be reviewed by humans
  • Recovery plan: What to do if the model fails?
  • Complete documentation: All assumptions, limitations, and design decisions must be recorded
The Basel Committee on Banking Supervision (BCBS) and other regulatory bodies have published comprehensive guidelines for model risk management that must be followed.

4. The Challenge of Predicting Crises

The 2008-2009 global financial crisis caused over $20 trillion in damage to the global economy. The question is: Can AI predict the next crisis?
Unfortunately, the answer is complex:
  • Crises often stem from "Black Swan" events - rare and unpredictable occurrences
  • AI models are trained on past data and cannot predict completely new events
  • Human behavior during crises is irrational and difficult to model
  • Self-fulfilling effect: If a model predicts a crisis and everyone acts on it, it might create the crisis itself

5. Cybersecurity Risk

Using AI increases the attack surface of financial systems. Fraudsters can:
  • Deceive AI models (Adversarial Attacks)
  • Exploit algorithm vulnerabilities
  • Poison training data (Data Poisoning)
  • Steal or copy proprietary models
The U.S. Treasury in March 2024 published a report on "Managing AI-Specific Cybersecurity Risks in the Financial Services Sector," emphasizing the importance of this issue.

Key Criteria for Successful AI Use

To ensure effective and safe use of AI in financial risk management, institutions must ask these key questions:

1. Data Access

Does the AI have access to sufficient, accurate, and up-to-date data?
Without quality data, even the most advanced models fail. Institutions must:
  • Have robust big data infrastructure
  • Create unified standards for data collection and storage
  • Continuously monitor data quality

2. Legal Flexibility

Can the system adapt to regulatory changes?
Financial regulations change rapidly. AI models must be designed to keep pace with these changes without requiring complete rewrites.

3. Goal Transparency

Are clear and measurable goals defined?
Models must be optimized for specific goals. Is the goal to reduce loss? Increase accuracy? Reduce false alerts? Must be clear from the start.

4. Human Oversight

Are critical decisions reviewed by humans?
AI should augment human decision-makers, not replace them. Major decisions still require human judgment.

5. Accountability

In case of error, who is responsible?
There must be a clear framework for accountability. Is it the data team? Risk team? Senior management?

6. Impact Assessment

How serious are the potential consequences of errors?
Decisions that can lead to major losses require more monitoring and validation.

The Future: Combining Human and Artificial Intelligence

The future of financial risk management lies in collaboration between humans and machines, not replacement. AI can:
  • Analyze enormous volumes of data that humans cannot
  • Identify complex patterns in milliseconds
  • Provide 24/7 monitoring without fatigue
But humans still excel at:
  • Understanding context and specific situations
  • Judgment in unusual situations
  • Ethical decision-making
  • Explaining decisions to customers and regulators

Case Studies: Real-World Success

U.S. Government: Returning Billions of Dollars

The U.S. Treasury, using machine learning, prevented over $375 million in potentially illegal payments in fiscal year 2024 and identified $4 billion in potential fraud. This success shows what returns technology investment can have.

Mastercard: Frictionless Security

Mastercard Decision Intelligence, the company's deep learning system, evaluates millions of transactions per second. The result? A reduction of 300 million incorrectly declined transactions each year. This means better experience for customers and more revenue for banks.

HSBC: Fighting Money Laundering

HSBC uses AI to analyze transactions and identify money laundering patterns. This system has not only increased detection accuracy but also significantly reduced the review time for each case.

AI's Role in Emerging Markets

Using AI in developing countries creates unique opportunities:
Financial Access: Supervised learning models can evaluate people with no credit history based on alternative data (such as mobile payments or social media activities).
Cost Reduction: Automated systems can deliver financial services at lower cost to remote areas.
Fighting Corruption: AI can identify suspicious patterns of financial corruption in government organizations.

Emerging Technologies and the Future

Federated Learning

Federated learning allows financial institutions to build shared models without sharing their sensitive data with each other. This technology can:
  • Preserve privacy
  • Build stronger models through collaboration
  • Be compatible with data protection regulations

Quantum Computing and AI

Quantum computing could revolutionize the future of risk management:
  • Solve complex optimization problems in seconds instead of hours
  • Simulate crisis scenarios with high accuracy
  • More advanced cryptography for data security
However, this technology is still in its early stages and will take years to reach full maturity.

Graph Neural Networks (GNN)

Graph Neural Networks can model complex relationships between financial entities. This technology is very useful for:
  • Identifying systemic risk
  • Analyzing money laundering networks
  • Understanding the complexities of interconnected financial markets

Transformer Models

Transformer models, which are the foundation of large language models like ChatGPT and Claude, are currently being used for:
  • Analyzing complex financial reports
  • Extracting information from unstructured documents
  • Predicting market movements based on news

Practical Recommendations for Financial Institutions

1. Start Small, Think Big

You don't need to launch a complex system from day one:
  • Start with a pilot project
  • Measure results
  • Learn and improve
  • Then scale

2. Invest in Data

Before investing in complex algorithms, make sure:
  • Data infrastructure is solid
  • Data quality is high
  • Data governance exists
  • Your team knows how to work with data

3. Hire Expert Team

You need a diverse team for success:
  • Data scientists and machine learning specialists
  • Financial domain experts who understand the problems
  • AI ethics and legal specialists
  • Project managers experienced in technology projects

4. Collaborate with Fintechs

Many fintech startups offer innovative solutions. Instead of building everything from scratch, you can:
  • Partner with them
  • Purchase their technology
  • Learn from them

5. Create Innovation Culture

Success in the AI era requires organizational culture change:
  • See failure as part of learning
  • Encourage experimentation
  • Welcome questioning
  • Promote continuous learning

Ethical Considerations and Social Responsibility

Using AI in the financial industry is not just a technical issue but has deep ethical and social implications.

Fairness and Discrimination

AI models can inadvertently reinforce historical discrimination. Institutions must:
  • Test models for fairness
  • Use diverse data
  • Regularly review impact on different groups
  • Have transparent complaint processes

Privacy

Collection and analysis of personal data must be done with respect for privacy:
  • Obtain informed consent from users
  • Collect minimum necessary data
  • Keep data secure
  • Be transparent about how data is used

Employment Impact

Automation can change or eliminate some jobs. Responsible institutions should:
  • Invest in employee retraining
  • Create new roles
  • Manage the transition
  • Support employees

Conclusion: Balance Between Innovation and Caution

AI has the potential to fundamentally transform financial risk management. From detecting fraud in milliseconds to predicting potential crises, this technology has provided powerful tools to the financial industry.
But as we've seen, these tools are not without challenges. Explainability, data quality, model risk, cybersecurity, and ethical considerations are all issues that must be carefully managed.
Success in the AI era requires balance:
  • Between innovation and caution
  • Between automation and human oversight
  • Between efficiency and fairness
  • Between profit and social responsibility
Institutions that find this balance not only better manage financial risks but also gain sustainable competitive advantage and contribute to a more stable and fair financial system.
The future of financial risk management lies in intelligent collaboration between humans and machines. AI is a tool, not a replacement. With its proper use, we can build a more secure, efficient, and equitable financial system.