Explore the benefits and risks of AI in fintech

AI use cases in fintech

AI offers numerous benefits for fintech organizations, including fraud detection and scam prevention, automation, compliance, virtual assistants, personalized service, predictive analytics and enhanced security.

Fraud detection and scam prevention

AI can combat fraud and scams in fintech via the following measures:

• Pattern recognition. AI tools can analyze vast amounts of financial data, such as online transactions, to identify anomalies that suggest fraudulent activity. For instance, AI tools can monitor signals such as deviations in spending behavior, logins from unusual locations and money transfers to unexpected destinations to trigger automatic alerts.
• Real-time alerts. AI tools can monitor financial transactions around the clock, flagging suspicious activity in real time for immediate review or halting it before damage is done. For example, an AI system might flag a rapid series of high-value transactions occurring outside of business hours for immediate inspection.
• Enhanced user authentication. AI tools aid in implementing stronger authentication mechanisms. Biometric authentication systems like facial recognition, fingerprint scanning and voice authentication provide higher standards for verifying users and securing their accounts.
• Fraudulent document detection. AI-powered image recognition can better detect manipulated or forged documents, helping to prevent identity theft and loan fraud. For example, AI can flag inconsistencies in documents that might be invisible to the human eye.
• User behavior monitoring. AI tools can detect account takeovers by picking up anomalies in certain criteria related to each user, such as typing speed, IP address, device type and behavior when accessing online financial portals. This enables fintech organizations to catch unauthorized access quickly.

Automation of repetitive tasks

AI helps fintech organizations automate repetitive tasks, improving efficiency and reducing human error. The following are a few examples:

• Data entry. AI tools can extract information from scanned documents, images and PDFs, then input that information into other digital systems. This speeds up workflows and reduces the risk of human error in data handling.
• Report generation. AI tools can combine information from different systems -- such as financial statements, compliance reports and risk assessments -- to create comprehensive reports.
• Customer onboarding. AI tools can streamline customer onboarding by recognizing and verifying identification documents, cross-checking information against databases, and flagging potential compliance issues.
• Loan processing. AI tools can automate loan application processing by verifying applicant information, assessing risk and suggesting additional procedures as needed.
• Customer service. AI tools can categorize incoming emails based on their content, automatically respond to common inquiries and route complex issues to the appropriate human agents.

Improved compliance

In compliance processes, AI can help conduct regulatory checks and prepare risk assessment reports. For instance, AI tools can monitor financial transactions and related activities in real time to ensure adherence to regulations and flag potential issues as they arise. These tools can also automatically generate compliance reports for human review, reducing manual effort and ensuring timely delivery to regulatory bodies.

Customer service chatbots and virtual assistants

AI-powered chatbots and virtual assistants can help fintech companies improve customer service and reduce support costs. For instance, AI chatbots provide the following benefits:

• 24/7 availability. Unlike human agents, who have limited working hours, AI chatbots are always available to assist customers.
• Instant responses. Chatbots can provide immediate answers to users' queries about a range of tasks -- for example, opening a new account, checking balances and getting information about financial products.
• Ability to handle high inquiry volume. Chatbots can respond to many inquiries from different users at the same time. This feature is particularly important during peak times when numerous users are requesting support.
• IT support. Chatbots can handle routine tasks such as password resets and unlocking accounts, letting IT staff focus on more complex issues.
• Contextual understanding. NLP capabilities let chatbots understand the context and sentiment behind users' queries, supporting more personalized and relevant responses.

Personalized financial advice

AI can provide more personalized customer service through machine learning, NLP and data analytics. Examples include the following:

• Customer profiling. AI can analyze large volumes of customer data, including customer interactions with the fintech company and even their public activities on social media platforms. This enables the organization to profile each customer and suggest tailored services based on their behavior and preferences.
• Individualized recommendations. AI models can forecast customers' future spending and financial status through predictive analytics. For instance, AI can help customers make proactive financial decisions by recommending investment opportunities or anticipating recurring expenses.
• Holistic financial management. AI can integrate a customer's financial information and activities with other services requiring payment, such as healthcare, travel, e-commerce and real estate. This provides a holistic approach for customers to manage their entire financial life in a single dashboard.

AI-driven predictive analytics

AI can enhance decision-making processes in fintech in the following ways:

• Risk assessment. AI tools can predict a client's credit risk before granting loans. Predictive analytics also helps fintech companies forecast market trends before offering services or entering new markets.
• Customer behavior prediction. Profiling customers based on their behaviors and interactions with a fintech company enables the business to target customers with more tailored services, improving retention and increasing satisfaction.
• Identifying investment opportunities. By analyzing large volumes of market and customer data using AI and machine learning, a fintech company can determine its future growth strategy, identify expansion opportunities and optimize the timing of marketing campaigns.

Preventing phishing attacks and combating malware

AI can assist fintech companies in several cybersecurity endeavors. For example, NLP algorithms can scan incoming emails, text messages and social media posts to identify phishing attempts and other social engineering attacks.

Many of today's antimalware and antivirus tools employ advanced machine learning algorithms to analyze software behavior in real time. This improves their ability to effectively stop emerging threats, especially zero-day exploits and polymorphic malware, compared with traditional security options that depend on detecting malware signatures. AI can also be connected to various threat intelligence feeds, providing up-to-date protection at lower costs.

AI-powered antivirus options also continually learn from their work. For instance, they can update their detection mechanisms based on emerging malware trends, which boosts their ability to detect and stop emerging malware, such as variants specifically targeting financial institutions.

Risks and limitations of AI in fintech

Despite these benefits, integrating AI into fintech also comes with risks. Key considerations include the following:

• Data privacy. Fintech companies typically handle sensitive customer data, such as personally identifiable information and financial information such as credit card and banking details. A technical issue in an AI-powered application could expose such information to unauthorized users, leading to a data breach.
• AI hallucinations. AI tools can produce false or inaccurate information known as hallucinations, which can be especially dangerous in sensitive, highly regulated industries like fintech. If an AI-powered virtual assistant generates false information in response to a customer's question about an investment opportunity, for example, the customer could make a financial decision based on that fabricated information. Such an outcome could severely affect the fintech company's reputation and even result in a lawsuit.
• AI bias. AI systems depend on their training data to make decisions and generate responses, and bias in that data -- whether due to skewed training data, developer prejudices or even manipulation from threat actors -- can lead to discrimination. AI bias is especially harmful when the AI tool is used for fintech tasks like loan evaluation.
• Security vulnerabilities. AI tools suffer from the same vulnerabilities as traditional software applications. For example, for a typical fintech AI tool using third-party components from various vendors, a security gap in one component could expose the entire system to cyberattacks.
• Regulatory compliance. Fintech companies operate in highly regulated environments, typically involving an array of government regulations and industry standards. Failing to adhere to such standards when deploying AI-powered tools can result in huge fines and reputational damage.
• Explainability. Some AI models, especially deep learning models, operate as so-called black boxes, making it difficult to explain their decisions. This lack of transparency is not recommended in financial services, where regulatory bodies often require clear explanations for decisions that affect customers.

Nihad A. Hassan is an independent cybersecurity consultant, expert in digital forensics and cyber open source intelligence, blogger, and book author. Hassan has been actively researching various areas of information security for more than 15 years and has developed numerous cybersecurity education courses and technical guides.