How AI is Helping Financial Services Companies Fight Fraud

Financial services organisations face a never-ending battle against financial crime; the convergence of sensitive financial data, the potential for direct financial gain, and the possibility of disrupting critical systems make financial institutions extremely lucrative targets and ideal for large-scale breaches. Whether through account takeover attempts or sophisticated identity theft, financial crime can cause significant financial losses and damage consumer trust. In fact, as of 2023, the average cost of a data breach in the financial industry worldwide was a staggering 5.9 million U.S. dollars. To combat this, financial institutions invest heavily in compliance. However, traditional fraud detection methods, reliant on predefined rules and manual reviews, often struggle to keep pace with the continually evolving tactics and growing level of sophistication employed by threat actors.

Artificial intelligence (AI) offers a powerful set of tools that can analyse vast amounts of data, identify complex patterns and learn from historical fraud trends. As such, the technology is becoming vital in the fight against fraud, helping financial services organisations to improve their cyber resilience and protect their customers.

The Challenges of Fighting Financial Crime

Until recently, financial services organisations have felt unable to compete with the level of criminal activity they face. Threat actors use a huge variety of sophisticated tactics, exploiting the distributed nature of financial systems to execute their attacks. Cybercriminals might engage in small transactions across multiple financial institutions or different accounts within the same institution to mask their activities, for example. The issue is that organisations aren’t able to see a complete picture of what’s going on across these distributed systems. Furthermore, the global focus on data privacy in an increasingly interconnected world leaves them powerless to solve the problem. The answers they need are within transaction records, account information, customer relationship databases and more, but they remain off-limits when they contain personally identifiable information. Beyond legal mandates, financial services firms also understand only too well the critical role data privacy plays in customer trust. Data leaks or mishandling can lead to significant financial repercussions, as customers are more likely to defect to competitors who prioritise data security.

How Confidential Computing Offers a New Layer of Protection

Technological advancements offer innovative approaches to safe data sharing in the financial services industry. Confidential computing is one such innovation, complying with the highest privacy requirements while enabling numerous parties to obtain insightful financial data. Confidential computing encrypts data even as it is being processed, in contrast to conventional techniques that only protect data at rest or in transit. This extra layer of encryption guarantees that not even cloud operators, malicious administrators, or privileged software can access the data. Ultimately, confidential computing creates a new level of security in the financial services sector by eliminating the chance of unauthorised access during processing.

Beyond confidential computing, advancements in data de-identification techniques are gaining significant traction within regulatory circles. Techniques like data masking, data perturbation, and differential privacy offer innovative tools to anonymise or remove personally identifiable information (PII) from datasets. When used with confidential computing, these de-identification methods empower AI to extract valuable insights from data while safeguarding privacy.

This convergence represents a significant breakthrough for the financial services industry. By leveraging de-identified data alongside AI, financial institutions can unlock invaluable insights crucial for combating financial crime. This opens doors for secure collaboration across vast enterprise datasets, encompassing data from various locations and potentially even different institutions. This collaborative approach signifies a paradigm shift in how financial services firms handle data, enabling secure collaboration across vast enterprise data sets and creating a more holistic and secure way of using information.

How AI is Helping in the Fight Against Financial Crime

AI has the potential to completely transform how financial services companies handle risk and combat the threat of financial crime. AI-powered solutions give organisations a significant edge in risk assessment and scoring, allowing them to deploy resources and prioritise investigations more efficiently. Furthermore, sophisticated pattern recognition algorithms can identify irregularities and questionable activity in a wide range of datasets, including customer and financial transaction data. This results in notable advancements in fraud control, an essential task for reducing financial risks. Moreover, because AI can spot trends and evaluate risk more precisely, institutions may be able to show that they have done more due diligence, which will save underwriting expenses.

Additionally, generative AI can be used to identify signs of possibly fraudulent activity by analysing a variety of unstructured data from different internal sources. Natural language processing (NLP) can be used to automate the creation of legal texts, compliance reports and regulatory papers, vastly improving efficiency. Ultimately, the benefits of AI stretch far beyond fighting financial crime. By incorporating AI into an organisation’s systems and productivity tools, employees across the business can leverage AI’s potential to become more efficient, more productive and, indeed, more secure.

The Future of AI in Financial Fraud Detection

The fight against financial fraud is an ongoing battle. However, AI is constantly evolving to enable financial institutions to stay ahead and mitigate the risk of a data breach.

As AI continues to develop, we can expect even more sophisticated models capable of identifying intricate fraud patterns and adapting to new threats in real time. Future iterations may be able to detect fraudulent behaviour based on historical data and market trends as well as looking for suspicious transactions. With a more proactive approach, financial services companies will be empowered to intervene before crimes even occur. In addition, integration with other technologies like biometrics and network analysis will further enhance AI’s capabilities. Biometric authentication, for example, can add an extra layer of security to transactions, while network analysis can help identify suspicious connections and activity patterns. This convergence of technologies will create a more comprehensive and robust defence system against financial crime.

Looking ahead, here are some additional ways AI might support the financial services industry:

  • Automated Know Your Customer (KYC) Processes – streamlining customer onboarding by automating identity verification and risk assessments, improving efficiency and reducing human error.
  • Personalised Fraud Prevention – analysing customer behaviour patterns to create personalised risk profiles and implement targeted fraud prevention measures.
  • Enhanced Anti-Money Laundering (AML) Compliance – assisting in monitoring transactions for suspicious activity and automating the generation of AML reports, ensuring regulatory compliance.
  • Cybersecurity Threat Detection – analysing network traffic and system logs to identify potential cyberattacks and vulnerabilities in real time, mitigating cybersecurity risks.
  • Chatbot-Powered Customer Support – AI-powered chatbots can handle initial inquiries about suspected fraud, freeing up human investigators for more complex cases.

This collaborative approach, where AI handles heavy data analysis and human expertise tackles complex investigations, will be crucial for securing the financial landscape. By leveraging the strengths of both AI and human intelligence, financial institutions can create a robust defence system against financial crime, protecting their customers and the financial system as a whole.