As the digital landscape of financial services expands exponentially, granting more individuals access to the global financial system and cashless economy, the threat of financial crime looms larger than ever before.
According to LexisNexis, global fraud’s annual impact surpasses $1 trillion, with each dollar lost to fraud costing U.S. financial services firms $4.23. In 2022, there was a staggering 79% rise in document fraud for financial service businesses compared to the previous year. Considering the economic climate of 2023, it is safe to anticipate an exacerbation of financial fraud.
In such a challenging environment, both emerging fintech startups and established players must not only defend against the most sophisticated fraud techniques of today but also anticipate new methods and identify malicious actors. They must accomplish this while adhering to anti-money laundering (AML) and know your customer (KYC) regulations and ensuring an exceptional customer experience.
Enter artificial intelligence (AI) and deep learning models, which possess the capability to process vast and complex datasets, enabling the detection of patterns and anomalies that humans alone would struggle to uncover. This not only saves time and resources dedicated to investigating fraud but also enhances the customer experience by reducing the number of legitimate transactions mistakenly flagged as fraudulent. By investing in deep learning models and collaborating with reputable platforms like NVIDIA and AWS, financial institutions can achieve unprecedented levels of accuracy in identifying and preventing fraudulent activities at scale.
The cost of fraud is growing faster
Financial services companies bear a fiduciary and regulatory duty to safeguard their customers, but combatting fraud and complying with financial crime regulations comes at a considerable cost. In 2021, FIs allocated an estimated $213.9 billion to financial crime compliance—an amount twice the investment made just two years earlier, and the figure continues to rise.
These escalating costs are partly a consequence of the industry’s rapid digitization and the advent of a cashless society. Digital payments are projected to reach $9.5 trillion this year, with a subsequent 15% year-over-year growth over the next five years. Additionally, the sector is becoming increasingly complex due to emerging business models such as “buy now, pay later” (BNPL), which introduce new risk categories and provide more opportunities for fraudsters. In the current economic climate, characterized by a cost of living crisis, the sector is likely to witness an accelerated risk landscape.
As a result, the fintech industry faces pressure to combat fraud proactively and address the risks that accompany intensified digitization. A recent survey conducted by ComplyAdvantage reveals that approximately 40% of FIs prioritize enhancing their fraud detection methods within their compliance programs, indicating a lack of confidence in their existing technology’s efficacy in effectively detecting and preventing fraud. Fintechs and FIs must seek new solutions to manage compliance and fraud prevention efforts while keeping pace with the rapid advancements in financial crime.
Combating fraud through deep learning
Deep learning empowers FIs to implement a wide range of anti-fraud measures swiftly, driving significant growth in their fraud protection capabilities.
Conventional rule-based systems often struggle to identify novel fraud patterns since they typically rely on historical transactions for comparison. In contrast, deep learning does not rely on predefined rules; it continuously learns to improve its ability to detect anomalies. This allows it to recognize patterns even when fraud exhibits slight variations from previously observed instances. Given the ever-evolving landscape of financial fraud, adopting a proactive approach to identifying new forms of fraud and preventing their proliferation is crucial.
AI enhances the effectiveness of identifying fraudulent practices and malicious actors by expanding the types of data utilized in fraud prediction models. These include computer vision for ID document analysis, voice recognition for login verification, and natural language processing to monitor evolving