In the ever-evolving landscape of cyber threats, a new player has entered the scene – Synthetic Fraud. This rapidly growing financial crime, fueled by the creation of what experts term “Frankenstein Identities,” is proving to be a formidable challenge for the financial industry, particularly impacting credit cards and unsecured lending portfolios. In this piece, we delve into the intricacies of synthetic fraud and how it operates and explore strategies for financial institutions to defend against this rising menace.
The craft of synthetic fraud
Unlike traditional identity fraud, Synthetic Fraud takes a different approach. Instead of stealing existing identities, cybercriminals craft entirely new personas using a mix of real or unused social security numbers, fictitious names, driver’s licenses, and physical addresses. This sophisticated method makes detection an uphill battle for financial institutions.
Patience pays off
What sets Synthetic Fraud apart is the patience of its perpetrators. These fraudsters play the long game, dedicating months to years to building credit histories with credit bureaus. Once their groundwork is laid, they apply for credit across various financial institutions, often starting with secured credit cards or products designed for high-risk borrowers. Over time, they accumulate multiple credit cards and small loans, maxing out credit limits before disappearing, leaving financial institutions to grapple with substantial losses.
Reinventing fraud protection strategies
Financial institutions must rethink their fraud protection strategies in the face of synthetic fraud, especially in the realms of digital onboarding and lending transactions. Striking a balance between robust protection and a seamless experience for legitimate customers is key. This involves verifying additional attributes beyond traditional credit scores through third-party data evaluation and ensuring high consistency within these supplementary data attributes, all matched to proven identities.
Harnessing the power of emerging technologies
As we confront Synthetic Fraud, emerging technologies step into the limelight. Combining Artificial Intelligence (AI) and Machine Learning (ML) with Big Data Analytics and High-Performance Computing promises to process vast volumes of data from diverse sources. These technologies, adept at handling structured, unstructured, and semi-structured data, can identify trends and patterns across disparate datasets.
Embracing AI and ML
Financial institutions can proactively identify Synthetic Fraud applicants by leveraging accelerated deep learning and statistical machine learning technologies. AI and ML enhance the efficiency of credit decision processes and streamline automated systems, expediting approval timelines. This shields financial institutions from Synthetic Fraud losses and benefits credit-worthy customers, offering them reduced paperwork and faster decision-making processes.
Safeguarding the future together
Artificial Intelligence and Machine Learning are guardians in the evolving financial landscape, playing a crucial role in protecting customers and financial institutions from the threat of Synthetic Fraud. By adopting these technologies, the industry can build robust defenses, creating a shield against the adaptive strategies of cybercriminals. It’s time for financial institutions to invest in proactive measures, ensuring their systems’ integrity and safeguarding their clientele’s trust.
Mr. Sudhir Sahu is the Founder and CEO of Datasafeguard.ai.
Image Source: Freepik
