Zest AI Unveils Innovative Fraud Detection Solution
Zest AI has launched an advanced tool designed to identify and combat fraudulent activity during the loan decisioning process. This new offering, dubbed Zest Protect, leverages cutting-edge artificial intelligence (AI) to address the 69% increase in fraud cases reported by community banks and credit unions in 2023, according to the Federal Trade Commission.
Adam Kleinman, head of strategy and client success at Zest AI, emphasized the need for smarter fraud prevention solutions in the financial sector. “Lenders need to outsmart fraud, including an increasing volume of AI-driven fraud in the industry with AI,” Kleinman stated. “Our team designed Zest Protect to create an efficient tool that can more accurately detect all types of fraud now and in the future, including AI-created fraud, with the ultimate goal of boosting lending confidence for our bank and credit union customers.”
Zest Protect utilizes machine learning technology to instantly detect both first-party and third-party fraud. It also flags income inconsistencies within the automated loan decisioning process, allowing lenders to adjust detection thresholds based on their unique risk tolerances and automation goals. With access to robust fraud prevention data and analytics, Zest AI can swiftly flag suspicious applications and safeguard against emerging threats.
AI is rapidly becoming the preferred tool for financial institutions aiming to prevent illicit activities such as money laundering and bank fraud. According to the PYMNTS Intelligence report “Financial Institutions Revamping Technologies to Fight Financial Crimes,” there has been a noticeable uptick in financial crime, with over 40% of surveyed financial institutions reporting an increase in fraud incidents. Moreover, 7 in 10 institutions are now employing AI and machine learning technologies to fend off fraudsters.
“Modern payments fraud demands real-time learning and adaptation at scale,” the PYMNTS report highlighted. “Generative AI offers the unprecedented advantage of continuous learning. It rapidly refines and adapts its understanding of patterns to distinguish between legitimate and fraudulent payments more accurately.”
Additionally, generative AI can generate synthetic datasets that mimic real-world financial data. This capability allows for robust model training without compromising privacy or regulatory compliance. Despite the benefits, developing AI and machine learning tools can be costly, which is why only 14% of financial institutions build in-house fraud-fighting technologies. Instead, almost 30% rely entirely on third-party vendors to provide these critical tools.
