Case Study

Series A Fintech Scales with Advanced Fraud Controls in Credit Card Operations

A Series A Fintech, evolving in the competitive credit card market, sought to scale its business while managing the inherent risks of fraud, such as ACH Returns, Fraud Rings, First-Party Fraud and others. Their product was aimed at providing frictionless customer experience through an intuitive digital interface. This case study outlines how the FinTech navigated the challenges of scaling, while reducing fraud and maintaining compliance with regulatory requirements.

Key Challenges

Account Takeover (ATO) Fraud

The Fintech was increasingly vulnerable to ATO fraud, with fraudsters using sophisticated methods like social engineering, phishing, and credential stuffing to access user accounts. Compounding this challenge was the difficulty in detecting new, synthetic identities created by blending real and false personal information.

Income Verification Fraud

There was a specific challenge in accurately verifying the income claims of customers, crucial for credit assessments and other financial services. Inaccurate income verification posed risks of credit loss and regulatory non-compliance.

First Party Fraud

There was a rising trend of customers intentionally misrepresenting information or engaging in deceitful activities to gain financial benefits, posing a significant risk to the company’s assets and reputation.

ACH Payments Fraud

Unauthorized ACH transactions were a growing concern, with fraudsters exploiting customer banking information for illicit transfers. The fraudsters continued to transfer funds illicitly even after putting name checks in place.

Fraud Losses

A critical issue was the inability to precisely measure current fraud losses, making it challenging to determine if these losses were within the Fintech’s risk appetite. The Fintech also needed effective strategies for accurate measurement of fraud-related losses.

How Did EntityVector Help?

Fraud Detection

Our approach involved mapping out existing risk and control measures, pinpointing gaps and weaknesses in these controls, and implementing new measures to fill these gaps. Additionally, we fine-tuned and updated existing rules to enhance the accuracy of risk prediction. Further, we deeply assessed the gaps in customer onboarding to identify any identity risks upfront. We also recommended incorporation of an anomaly detection model, as the business scaled. This approach not only improved overall customer onboarding but also enabled the early identification of identity-related risks.

 

Income checks & monitoring

We established a multifaceted approach that incorporated rigorous verification processes and robust internal controls. By deploying risk-based income verification methods, we ensured the authenticity of customer-reported income, which was pivotal in mitigating risks associated with inaccurate or fraudulent income declarations. Concurrently, to tackle the rising threat of ACH Payments Fraud, we adopted predictive risk-scoring models specifically designed for ACH transactions. This helped improve first-party fraud and income verification inaccuracies, as well as intercepting unauthorized ACH transactions.

 

Governance & Loss Metrics

We assisted the Fintech in developing a robust framework to enhance its fraud program governance, integrating real-time monitoring systems and regular risk assessment for continuous oversight and adherence to fraud prevention protocols. Additionally, we helped implement precise measurement protocols for fraud losses, essential for quantifying these losses. This approach was crucial in aligning the Fintech’s fraud loss metrics with its risk appetite, significantly improving its capacity to manage fraud risks effectively and sustainably.