Case Study: FinTech Company banQi Achieved Automatic Fraud Detection with Custom AI

Who is banQi:

banQi, a thriving financial platform with a vision in achieving financial inclusion in Brazil, provides personal banking solutions to the low-income audience who usually don’t have access to the traditional financial services. 

What We Heard:

banQi was confronted with a growing problem of individuals exploiting their referral bonus program. A manual review process was in place to identify and block fraudulent activity, but this was resource-intensive and often too late, with funds already stolen. The company recognized the need for a proactive solution to detect and prevent bonus fraud before it resulted in monetary losses.

banQi attempted to engage vendors for fraud prevention services but encountered a significant roadblock. Existing vendors were unable or unwilling to tailor their models to address this unique type of fraud, as banQi's audience profile and this specific problem differed from their usual offerings. The existing vendor solution also had a high false negative rate, rendering it ineffective in identifying the behavior-based fraud that banQi faced. So banQi realized the necessity of developing a proprietary solution that integrated seamlessly with their business logic.

What We Did and What the Outcomes Were:

In response to banQi's pressing challenges, our team undertook a comprehensive transformation of their fraud detection and prevention system. We began by strengthening banQi's data infrastructure, enabling advanced data processing and custom capabilities. This facilitated the creation of custom automation, AI algorithms, and business logic tailored to the company's specific needs. With the enhanced data infrastructure, we were able to promptly leverage the new capabilities for fraud detection and prevention.

Within just three weeks, we implemented automation to detect and flag bonus fraud in real-time, preventing the theft of funds before it occurred. Our solution enabled banQi to flag fraudulent activity during the transaction processing window, considerably reducing the time between detection and action. We also created a real-time fraud dashboard for the fraud team and customer service team, allowing them to monitor ongoing situations and take immediate action. This proactive approach significantly curtailed fraudulent activity.

Within a quarter, our real-time AI solution, the "Fraud Hub," was evaluating transactions in real-time, making swift decisions to allow or reject them. In the following months, banQi was able to expand the scope of fraud detection by adding new rules and developing custom business logic. Our solution not only saved banQi from financial losses due to fraud but also eliminated the need to purchase ineffective vendor data. We fought automatic attacks with automatic detection.

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