FairMoney Onboards Better Digital Bank Customers Thanks to Digital & Social Footprint Checks

The Challenge

Despite its rapid growth, FairMoney faced significant challenges :

• Serving underbanked populations: With such a large underbanked population, they lack a traditional credit history, making them a risky sign-up due to potential loan default.
• Manual review times: Without a credit history, onboarding customers requires a lot of manual review time for Fairmoneys analysts, taking time away from other critical business tasks and making expansion harder.
• Customer friction: Customers signing up face time delays due to the manual review processes, and when customers need a loan fast, they may look for another company.

Juris Rieksts-Riekstins, Head of Risk at FairMoney, highlights the difficulty of obtaining reliable data in Nigeria’s financial landscape.

The solution

To tackle these challenges, FairMoney turned to SEON, an anti-fraud system of record that utilizes alternative data and offers insights into loan decisions. This approach aligns with FairMoney’s need to gather reliable data discreetly and efficiently without compromising the speed of its decision-making process.

FairMoney focused on two key SEON modules as part of a multi-layered bad debtor detection strategy:

• Digital footprinting: To provide insights into a user’s default/risk ratio via 90+ digital and social signals gained from email addresses and phone numbers.
• Device Intelligence: To identify and track device data points, offering an additional layer of insights into the onboarding customer, such as IP address location, to identify if numerous sign-ups are coming from one IP location, suggesting a fraud ring.

By combining these modules, FairMoney can quickly filter out obvious loan defaulters who lack a digital or social presence and automate informed decision-making processes.

We overlay SEON’s digital and social lookup with device fingerprinting to get a good indication of whether the phone or tablet belongs to the actual client. We can instantly filter out obvious fraudsters/bad debtors with no digital or social presence, and we get better intelligence to automate our decision making.

Juris Rieksts-Riekstins

Results

Within a month of deploying SEON, FairMoney streamlined its onboarding process, enabling the company to deliver microloans of up to $2000 in less than five minutes. This efficiency has resulted in a seamless user experience, where a loan is approved on average every 8 seconds.

The deployment of SEON has provided several critical benefits:

• Reduced Fraudulent Activities: By leveraging SEON’s advanced data analytics and blacklisting capabilities to automatically block a bad debtor, FairMoney has been able to significantly reduce fraudulent loan applications.
• Improved User Experience: The frictionless onboarding process ensures legitimate users can access loans quickly, enhancing customer satisfaction and retention.
• Operational Efficiency: Automating the decision-making process has allowed FairMoney to handle a high volume of loan applications daily without compromising accuracy or security.

Juris Rieksts-Riekstins commends SEON’s adaptability and support, noting:

SEON’s blacklisting system is very flexible and well-suited for our needs in Nigeria. We’re also happy to get one of the best customer support. The team is always interested to learn how they can make our lives easier.

Juris Rieksts-Riekstins

FairMoney’s partnership with SEON exemplifies how leveraging innovative fraud prevention solutions can address the unique challenges of emerging markets. By integrating digital and social data analysis with device fingerprinting, FairMoney has successfully enhanced its risk management framework, enabling secure and efficient financial services delivery. This case study underscores the importance of adopting adaptive technologies to maintain growth and integrity in the rapidly evolving digital lending landscape.