Companies’ most pressing challenges in the rapidly evolving technology landscape are ensuring robust fintech identity verification and facilitating fintech fraud detection. As fintech continues to disrupt traditional financial services, the need for a comprehensive solution that combines various data sources has become increasingly evident.
- Do you have issues with fraud detection in fintech?
- Do you want to know for sure that the person behind the computer is exactly who he claims to be?
- Do you wonder why you should comply with fintech KYC regulations and what is digital identity verification?
- Do you realize that your anti-fraud solution interferes with “honest” users?
- Do you feel your anti-fraud solution may be improved significantly?
If you answered “yes” at least on two of these five questions then this article is for you.
This article explores the complexities surrounding fintech ID verification and fraud detection, highlighting the significance of integrating personal data, banking/payments data, biometrics, and technical data.
Furthermore, it delves into the contrasting approaches of linear methods versus leveraging artificial intelligence’s (AI) power. Finally, it emphasizes the importance of a triangulation solution and integrating high-quality data into front and backend operations to enable better decision-making and gain a competitive advantage.
The average U.S. FinTech loses $51 million to fraud every year, and many lose even more.
The real challenge of fintech identity verification
Fintech companies operate in an environment where user trust and security are paramount. The challenge lies in balancing frictionless user experience and robust security measures. Due to increasingly sophisticated fraud techniques, traditional methods of identity verification for fintech, such as document checks or knowledge-based authentication, are becoming less reliable.
Failure to implement robust identity verification measures for fintech can lead to severe consequences, including financial losses, damaged reputation, and regulatory non-compliance. So what is basically Fintech ID verification? It is a combination of measures, which Fintech companies need to take to adopt innovative approaches to ensure the integrity of their user base while mitigating the risk of identity fraud to build customer trust.
Payment card fraud losses worldwide exceeded $32 billion in 2021, of which nearly $12 billion was in the US, according to the Nilson Report, the leading trade publication covering the global payment card industry. Losses to fraud worldwide increased by 14% in 2021.
Existing services but no integral solution
While various identity verification services exist in the market (e.g., Onfido, FaceTec, Iovation, and others), many lack an integral solution. Traditional methods often rely on static data, such as identification documents, which can be easily forged or manipulated. These methods fail to account for the dynamic nature of identity and the sophisticated techniques fraudsters employ.
To effectively combat identity fraud, fintech companies require a holistic solution that combines multiple data sources, integrated with different data sources within and outside the Company, offering a comprehensive view of an individual’s identity.
The global average rate of losses caused by fraud for the last two decades represents 6.05% of the gross domestic product. Additionally, companies have reported that cyber breaches have caused financial damages equaling 3% to 10% of their revenue. Moreover, global digital fraud losses are projected to exceed $343 billion between 2023 and 2027.
Combination of personal data, banking/payments data, biometrics, and technical data
A robust identity verification and fintech fraud detection system are the fusion of personal data, banking/payments data, biometrics, and technical data. Personal data includes information users provide during onboarding, such as name, address, and date of birth.
Banking and payment data encompasses transaction history, account details, and behavioral patterns. Biometric data, such as fingerprints or facial recognition, provides an additional layer of security. Lastly, technical data involves analyzing device information, IP addresses, and geolocation.
Combining, analyzing, and processing these data types due to predefined rules and conditions creates an opportunity for a more accurate and comprehensive assessment of an individual’s identity.
The fact that losses to fraud are increasing in the world tell us that existing anti-fraud solutions are not so effective as they should be.
"Linear" approach versus AI
Traditional “linear” approaches to identity verification rely on predefined rules and thresholds, limiting their ability to adapt to evolving fraud techniques. On the other hand, artificial intelligence (AI) empowers fintech companies to leverage machine learning algorithms, pattern recognition, and anomaly detection to detect and prevent fraud in real time.
AI-driven solutions can analyze vast amounts of data, identify subtle patterns, analyze vast amounts of data in real-time, uncover hidden correlations, and detect fraudulent activities more accurately. Thus, as a result, the Company makes accurate decisions, significantly improving the effectiveness of identity verification and fraud detection processes.
AI-powered fraud management systems can identify and prevent various types of fraud, such as payment fraud, identity theft or phishing attacks. They can also adapt and learn from new fraud patterns and trends, improving their detection over time.
A triangulation solution that combines multiple data sources and verification methods enhances the effectiveness of identity verification and fraud detection in fintech. By cross-referencing data from various angles, fintech companies can corroborate information and ensure its authenticity.
For example, if personal data matches banking transactions, biometric verification, and technical data from the user’s device, it strengthens confidence in the individual’s identity. Triangulation minimizes false positives and provides a more robust foundation for decision-making.
Using triangulation and similar techniques, you can empower AI with confirmation of identity and payment identity.
Integration(s) with frontend and backend to obtain high-quality data
To achieve an integral solution, fintech companies must tightly integrate identity verification processes into both frontend and backend systems. This integration facilitates seamless data exchange, ensuring that high-quality data is collected and processed efficiently.
By implementing robust APIs and incorporating identity verification into user onboarding and transaction processes, fintech companies can establish a smooth and secure user experience.
Online “technological” data (such as device fingerprint etc.) may make your anti-fraud solution much better.
The more data we have, the better decisions we can make
In identity verification and fraud detection, the adage “the more data, the better” is a general approach. A rich dataset, formed by specific rules, comprising personal, financial, biometric, and technical information, provides a comprehensive understanding of an individual’s identity.
Following this data-driven approach, analyzing diverse data points helps identify anomalies, patterns, or discrepancies that might indicate fraudulent behavior and rely more on independent data-driven decisions. Therefore, fintech companies should strive to collect and leverage as much relevant data as possible to make informed decisions and mitigate the risk of identity fraud.
The effectiveness of your anti-fraud solution strictly depends on the variety of data you can provide.
Gain a competitive advantage
Fintech companies gain a competitive advantage by implementing an integral identity verification and fraud detection solution. By ensuring the security and trustworthiness of their user base, companies can attract and retain customers in an increasingly crowded market.
A robust and reliable identity verification system instills confidence in users, enhancing their overall experience and fostering long-term relationships.
The challenges posed by identity verification and fraud detection in the fintech industry are immense but not insurmountable. Fintech companies must embrace a comprehensive solution that combines personal data, banking/payments data, biometrics, and technical data.
By leveraging AI and machine learning, implementing a triangulation approach, and integrating high-quality data into frontend and backend operations, these companies can enhance their ability to make accurate decisions and effectively detect identity fraud.
Adopting such a solution not only safeguards the interests of companies but also enables them to gain a competitive advantage in the dynamic and fast-paced world of fintech.
ID verification in FinTech is the process of confirming the identity of users or businesses in digital financial transactions, ensuring compliance with regulations, and preventing fraud.
FinTech KYC is done by collecting user information, verifying documents, and often using biometrics and database checks to confirm identity. Continuous monitoring and risk assessment are part of the process.
FinTech combats fraud with advanced analytics, behavioral analysis, 2FA, biometrics, machine learning, and customer education, creating a secure environment for financial transactions.