In recent years, fraud has become an even bigger challenge to digital payment systems in Nigeria. Fraudsters have become more sophisticated and traditional methods of detecting and preventing fraud are proving inadequate.
As a result, there is an urgent need to find new ways to deal with fraud and behavioural analytics offers a promising solution, leveraging data-driven insights to improve the security of digital payments.
Behavioural analytics involves collecting and analysing data about user actions to understand human behaviour. Concerning digital payments, this means gathering data on how users interact with payment platforms. This data includes bio-data, device information, location data, and details about specific user actions. By analysing this data, platforms can gain insights into user preferences and habits, which can be used to improve user experience and enhance security measures.
Nigeria faces several types of fraud in digital payments, including phishing and social engineering, card-related frauds, account takeovers, Ponzi schemes, and fake receipts. Additionally, the “what I ordered vs. what I got” fraud is prevalent on social media and e-commerce platforms. As fraud methods evolve, it is crucial to use more adaptive and predictive approaches to fraud prevention.
One of the significant advantages of behavioural analytics over traditional methods is its adaptability. Behavioural analytics solutions can learn and relearn user behaviours in near real-time, making it easier to detect new patterns quickly.
Furthermore, behavioural analytics has predictive capabilities, allowing for early pattern matching and fraud prediction. This adaptability and predictive power enable more targeted and effective fraud prevention measures, improving user experience.
At Kuda, we are exploring this and have plans to integrate behavioural analytics into our existing fraud management technology stack. This integration involves merging fraud, transactional, and other behavioural data into a behavioural analytics platform powered by machine learning models.
These models will detect unusual patterns and, combined with user profiling and risk scoring, will help prevent fraud. Initially, we will run our existing system and the new behavioural analytics platform simultaneously to build confidence and improve accuracy, ultimately creating a best-in-class fraud detection and management system.
Integrating behavioural analytics into existing systems can be complex, especially when merging systems with different internal workings. We address this at Kuda by keeping our solutions simple yet effective. We use data templating, transformation, and standard data formats like JSON for compatibility.
Ensuring customer privacy and data security is paramount when using behavioural analytics. At Kuda, we adhere to local and international data privacy regulations, including the NDPR and GDPR. We are also ISO-27001 certified, ensuring robust information security management.
Additional measures include data anonymisation, detection and prevention of data leakages, data encryption, strict access control, and regular system audits to maintain data security.
Technological advancements, particularly in cloud computing, big data, and AI/ML, drive the effectiveness of behavioural analytics in fraud prevention. These technologies provide the processing power and scalability needed for advanced machine-learning models.
AI and machine learning enhance behavioural analytics by detecting complex patterns, establishing new baselines, and constantly improving fraud detection capabilities.
Collaboration also has a huge part to play. Collaboration among fintech companies and financial institutions is crucial for enhancing fraud prevention. Collaborative efforts improve the robustness and effectiveness of fraud prevention solutions by providing a broader view of fraud cases and larger datasets for behavioural analysis.
While there are no specific industry standards for behavioural analytics in fraud prevention, standard reporting practices and blacklisting initiatives are commonly used.
The regulatory landscape in Nigeria supports fraud prevention efforts. The Central Bank of Nigeria (CBN) and other regulatory bodies are pro-fraud prevention and advocate for consumer protection and a trustworthy financial system.
I believe that collaboration between fintech companies and regulators to facilitate industry-wide engagement and develop best practices, guidelines, and standards to support the adoption of behavioural analytics in fraud prevention will be beneficial.
The future of behavioural analytics in fraud prevention for digital payments in Nigeria looks promising. More financial institutions are expected to adopt behavioural analytics-based solutions for their adaptability and predictive capabilities.
Collaborative efforts among digital payment players will enhance fraud detection and prevention. At Kuda, we prepare for these advancements by building an integrated fraud management system, investing in skills development, and ensuring interoperability for future collaborations.
Behavioural analytics offers a robust solution to the evolving challenges of fraud in digital payments. By leveraging data-driven insights and advanced technologies, we can enhance fraud prevention measures, improve user experience, and build a more secure digital payment ecosystem in Nigeria.
Aina, is the VP, technology at Kuda
