The rise of the digital-first economy has led to a significant increase in data, which continues to grow exponentially, fueled by faster computing power, the growing adoption and development of IoTs, and changes in how consumers communicate. According to IDC, global digital data is estimated to grow at a compound annual growth rate of 30%, reaching 163 zettabytes of data by 2025.
With approximately 90% of the world’s data created over the past two years, there have been considerable advancements in data collection capabilities. However, the ability to effectively manage that data is a different story, as businesses continue to struggle with how to organize, use, and control their growing data.
Data is often collected from heterogeneous siloed sources and comes in a variety of forms. The explosion of emails, texts, images, videos, and more have contributed to a wealth of unstructured data, which is estimated to account for a whopping 80% of global data, according to Gartner. With structured data therefore only comprising the remaining 20% of global data, businesses are investigating ways to analyze and unlock insights from not only structured, but also unstructured data for a variety of purposes including targeted marketing, personalized shopping experiences, tailored products or services, and fraud prevention.
As data breaches continue to proliferate and fraud persists, businesses are ever more concerned about their risk tolerance and susceptibility to evolving attacks, which are growing more sophisticated and complex. According to Juniper Research, online transaction fraud will double to reach USD 25 billion by 2020, with ecommerce businesses being highly susceptible to fraud, followed by the banking and airline industries.
Fraud is a growing concern not only because it causes financial loss, but also because it can lead to customer friction, lower revenue, negative brand perception, customer attrition, and lower customer lifetime value.
To effectively tackle fraud, businesses need to understand the complete picture behind a login, account application, or payment. And in order to obtain this comprehensive view, they need to make better use of data.
Unfortunately, legacy fraud solutions rely on data warehouse technology, which only stores and ingests structured data. This makes decisioning challenging because outcomes are based on a very small and limited subset of a business’ data. As a result, businesses are unable to extract accurate, let alone meaningful insights necessary to obtain a complete view of the connecting user. Furthermore, the rigidity of legacy systems prevents businesses from being able to quickly pivot in response to evolving fraud, as they require analysts to write complex statements and queries in order to modify and encode them.
To identify potential fraud with greater accuracy, it is important for businesses to correlate and collate insights from various heterogeneous data sources, such as clickstream, weblog, CRM, POS, financial, social media, device data, and more. In addition, data from external sources such as bureau and credit agencies and phone and address verification services can help businesses validate a user’s information to prove their identity.
Only a data-lake-based fraud detection solution, such as Simility’s Adaptive Decisioning Platform, that can ingest structured and unstructured data sources, combined with big data analytics and advanced machine learning capabilities, can help businesses effectively manage and make sense of data to accurately detect fraud.
Simility’s Adaptive Decisioning Platform is fundamentally different from legacy fraud prevention solutions. By pulling different data streams spanning transactional, channel, payment, historical data, homegrown as well as third-party data among others, into Simility’s purpose-built data lake, businesses can gather real-time, dynamic information across silos to obtain comprehensive insight and actionable intelligence, which can lead to lower false positives, reduced fraud losses, less friction, and better conversion rates, while keeping businesses ahead of rapidly evolving fraud tactics.
For more information on how to use data as a strategic advantage, download the white paper here.
About Vanita Pandey
Vanita is the Senior Director of product strategy and marketing for Simility. In this role, she is responsible for establishing Similitys brand, driving Similtys go-to-market strategy as well as product and market positioning. Prior to Simility, Vanita worked at ThreatMetrix where she was responsible for the strategic vision and go-to-market for ThreatMetrix products and solutions. With extensive experience in strategy, innovation, product management and analytics within the payment industry, Vanita previously led merchant development and global go-to-market for Visa’s digital products. Prior to Visa, Vanita held a range of diverse positions at some of the world’s leading financial institutions including Capital One, Standard Chartered Bank and ABN AMRO Bank.
About Simility
Simility, a PayPal Service, offers real-time risk and fraud decisioning solutions to protect global businesses. Simility’s offerings are underpinned by the Adaptive Decisioning Platform built with a data-first approach to deliver continuous risk assurance. By combining artificial intelligence and big-data analytics, Simility helps businesses orchestrate complex decisions to reduce friction, improve trust, and solve complex fraud problems.
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