How to Derive Value from Data to Boost Fraud Detection

Today’s organizations deal with copious amounts of data from many sources. However, only a small portion of the data collected is leveraged to drive real business value. Companies often lack the skills and tools necessary to leverage digital data to build powerful fraud and risk rules and models for risk management and fraud detection.

DataVisor aims to change this with its Feature Platform, which is designed to use cleaner and enriched data to drive better decision-making. Out-of-the-box features offer a turnkey solution that has demonstrated up to 50% detection lift over existing solutions, as well as the ability to build custom features based on unique business needs. 

Here’s how DataVisor is helping to solve data challenges and deliver real value to its users:

Challenges in Deriving Value from Data

Data is a key driver in today’s business environment. It’s used to analyze everything from sales forecasting to profitability to marketing and even fraud detection. However, one of the biggest data challenges is the ability to use it.

Too Little Data

On one extreme, having too little data means you don’t have enough information to make informed decisions. This information may be readily available, such as transaction or customer data. But without also comparing that data to other activities, such as historical data, companies may be unable to properly detect fraud attempts. Data that is stored across multiple systems can be hard to compare to get a complete picture.

For example, in terms of fraud, companies cannot base all fraud-related cases solely on transaction data. This only gives you a partial view, as transactions that look legitimate on the surface may actually be a part of the detailed work of an organized crime ring. 

DataVisor’s Global Intelligence Network and Deep Learning feature help to fill in the gaps by analyzing all data points holistically and in real time, offering deep analysis on any entity. It compares client-generated data with DataVisor’s consortium database to detect unique signals such as entity ages, blacklists, and geo-profiling for a more comprehensive snapshot.

Too Much Data

On the other extreme, having too much data can lead to confusion, time-consuming processes, and missed opportunities. There could be hundreds or even thousands of data fields per customer. Some data collected, such as those from payment gateways or consortium or bureau data, can be difficult to harness and use. 

DataVisor solves this challenge with its real-time big data architecture that has the ability to handle massive amounts of data in real time with only a 10-15 second latency. Automation features sort through millions of data points simultaneously to deliver faster insights than manual methods. Organizations can confidently process large-scale data with the hyper-modern platform without concerns for speed or performance.

Production Data Pipeline is Complex

Companies need a process in place to produce their data. This may include collecting the data, verifying its authenticity, analyzing the data, and figuring out a way to make it accessible to users, among other steps.

In the above example, the size of each box is proportional to the coding required to support each feature. Machine learning requires very little in comparison, especially when considering that many of the other features are still largely manual and require domain expertise. What’s more, DataVisor reduces time spent building features by reusing architecture instead of building every feature from scratch. All features are production-ready and can be deployed in minutes. 

Deriving Instant and Ongoing Value with Out-of-the-Box Features

Because data collection can be such a time-consuming process to set up, not to mention the methods in which companies analyze and use that data, out-of-the-box solutions can provide a faster route to value. Turnkey solutions like DataVisor’s consist of useful features for fraud detection and prevention.