Bolster Defenses with the New AutoML Feature

Businesses can quickly create and/or retrain models, make them smarter and more incisive, and adapt to evolving fraud patterns using Simility’s new Automated Machine Learning feature.

AutoML for fraud prevention

Fraud prevention is an ongoing, continuous process. In keeping with its commitment to help global businesses fight fraud, Simility, a PayPal service, provides the flexibility for businesses to adapt as fraud evolves, helping to ensure that those on the frontline always have the information they need to make the right decisions. Simility’s Adaptive Decisioning Platform leverages the most advanced technologies—artificial intelligence, and machine learning – to make fraud detection operations more efficient and enable accurate decisioning.

Machine learning is the go-to-technology when it comes to addressing many pressing challenges arising from the rapid digitization of global businesses. It also enables businesses to unearth patterns and gain incisive insights from seemingly disparate data sources. But, the traditional machine learning approach to solving business challenges is not only time-consuming, but also requires tremendous human resources.

Currently, machine learning experts perform numerous repetitive and time-consuming tasks including pre-processing and cleansing the data, constructing relevant features, and selecting appropriate machine learning models. As the volume and complexity of data increases, so will the time required for these processes. At the same time, fraudsters are getting creative, and cybercrime has evolved to organized business with advanced tooling and automation. As a result, there will be a need for advanced, robust solutions that can help analysts stay a step ahead.

Automated Machine Learning

All of this is set to change with progressive automated machine learning, called AutoML. AutoML uses machine learning best practices to make data science more accessible and reduce dependency on data scientists, engineers and researchers. Using AutoML, businesses can save time, effort, and costs associated with developing these capabilities in-house as well as reduce the time-to-action based on the insights gained from the data in hand. Further, by automating the process of choosing appropriate information from the available raw data—’the signal in the noise’—AutoML eliminates chances of human error and bias creeping into the machine learning models.

Bolster Frontline Defense with Simility

Simility, a PayPal service, provides its business partners with cutting-edge, smart solutions to help fight evolving fraud. Simility’s Adaptive Decisioning Platform is a powerful platform that enables businesses to make real-time, data-driven decisions and adapt to ever-evolving fraud types. With pre-built algorithms that have expert data science knowledge baked-in and an intuitive user interface, Simility provides businesses with ‘off-the-shelf’ machine learning models that developers can deploy quickly and easily. In addition to a large repository of machine learning algorithms, this omnichannel platform offers businesses the flexibility to use their own supervised and unsupervised models.

To further sharpen machine learning models and bolster the frontline defense with relevant real-time insights, Simility has added the AutoML feature to its Adaptive Decisioning Platform. This new feature helps empower fraud analysts to create new models or retrain existing ones in just a couple of hours. The process to create or retrain a model is simple with a 5-step procedure that allows analysts to build a new model or train an existing one, define model parameters, and select fraud labels, field types, data types, and data range. Since it’s only a matter of making a few selections, AutoML can drastically reduce the time an analyst would otherwise spend on selecting data, data processing, and feature engineering. This provides businesses with the ability to self-service their machine learning requirements, according to their business needs, freeing up human experts to focus on more strategic activities.

AutoML for fraud preventi

With the addition of the new AutoML feature in the Adaptive Decisioning Platform, Simility helps ensure that businesses use the latest models and make accurate decisions based on the latest data sets. This enables businesses to quickly adapt to evolving fraud patterns. Now, businesses no longer need to play the catch-up game, and instead can stay a step ahead of fraud, as they test numerous fraud models and deploy the most appropriate ones in real time. Also, since it is now possible to retrain and/or create newer models, dependence on data scientists is significantly reduced. This not only frees up key human resources to focus on core business activities but also empowers data teams to fuel sustained business growth with smarter, data-backed business decisions.

To learn how the latest AutoML feature in Simility’s Adaptive Decisioning Platform helps businesses sharpen their machine learning models for enhanced fraud fighting capabilities, schedule a demo now.

Ravi Sandepudi

Ravi Sandepudi

As Director of Software Development, Ravi heads data science and engineering teams at Simility. He is a fraud industry veteran with extensive experience in building large scale fraud detection and prevention platforms.
Ravi Sandepudi