Adaptive Decisioning Platform: A Potent Weapon to Fight Fraud
Big data analytics and advanced machine learning capabilities can help businesses remain agile in the face of evolving cyber threats.
In the data-driven, cognitive world of today, petabytes of data—big data—are generated in a single day. From rapid digitization of businesses to always on smart devices and social media, there are numerous avenues that are adding to the explosive volumes of a variety of data at a breakneck speed. IBM’s Jeremy Waite observes that the rate at which big data is growing is equivalent to a company of the size of Google —being created everyday.1 That’s a whole lot of data we are talking about.
Data Drives the World
What does all this data mean to businesses? When analyzed intelligently, big data can be used to fuel business growth. Big data can help the retail sector offer personalized shopping experiences to tech-savvy shoppers. Law enforcement agencies can harness big data to pre-empt criminal activities by gleaning information from seemingly unrelated data sets. The healthcare sector can efficiently use patients’ data to provide diagnoses and appropriate treatment fairly quickly. Financial institutions and merchants offering multiple payment options can correlate patterns of the available data to spot anomalous behaviors and prevent fraud. The possibilities are endless.
That said, analyzing big data is not as simple or straightforward as it may seem. This is because big data is siloed, spread across many departments and functions, and unstructured. Therefore, in order to cleanse, enrich, and harness data to gain actionable insights, businesses need solutions that are robust and technology-driven. However, finding the right mix of tools and technologies that can unlock value from the data in hand is a huge challenge that most businesses face today.
Another challenge businesses face is that of evolving cyber threats. Because data-driven, digital businesses possess enormous amounts of data (including personally identifiable customer information), they are much more vulnerable to cyber threats. Data breaches can expose personal customer credentials for subsequent fraud and money laundering. To add to the angst, most businesses today have workloads across multiple platforms and environments. This makes data security a daunting task as the same security, policy and incident management, and business continuity must be deployed across all touchpoints. This means, businesses must take a 360-degree view of the data—past, present, and predictive—in order to secure data and weed out fraud before it strikes roots. This can be done only with sophisticated technology-driven tools.
Businesses that can harness data to gain insights and remain agile in response to fast evolving cyber threats will succeed in this data-driven world. Big data analytics and advanced machine learning can help businesses gain real-time actionable insights through the correlation of patterns and deep-dive investigations to proactively fight fraud.
New Features, Enhanced Capabilities
Simility, a PayPal service, through Adaptive Decisioning Platform enables businesses to adapt, strengthen, and quickly detect and react to new fraud schemes. And, with the addition of the latest features, this omnichannel fraud prevention platform has become even more potent in helping businesses remain agile in the face of evolving cybercrime.
The Adaptive Decisioning Platform is built using data lake technology, which makes it possible to incorporate the latest data sets as and when they emerge. Using big data analytics with augmented machine learning capabilities, the platform can ingest varied data types—structured and unstructured—to precisely analyze and unearth subtle, seemingly unrelated patterns that are beyond human perception. Strong visualization on top of the fraud-centric data lake helps identify, conceptualize, validate, and operationalize team members’ fraud intuitions quicker than ever.
Simility’s fraud prevention platform features algorithms that evolve with the emergence of new data, helping to future-proof the solution as the environment changes. Businesses can test historical data by defining and fine-tuning rules. They can now use the powerful rule builder to create smarter rules that evolve with every decision and rule; and, with champion challenger, businesses can test the efficacy of rules before execution. In addition, with the maker-checker feature, a rule can be reviewed and approved by an analyst other than the one who created it before it is executed on live data.
The Adaptive Decisioning Platform comes with a large repository of machine learning models. But, since it now supports numerous other programming languages such as Python, R, and H20, businesses can even use their own supervised or unsupervised models.
View the webinar highlighting the latest features and enhancements of the Adaptive Decisioning Platform or schedule a demo to learn how you can use the platform that is designed to help you gain a competitive edge in today’s digital economic landscape.
1. 10 Key Marketing Trends for 2017 and Ideas for Exceeding Customer Expectations, IBM, https://public.dhe.ibm.com/common/ssi/ecm/wr/en/wrl12345usen/watson-customer-engagement-watson-marketing-wr-other-papers-and-reports-wrl12345usen-20170719.pdf.
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