2018 Product Roundup: Upgrade Defense with Upgraded Features

When it comes to proactively fighting fraud, Simility provides businesses with advanced technology-driven, cutting-edge solutions

Simility product features

Petabytes of data are being generated every single day. Data-hungry work environments, rapidly digitalizing businesses, always-on smart-devices, Internet of Things, and social media are some of the areas that are contributing to this data explosion.

The availability of oceans of data is both good news and bad news for businesses. Good news first. Large amounts of data mean businesses have a treasure chest of information that they can harness to fuel business growth, sharpen their business strategies, and enhance customer experience through personalized services. Data is, therefore, increasingly being referred to as the competitive advantage in today’s data-driven and cognitive world.

Now the bad news. Because data is largely unstructured and siloed, businesses are finding it difficult to harness it efficiently. Second, and a much bigger challenge is that of cyber threats. The numerous avenues of data creation means multiple avenues for cyberattacks, as it provides fraudsters with a much larger attack surface.

Scary Fraud Landscape

The problem is further compounded because the data residing with businesses comprises personally identifiable customer information as well. Fraudsters are always looking around, sniffing for opportunities to steal the data, which can then be used for a variety of fraudulent activity, including card not present (CNP) fraud, fraudulent purchases, account takeover, fraudulent loans, and establishing fake bank/utilities/phone accounts. The 2018 Identity Fraud Study reports an increase in theft of social security numbers (35%) that for the first time surpassed compromised credit card numbers (30%). The same study also reports that identity fraud caused $16.8 billion worth of financial losses.1

Agility: The Key Differentiator

Fraudsters are an agile breed and they are continuously upgrading their techniques. Businesses too need agile technology to equip themselves for a winning combat against fraud. To this end, businesses need three-way agility—in terms of data, analytics, and decisioning. Data agility prepares businesses to tackle newer forms of fraud that are evolving rapidly. With analytics agility, businesses can use deep learning techniques to create smart rules and machine learning models that generate human (and regulator) readable decisions. And finally, with decisioning agility, businesses can establish self-improving ‘auto-decision, refer, and alert mechanisms’ that propagate decisions and automate workflows.

Rules and machine learning models are a crucial component in defining fraud-fighting strategies of a business. That said, many businesses are still apprehensive about machine learning models, probably due to its blackbox approach. Businesses that want greater control over the way rules are written, therefore, prefer hand-written tools. However, this means with the introduction of every new data set, the rules will need to be written all over again. This can lead to humongous amounts of legacy rules that are mostly useless and difficult to manage.

Therefore, the bottomline is that businesses can no longer rely on ‘make-do’ solutions in this digital era where attackers are agile and equipped with sophisticated technology.

Leverage Technology

Simility, a PayPal service, understands the predicament of today’s digital businesses and, therefore, endeavors to help them build operational efficiency and fight fraud, proactively. Simility has built an end-to-end fraud prevention solution—the Adaptive Decisioning Platform—to empower businesses to achieve greater adaptability and stay ahead of the fraud-fighting curve. This omni-channel platform allows businesses to arrest fraud early in its track.

The platform ingests varied data types—structured and unstructured—and uses powerful rule builder and big data analytics with augmented machine learning capabilities to precisely analyze and unearth subtle, seemingly-unrelated patterns that are beyond human comprehension. Strong visualization on top of the fraud-centric data lake helps identify, conceptualize, validate, and operationalize team members’ fraud intuitions quicker than ever.

Enhanced Features, Enhanced Capabilities

In keeping with its commitment of providing digital businesses with cutting-edge fraud-fighting solutions, Simility introduced key features to its platform in 2018. These include:

  • Rule Improvement Suggestion: This feature provides an innovative approach to address the problem of maintaining and tuning an overhead of an existing suite of rules, that usually spring up due to excessive rule creation in a bid to mitigate bad actors with rising avenues of fraud.
  • Rule Historic Performance: The ‘Rule Historic Performance’ feature allows businesses to monitor the performance of the rule, the total number of fraud cases, and the total number of fraud cases flagged by the rule over the configured period.
  • Rule Testing: With the ‘Rule Testing’ feature, businesses can instantly test a rule not just on a small sample but on large portions of historical data. When the ‘Test with Historic Data’ checkbox is selected, changes to a rule can be evaluated on several months of past transactions within seconds, often eliminating the need for a time-consuming champion/challenger rule testing workflow that can span over days or even weeks.
  • Rule Grouping: The ‘Rule Group’ feature allows grouping rules of the same entity. With this feature, businesses can surgically control what groups of rules run on what sets of transactions, thus eliminating the need to modify every rule to add a new criteria. In a rule group, there is no limit on the number of rules, and a rule can also be part of multiple rule groups.
  • Maker Checker Functionality: With this feature, an analyst can review and approve a rule that was created by another analyst, before it is executed on live data.
  • Support for More Programming Languages: Although, there is a large repository of machine learning models in the Adaptive Decisioning Platform, it also supports numerous other programming languages such as Python, R, and H20. This enables businesses to use their own supervised or unsupervised models as well.

Final Thoughts

Looking ahead to 2019, businesses will witness multiple challenges spanning rising customer expectations in terms of enhanced convenience, increasing numbers of connected devices, mounting pressure of staying compliant with regulations, and most importantly increasing cyber attacks. Simility, a PayPal service, will continue to iterate and add new features to empower digital businesses to fight evolving fraud efficiently.

To learn more about the Adaptive Decisioning Platform and how it can be a potent weapon in your armory to fight fraud, schedule a demo now.


1. 2018 Identity Fraud Study: https://www.javelinstrategy.com/press-release/identity-fraud-hits-all-time-high-167-million-us-victims-2017-according-new-javelin. 
Kedar Samant

Kedar Samant

Kedar Samant is a seasoned technologist and a fraud and risk management expert. Kedar co-founded Simility and crafted the vision for Simility’s disruptive approach to fraud management. He has been key in establishing Similty’s product superiority and driving innovations. Kedar brings a wealth of experience from his prior stint at Google where he built the platform for fighting fraud and abuse across Google’s products. Prior to Google, he held technology leadership positions across industries including banking and online services.
Kedar Samant