Making Data a Competitive Advantage in the Fight Against Evolving Cyber Threats
Businesses must efficiently harness data to achieve actionable insights and make necessary changes to the rules, quickly
The rise of the digital economy has led to an explosion in the amount of data being created and handled every day. This Big Data is a treasure trove for businesses and provides them with valuable insights to customize service offerings according to changing consumer preferences. But, this same data can be a minefield for businesses, as cyberspace is rife with fraudsters who can access, share, and exploit all of this information, using sophisticated tools, to launch specialized and targeted attacks.
To be successful, businesses must emerge as winners in this data combat. To maintain data integrity and gain a competitive advantage, businesses must harness data efficiently. This will also help them achieve actionable insights from historical data (records where decisions have already been taken) to remove anomalies and make the necessary changes, quickly.
Need for Greater Control
Machine Learning (ML) is effective in identifying specific trends and insights from large amounts of data. However, apprehension often surrounds ML due to its black box approach, as businesses that want greater control prefer defining and hand-writing rules to detect specific behaviors. Given the large number of customer touch points, delivery mechanisms, and products and services on offer, businesses have their hands full with too many and, possibly, outdated rules to manage. Fraudsters, on the other hand, are continually upgrading and sharing knowledge to crack these hard-coded rules.
Data: The New Competitive Advantage
Many businesses are using ML models and behavioral analytics across structured and unstructured data to detect fraud and suspicious activity. Still, businesses need solutions that help them achieve a fine balance between taking a pure ML approach, and maintaining control over how existing rules are managed or modified. Simility understands the difficulty businesses face, and has developed solutions that make it easy to ingest siloed data from across departments and business units to generate meaningful fraud and risk insights. Simility’s solutions enable businesses to harness transactional data – both streaming and historical – to build smarter rules and speed up investigations. Simility’s on-demand webinar Fishing the Data Lake? Cast a Smarter Net to Catch Fraud shows how businesses can make their analytics stack cleaner, flexible, and accessible.
Rule Historic Performance
Simility’s software helps businesses analyze historical performance of the rules already available and provides results in terms of Precision and Recall. With the ‘Rule Historic Performance’ feature of the software, businesses can 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. Apart from the standard display, these results are also represented using a graph. Along with the results, changes required for efficient rule performance are also suggested that can be applied to replace the conditions with new values. This makes the rules smarter and more incisive.
Modified Rule Testing
The ‘Rule Testing’ feature in Simility’s software allows businesses to 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. Simility provides all of the necessary behavioral metrics of the modified rule instantly, thus enabling businesses to take immediate corrective action.
It is now possible to group rules of a same entity with the ‘Rule Group’ feature. With this feature, businesses can surgically control what groups of rules run on what sets of transactions. For example, if businesses require some set of rules to be applicable only for transactions from one country while another set for another country, this can easily be achieved using Rule Groups rather than duplicating rules for each country. It is also helpful in conditions that are common across multiple rules. In such a case, it is possible to create a rule group, defining the common criteria as a rule group condition, and adding rules to the group. This eliminates the need to modify every rule to add this 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. This makes defining conditions and managing multiple rule groups easier.
Rule Improvement Suggestions
As businesses grow and avenues of fraud increase, analysts end up creating more and more rules to mitigate bad actors. While ease of rule creation and testing is essential, businesses can end up with an overhead of maintaining and tuning the existing suite. Simility’s new ‘Rule Improvement Suggestions’ feature provides an innovative approach to address this. By using advanced machine learning techniques, Simility can suggest new thresholds to existing rules that can make tuning and maintaining hundreds of rules as easy as clicking a button. Consequently, Simility not only ensures the best thresholds get picked, but also fits the overall strategy of a business’ model – for example, be more precise to improve automated decisions or catch more fraud in the manual review queues.
Empowered with the way rules are applied, modified, and managed on their transactional data, businesses can gain better control over their data and fortify defense against cybercriminals.
Check out the on-demand webinar, “First Look at Simility’s Adaptive Decisioning Platform Enhancements” to learn how businesses can harness data for actionable business intelligence and gain a competitive advantage in fighting evolving cyber threats.
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