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Lexicon Based Detection Kill Your Surveillance Efficiency

Lexicon-based detection system generates approximately 3000 triggers out of 75000 communication analyzed. This is a very high no. of alerts which develops a massive burden for a compliance surveillance team, especially when the current market norm of a false-positive rate is 95-99.9%. Have a look at this presentation and visit our website for detailed information. Visit: https://bit.ly/3yOG7Ee

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Lexicon Based Detection Kill Your Surveillance Efficiency

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  1. How Common Lexicon Triggers Kill Your Surveillance Efficiency

  2. Introduction Lexicon-based detection in electronic communications can be a bit like using a sledgehammer to crack a nut. Sure, it almost gets the job done, but creates huge inefficiencies and potentials risks. When it comes to lexicon-based detection, built around alerts that are triggered when certain keywords are used, one of the biggest problems is the enormous number of false positives that typically return.

  3. Massive numbers of false positives Out of a total of 75,000 communications ingested and analyzed daily, approximately 3,000 alerts may be generated during that same period. This high number of alerts that are being generated this way is a massive burden for compliance surveillance teams, especially with the current market “norm” of 95-99.9% false-positive rate. Not only is this not an effective automated form of combatting market abuse and misconduct by actors.

  4. Spending on compliance is ramping up Today, financial institutions are spending more on compliance than ever. According to a Risk Management Association survey, 50% of firms that responded said they spent between 6-10% of revenue on costs related to compliance. FIs must at least make sure that they are utilizing the right tools for the job, and not making their surveillance efforts more challenging than they need to be.

  5. Shield and its focus Shield’s approach focuses on the three C’s: Content, Context, and Characteristics. Content isn’t simply analyzed by looking for keywords. It employs a variety of text analysis tools including tokenization, stemming, fuzzy matching, AI algorithms, and Expert Driven Rules, in addition to Shield’s proprietary multilingual lexicons, which can be tailored to clients’ specific pain points. Context, meanwhile, uses machine learning and advanced Natural Language Processing (NLP) tools to determine the context of specific comments with a high level of precision.

  6. Conclusion This must be all done by a Hybrid Surveillance system that’s capable of linking data from a variety of surveillance systems to accurately and comprehensively detect market abuse scenarios, information handling issues, and more. It manages this high level of data completeness, while also reducing false positives by more than 80%, compared to inaccurate lexicon-driven alerts.

  7. Contact Us https://www.facebook.com info@shieldfc.com https://www.shieldfc.com/ https://www.instagram.com/shield.regtech/ https://twitter.com

  8. THANK YOU

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