We decreased „false alarms“ by 60%, increased operation efficiency and improved customer experience
Even though insurers have fraud detection process, tools and dedicated employees they felt need to innovate and increase fraud detection efficiency, decrease false positives and protect themselves from evolving fraud activity.
Blindspot leveraged historical claim data applying our machine learning fraud detection framework. As the result, AI models picked up complex patterns from claims, policy and other data enabling reliable fraud detection and whitelisting at the same time.
Blindspot anti-fraud solution applied to historical claim data automated fraud detection process, allowing analysts to concentrate on relevant cases, reducing false positives by 60%.
Results of Machine Learning:
Top 40% sorted by Machine Learning score contains 90% true positives and 48% of all frauds
Case Studies
Adastra´s CRM solution implemented as a support tool for automation and more efficient campaign management at ČSOB
The target was automation and an increase in campaign management...
Dôvera Insurance Company: we reduced response time for troubleshooting and streamlined application operations with Dynatrace application monitoring
Know about application problems before a disgruntled client alerts you...
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