HOW TO INTEGRATE AND STREAMLINE YOUR CUSTOM FRAUD PREVENTION ML MODELS WITHIN A FULLY AUTOMATED CLOUD ENVIRONMENT.
FRAUD PREVENTION USING MACHINE LEARNING EXPLAINED.
Today, most companies are exposed to fraud risks, whether it is dealing with false claims in the insurance industry or theft of personal and banking information. AI-Surge allows companies to stay ahead of fraudsters with real-time anomaly detection and fraud prediction which is critical to reduce operational expenses and increase brand and customer trust.
Big risks require a big data platform
Every year, frauds can cost your company a significant amount of money. And that’s not to mention the negative impact it can have on your brand image. Fraud can take many different forms, whether it is external like false insurance claims, or internal like an employee modifying records. The fact remains that the use of data to detect fraud is still very limited. While 72% of companies believe that big data can play a key role, only 2% report exploiting big data technologies, and 11% use statistical analysis or data mining ... *
Because dealing with fraud is an essential part of being competitive, you have decided to handle it internally. Maybe that’s why you’re here?
Fraud detection is way more than just basic regressive analysis: every day, your teams of data scientists and analysts work on analyzing suspect behavior, detecting weak signals and creating well-performing models to: :
Detect suspect behavior as early as possible
React as quickly as possible
Improve operational performance
*« Big risks require big data thinking », Global Forensic Data Analytics Survey 2014
Making meaningful connections between scattered heterogeneous data
Designing an algorithm is only part of the solution. You first need to discover connections between large quantities of varied raw data, leveraging a comprehensive technological, scalable environment. Fraudsters can get pretty creative when it comes to finding new techniques.
In practice, this means long, ever-evolving, complex projects, that use multiple expert resources and are extremely costly. Your data science teams might work for months, even years and clock up significant budget before they can deliver the first benefits for your company.
How to operationalize your fraud detection models
AI-Surge helps you quickly prototype and orchestrate complex data transformation operations, capturing varied fraud-related data from multiple sources, such as claims, health records, financial or accounting reports, to consider all possible triggers and interactions.
AI-Surge provides a fully-fledged environment that supports all steps of your fraud detection project, from exploration, to model creation, training, scoring and visualization. Deploy your algorithms in one click thanks to the high level of automation.
Use it as a standalone solution with rich graphical opportunities or integrate it with your existing ecosystem of apps to export and productionize your algorithms in record time.
You might want to read
Big Data, Low Code, Preidtive Analysis
Low-Code Data Fabric: Unlocking Productivity and Cost Savings
Big data, Low Code, Data Analytics
Data Fabric Architecture: The Future of Data Integration and Management
What is Centralized or Decentralize Decision Making
ML And The Problem Of Bias
Data Strategy for Startups