Company Size
1,000+
Region
- America
Country
- United States
Product
- Sift Content Integrity
Tech Stack
- Machine Learning
- Data Integration
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Cost Savings
- Productivity Improvements
Technology Category
- Analytics & Modeling - Machine Learning
- Application Infrastructure & Middleware - Data Exchange & Integration
Applicable Functions
- Sales & Marketing
Use Cases
- Automated Disease Diagnosis
- Fraud Detection
Services
- Data Science Services
About The Customer
The customer is a growing email marketing platform that aims to help businesses connect with their valued customers. The platform enables messaging, which is generally trusted by consumers but can be leveraged to send fraudulent messages. As the company began to scale, they found their manual vetting processes insufficient to keep up with the pace of growth. They needed a solution that could prevent fraud attacks from reaching end users and consumers, who could fall victim to fraud unknowingly enabled by the platform.
The Challenge
The email marketing platform was facing a significant challenge due to the susceptibility of the marketing technology industry to fraud attacks. The scale and severity of spam and scams were increasing, putting the onus on sending providers to protect the health of their network. As the company began to scale their business faster than their manual vetting processes would allow, they needed a solution that could keep up. They were looking for a solution that offered uptime, affordability at scale, model customization, data sharing and app integrations, and the ability to automate common support tasks such as account disablement.
The Solution
The company chose Sift for its positive experience using Sift during a prior working relationship and the ease of integration. The customization of the Sift model was a major factor, as the model learns based on trends specific to the company’s user base and unique fraud signals to more accurately detect abuse. Sift also gives them the ability to observe user-level risk as well as content-level risk to get a clearer picture of fraud on their platform. They began using Sift Content Integrity to identify the full scope of their fraud problem, and to help automatically squash abuse before it starts. The content model implementation makes it simple to integrate data and understand which actions trigger workflows. They’re now able to identify groups of fraudsters and associated accounts to keep fraud rings from compromising their ecosystem.
Operational Impact
Quantitative Benefit
Case Study missing?
Start adding your own!
Register with your work email and create a new case study profile for your business.
Related Case Studies.
Case Study
Artificial Intelligence and the implications on Medical Imaging
There are several factors simultaneously driving integration of AI in radiology. Firstly, in many countries around the world there is a discrepancy between the number of doctors trained in radiology and the rising demand for diagnostic imaging. This leads to greater demands for work efficiency and productivity. For example, the number of radiology specialists (consultant work- force) in England went up 5% between 2012 and 2015, while in the same period the number of CT and MR scans increased by 29 and 26 percentage points respectively. In Scotland, the gap widened even further (The Royal College of Radiologists 2016). Today, the average radiologist is interpreting an image every three to four seconds, eight hours a day (Choi et al. 2016).Secondly, the image resolution of today’s scanners is continuously improving – resulting in an ever greater volume of data. Indeed, the estimated overall medical data volume doubles every three years, making it harder and harder for radiologists to make good use of the available information without extra help from computerized digital processing. It is desirable, both in radiological research and in clinical diagnostics, to be able to quantitatively analyze this largely unexploited wealth of data and, for example, utilize new measurable imaging biomarkers to assess disease progression and prognosis (O’Connor et al. 2017). Experts see considerable future potential in the transformation of radiology from a discipline of qualitative interpretation to one of quantita- tive analysis, which derives clinically relevant information from extensive data sets (“radiomics”). “Images are more than pictures, they are data,” American radiologist Robert Gillies and his colleagues write (Gillies et al. 2016). Of course, this direction for radiology will require powerful, automated procedures, some of which at least will come under the field of artificial intelligence.
Case Study
Improving Diagnosis Accuracy and Saving Lives
Dr. Partho Sengupta needed a way to accurately identify disease patterns resulting from echocardiograms in order to improve diagnostics and save more lives. Specifically, he wanted to distinguish between two disparate diseases: cardiomyopathy, which directly impacts the heart muscle and often leads to heart failure, and pericarditis, which acts as if the heart is involved but doesn’t actually affect the heart. While both diseases present with similar heart conditions, the treatments are vastly different. For pericarditis, the treatment may include medication and, rarely, surgery. However, if the diagnosis is cardiomyopathy the patient undergoes medical management (i.e. a pacemaker) or in extreme cases, a heart transplant. Misdiagnosis of these disease conditions can put the patient’s life at risk and be very expensive for the hospital. Dr. Sengupta, therefore, looked to Saffron’s Natural Intelligence Platform to help his team increase the diagnosis accuracy of these medical conditions.
Case Study
Largest Production Deployment of AI and IoT Applications
To increase efficiency, develop new services, and spread a digital culture across the organization, Enel is executing an enterprise-wide digitalization strategy. Central to achieving the Fortune 100 company’s goals is the large-scale deployment of the C3 AI Suite and applications. Enel operates the world’s largest enterprise IoT system with 20 million smart meters across Italy and Spain.
Case Study
KeyBank's Digital Transformation with Confluent's Data in Motion
KeyBank, one of the nation's largest bank-based financial services companies, embarked on a national digital bank initiative following the acquisition of Laurel Road, a digital consumer lending business. The initiative aimed to build a digital bank focused on healthcare professionals looking to refinance student loans and buy homes. A significant challenge was reducing the time to market for new products by democratizing data and decoupling systems across the IT landscape. Like many large enterprises, KeyBank had a variety of vendor applications, custom applications, and other systems that were tightly coupled to one another. New projects often required developing specific point-to-point integrations for exchanging data, which did not address the needs of other downstream systems that could benefit from the same data.
Case Study
Bank BRI: Revolutionizing Financial Inclusion in Asia with Digital Banking
Bank Rakyat Indonesia (Bank BRI), one of the largest banks in Indonesia, was faced with the challenge of increasing financial inclusion among unbanked Indonesians. The bank had an ambitious target of having 84 percent of Indonesians participating in the banking system by 2022. However, the bank's legacy technologies were proving to be a hindrance in achieving this goal. Each of the bank's products had their own public APIs, which were difficult to manage, secure, and monetize. Additionally, the process of onboarding new partners using host-to-host and VPN technology was time-consuming, taking up to six months. The bank also faced the challenge of reaching a largely rural population, with an estimated $8.3 billion in currency being held outside the banking system.
Case Study
Neobank Transformation: Enhancing Compliance and Security
The client, a leading specialist digital challenger bank based in the UK, was faced with the challenge of redesigning and rebuilding their mobile banking application. The goal was to provide a more convenient way for their customers, primarily small businesses, entrepreneurs, and consumers, to interact with their platform. Additionally, they needed to implement Open Banking, a mandatory requirement from the UK financial institution. Prior to this, the client had outsourced the development of its mobile app to other vendors. However, they needed a strong team that would take over the development completely and implement new features to improve the functionality for both the client and its customers.