Teradata Fraud Management enabled the system to make autonomous decisions in real time that aligned with the procedures, security and high availability guidelines. The solution provided new levels of detail, such as time series and sequences of events, to better assist with fraud investigations.
- The solution had to have the capability to identify fraud across all channels and products, including mobile.
- This required gathering and integrating quality data from emerging and existing sources, establishing pipelines for data processing, and ensuring the right data was available for the right analytic techniques.
- It also required cross-functional collaboration with data scientists, IT professionals, engineers, bank representatives and others to make sure the solutions would deliver the high-value outcomes the bank needed.
Businesses are more vulnerable to fraud risk than ever before. Although the degree of risk varies by the industry and takes many forms, all companies face increased threats and most do not have the capability to identify and quickly mitigate risk before it negatively impacts their business. Some risks, such as financial fraud, can cost the companies millions of dollars per year and erode their brand equity.
Organizations are exposed to varying types and degrees of frauds perpetrated by customers, employees, vendors, third parties and others. Every year, the companies lose billions of dollars to financial crimes and fraud. The growth of the online channels has led to increased instances of fraud, especially for eCommerce entities, airlines and banks, among others.
The typical approach to combat fraud is to strengthen the process controls through either system-based or manual controls. Another approach used extensively in proactive fraud detection is leveraging the analytical models with predictive capabilities to detect vulnerable transactions at the outset.
The project entailed integrating open-source solutions and deploying production models, then applying deep learning analytics to extend and improve the models. A framework was created to manage and track the models in the production system, and to make sure the models could be trusted.
Teradata Business Value Framework
Teradata helps by leveraging the Teradata Business Value Framework, which identifies and prioritizes business use cases to align with the company’s strategic initiatives. The low-risk engagement model brought together the business knowledge, data science and advanced analytic solutions to make claims fraud detection more effective.
The Teradata Business Value Framework helps to determine how to best utilize the company’s data to deliver the most value in an accelerated time frame. The software helps the analytic model identify potential cases of fraud while intelligently avoiding false positives. Operational decisions are shifted from users to AI systems.
However, human intervention is still necessary in some cases. For example, the model can identify anomalies, such as debit card purchases taking place around the world, but analysts are needed to determine if it’s fraud or if a bank customer simply made an online purchase that sent a payment to China, then bought an item the next day from a retailer based in London.
Implementing deep learning and artificial intelligence (AI) modeling solutions can be difficult for companies to achieve on their own. They can benefit by partnering with a company that has the proven capabilities to implement technology-enabled solutions that deliver high-value outcomes.
- By using Teradata Business Analytics Solutions to optimize business processes, Teradata Fraud Management provided the ability to identify new fraud cases faster and also find patterns and details that could predict future fraud.
- Teradata Business Value Framework also implemented new processes that withheld automated payments on claims that appeared fraudulent. The Fraud investigators armed with technologies such as machine learning and advanced analytics could then review those cases for further action.
- The Teradata Business Analytics Solutions also made the fraud team more productive by eliminating red herrings that could be mistaken as fraud so investigators were not investing time and resources on legitimate claims.
The same analytic techniques and methodologies used to detect the claims fraud at higher rates can be leveraged by businesses in any industry to reduce risk exposure and solve other challenges.
The Teradata Fraud Management consultative approach backed by industry-leading solutions uncover analytic insights that give companies the ability to create and automate new business rules to make processes more efficient and effective.
Locus IT offers a multi-layered Teradata Fraud Management solution that helps businesses minimize fraud losses, maximize revenue and minimize operational costs.
Locus IT enables you to streamline operations with greater agility by fine tuning screening models and strategies, whether it is based on the relationship, across multiple channels, various devices or levels of service. For more information please contact us.