Case Study

Aite-Novarica: 2022 Impact Awards in Fraud Report

Aite-Novarica Group is an advisory firm providing mission-critical insights on technology, regulations, strategy, and operations to hundreds of banks, insurers, payments providers, and investment firms

Financial Services

Feedzai and our partners Lloyds Banking Group (LBG) are proud recipients of Aite-Novarica’s 2022 Fraud Impact Award for Best Transaction Fraud Monitoring and Decisioning Innovation. The award recognizes a joint project to enhance the bank’s fraud detection capabilities. Read this case study to learn how LBG and Feedzai approached the challenge, what was learned from the collaboration, and the results achieved.

The Fraud Detection Challenge

Fraudsters leverage breached PII data (username, password pairs, email addresses, phone numbers, etc.) to commit social engineering fraud, launch account takeover (ATO) attacks, and push scams. Authorized fraud and scams have skyrocketed in the UK, with scam-related fraud losses exceeding card fraud losses for the first time in 2021. These trends indicate that fraudsters exploit a fragmented risk management approach to enable their criminal activity.

Lloyds Banking Group collaborated with Feedzai to take an innovative approach to reducing financial crime.

Lloyds Banking Group’s Main Goals for the Project Were to:

  • share data across the organization with a set of new machine learning practices to address payment risk across multiple digital channels telephone, and in-branch;
  • build new machine learning models from the ground up with rapid iteration and deployment, with a particular focus on improving the detection of authorized fraud;
  • score payments with millisecond latency, irrespective of throughput, by using a secure, scalable cloud deployment;
  • and automate the feedback loop for system adjustments.

Results Achieved from Innovative Fraud Detection with Feedzai and Lloyds Banking Group

The collaboration between Feedzai and Lloyds Banking Group resulted in a 360-degree client view that incorporated data across multiple sources within LBG, breaking down organizational data silos, and fully tapping into the power of enterprise data. The accomplishments from the project include:

  • a significant reduction in false positives;
  • improved detection of APP fraud occurring in real time;
  • and an increased value detection rate compared to the previous system.

Aite-Novarica noted several elements that make this collaboration innovative, including:

  • cloud-based implementation accessible by data science teams from both Feedzai and LBG to enable collaboration;
  • automation of the feedback loop for system adjustments;
  • implementation of machine learning at scale – enabling LBG to replace its legacy solutions;
  • and over 300 models trained and tested within six months with a technology that can be used to deploy the best models immediately after development.

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