GlobalLogic Leverages Big Data & AIOps to Protect a Leading UK Retail Bank Against Payments Fraud
Customer Company Size
Large Corporate
Region
- Europe
Country
- United Kingdom
Product
- Splunk’s Machine Learning Toolkit
Tech Stack
- Machine Learning
- Big Data
- AIOps
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Cost Savings
- Customer Satisfaction
- Productivity Improvements
Technology Category
- Analytics & Modeling - Big Data Analytics
- Analytics & Modeling - Machine Learning
Applicable Industries
- Finance & Insurance
Applicable Functions
- Business Operation
Use Cases
- Fraud Detection
- Real-Time Location System (RTLS)
Services
- Data Science Services
- System Integration
About The Customer
The customer in this case study is a leading UK retail bank with over 14 million active customers and multiple brands under its umbrella. As a large corporate entity, the bank operates in the finance and insurance industry, providing a wide range of financial services to its customers. The bank has been facing increasing challenges related to fraud, especially with the rise of online banking and geopolitical tensions that have heightened the risk of fraudulent activities from non-UK countries. The bank's primary focus is on ensuring the security of its payment systems and protecting its customers' information and assets. To achieve this, the bank has been seeking advanced technological solutions to monitor and detect fraudulent activities in real-time, thereby safeguarding its operations and maintaining customer trust.
The Challenge
With 14 million+ active customers and multiple brands, the risk of fraud activity had greatly increased and recently, due to geo-political tensions, the bank wanted the ability to monitor unusual behaviour and fraudulent activity mainly from Non-UK Countries. In particular, this bank needed to gain insights into payments-related fraudulent activity, including payments going to a single account from multiple users & credit card applications and approvals. By having additional monitoring in place, the bank was confident it would be able to implement a process and associated actions that would protect customers and enterprise information, assets, accounts and transactions through the real-time, near-real-time or batch analysis of activities by users and other defined entities. Ultimately, they wanted a solution that automated the detection of potentially fraudulent activity and flagged that activity for review. The bank knew exactly what it wanted and the importance of getting it done, but they were struggling to put the necessary tools in place.
The Solution
Initially, the Bank’s Digital Security & Operational teams had no means of monitoring these fraudulent activities apart from relying on Cyber Security team to provide a view of that data. GlobalLogic was on-boarded to provide professional services to the bank. With the adoption of Splunk’s Machine Learning Toolkit, we began indexing relevant machine data before searching and correlating it to identify the patterns of fraud. Doing so enabled us to put alerts in place that flagged fraud attempts in real time and prevented them from impacting the bottom line. Our AI-enabled approach and engineering capabilities enabled us to analyse data coming from multiple sources, such as F5 devices, authentication systems, transaction processing systems, payment and billing systems, databases etc. Leveraging all the associated data helped to detect anomalous internal and external behaviour, as well as indicators of failures through statistical analysis and machine learning capabilities. All these insights were collated into a customised form-based dashboard, with drill downs providing easy access to targeted data for their investigative needs. We also created rules & dedicated dashboards capable of correlating possible fraud indicators across all channels. Not only did this eliminate silos and manually intensive and cumbersome investigation processes, the bank now has a 360-degree view of their data. Digital Teams can see all customer activity in one place and look for anomalous changes in patterns in single or multiple channels that might indicate fraudulent activity.
Operational Impact
Quantitative Benefit
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