Find Out About DBS’s Groundbreaking Fraud Detection Tools

However, improving security is perhaps one of the main ways artificial intelligence (AI) is upending the sector. Conventional anti-money laundering (AML) methods can quickly become antiquated. Here we will learn about DBS’s groundbreaking fraud detection tools.

This is where artificial intelligence (AI) comes in; by examining and forecasting what constitutes typical behaviour, it can identify questionable conduct. Furthermore, the fraud notification process, which is usually a laborious, repetitive, and ineffective procedure, can be automated using specialist AI technologies.

We employ AI and ML to filter transaction data and highlight those that do not fit expected conditions as part of our rule-based “transaction surveillance” approach at DBS. To further increase efficiency, we have also trained the algorithm to provide a numerically determined likelihood score that represents the degree of suspicion. Human analysts analyze and take action on the flagged cases before submitting a report.

The United Nations Office on Drugs and Crime (UNODC)2 believes that money laundering activities contribute between 2% and 5% of the world’s GDP, or USD 800 billion to USD 2 trillion. This shows how significant the influence of such technologies is.

DBS Bank is proof that conventional banking establishments can effectively evolve into AI-driven financial giants. Serving more than 18.68 million customers in 2024 and holding the title of World’s Best Digital Bank for several years, DBS is Asia’s top digital bank and has methodically incorporated AI into its operations.

The bank’s AI transition has produced impressive outcomes. It achieves an 85% decrease in manual processing time across core functions and handles over 45 million monthly client interactions through AI-enabled channels. In 2024, their strategic use of AI produced an economic value of about USD 563 million (SGD 750 million). This illustrates how targeted AI adoption can produce significant commercial results while upholding the security and confidence that banking requires.

Technology Design Architecture and Infrastructure

Cloud Infrastructure

• By 2024, more than 90% of apps will be hosted in hybrid cloud environments.
• Using AWS, Google Cloud, and private cloud as part of a multi-cloud strategy
• Microservices design facilitates quick deployment

AI Platform Architecture

Data Layer

• Every day, 160 million transactions are handled by real-time data processing.
• Structured and unstructured data using data lake architecture
• Advanced data transformation ETL pipelines

AI/ML Layer 

• AI models in containers for scalability
• AutoML features for quick model creation
• Infrastructure for model monitoring and retraining

Integration Layer

• API-first design
• Real-time processing using an event-driven architecture
• Micro services for adaptable implementation
• Infrastructure for Security
• Zero-trust security architecture
• Advanced encryption to safeguard data

Detecting and responding to threats in real-time

DBS’s Strategic AI Framework

DBS stands apart in the Asian banking industry thanks to its all-encompassing approach to AI application. The bank’s achievements in several important areas and its continuous acknowledgement by industry analysts demonstrate its superiority in AI maturity: 

Three main goals form the basis of DBS’s AI strategy and inform its implementation choices: 

1. Enhancement of Customer Experience: DBS has transformed conventional banking interactions in the area of customer service. Hyper-personalized services that anticipate consumer requirements before they exist are made possible by the bank’s AI-powered platforms, which evaluate customer behaviour patterns across many touchpoints.
2. Operational Transformation: DBS is dedicated to using AI to achieve operational excellence in ways that go beyond straightforward automation. The bank has put advanced AI technologies in place that improve decision-making skills while also streamlining procedures. Their innovative workflow tools have reduced loan application processing times by 85% while preserving high accuracy rates and enhancing risk assessment capabilities.
3. Innovation in Risk Management: The bank’s approach to risk management shows how artificial intelligence (AI) may turn conventional banking security measures into preventative defences. 

DBS has improved fraud detection capabilities, automated risk assessment procedures, and real-time transaction monitoring with sophisticated AI algorithms. By employing machine learning to examine intricate transaction patterns, the technology considerably lowers the number of false positives in reports of suspicious activity. While preserving strong security procedures, this change allows for a quicker reaction to new threats.