The banking sector is constantly grappling with challenges in transaction processing and decision-making. This article delves into a case study of one of the largest Chinese multinational banking companies, demonstrating how intelligent systems can revolutionize banking operations.
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December 26, 2023
12 minutes
Shuant Goh, Elaine Cheong, Yuqi Chen
The bank’s risk control effectiveness is now considered the best among its peers - with capabilities to verify over 700 million transactions volume per day and within 100 milliseconds response time.
The rapid advancement of financial technology has highlighted significant gaps in traditional banking systems, exposing a range of operational vulnerabilities. As banks undergo digital transformations, they must also navigate new regulatory requirements. In this case study, we focus on a multinational bank navigating these challenges in China, balancing regulatory compliance with the need to deliver seamless digital banking services. These includes:
While these regulations are specific to China, similar mandates and growing market demands for enhanced fraud protection and improved digital banking experiences are emerging across the APAC region.
The bank's previous solution providers had a long-standing reputation and were well-recognized in the market for their stability and established presence. However, these systems, developed during an earlier era of technology, faced limitations in keeping pace with the rapid advancements in the financial sector. While reliable, the infrastructure did not offer the agility and modernization required for the bank's evolving operational needs, including transaction risks, credit risk scoring, operation, marketing, etc. Let’s take a closer look at the challenges that bank faced.
1. Fragmented Data and Lack of Unified Decisioning
The bank’s data was siloed across multiple departments, preventing a holistic view of customer behavior, transaction patterns, and emerging risks. Without a unified decisioning framework, the bank struggled to assess and act on risks across multiple fronts—fraud, credit, and regulatory compliance.
2. Limited Scalability, Customization, and Real-Time Decisioning Capabilities
Legacy systems lacked the scalability to process vast amounts of data quickly enough for modern financial needs. Transaction monitoring, credit assessments, and AML checks were inefficient, delayed, and reactive, rather than proactive, hindering the bank’s ability to make timely, accurate decisions. In addition, the decisioning platform used operate like a blackbox – it lacks transparency and customizable features. It leads to high reliance on the vendor’s team which creates frustration and delays.
3. Rigid Technology Infrastructures
The outdated technology infrastructure prevented the bank from adopting new solutions like AI and machine learning, essential tools for real-time risk management and decisioning. The inability to integrate these technologies left the bank vulnerable to emerging threats, particularly in the realms of fraud, AML, and credit risks.
4. Complex Regulatory Compliance
Navigating China’s stringent regulatory environment, with laws such as the Cybersecurity Law and Personal Information Protection Law, added an additional layer of complexity. The bank needed a solution that could ensure full compliance with data localization, AML practices, and cross-border data transfer regulations, while still maintaining operational agility.
To tackle these challenges, TrustDecision helped the bank build a unified, intelligent decisioning platform that makes real-time, data-driven decisions across a wide range of financial risks. The solution breaks down into two main parts: the tech enhancements that power it, and the product features that make decision-making across operations smoother and more efficient.
1. AI-Powered Risk Detection and Predictive Analytics
The platform employs advanced AI and machine learning models to analyze transaction patterns, credit history, and customer behaviors in real-time. Leveraging these technologies, along with knowledge graphs, the system enhances risk detection by 3 to 5 folds. It can identify emerging threats like potential fraud or credit defaults and predict future behaviors, allowing the bank to proactively mitigate risks before they escalate. For example, the platform can flag high-risk credit card applications or detect money laundering activities through real-time transaction analysis.
2. One Risk Decisioning System for Various Scenarios
The integrated infrastructure enables real-time transaction monitoring, fraud detection, and credit risk evaluation, processing large volumes of data in milliseconds. This ensures rapid decision-making across various financial operations, including fraud detection, AML, credit risk assessments, and transaction approvals. Additionally, device fingerprint technology enhances decision-making by enabling cross-device tracking, leading to faster, more accurate decisions that reduce exposure to risk and build customer trust.
By leveraging an open-source technology framework, the bank can seamlessly integrate the decisioning system with its existing data resources and business processes. This facilitates the creation of a tailored decisioning engine that aligns with its unique operational needs, allowing for technology consolidation and process optimization.
3. Scalability and Adaptability to Meet Growing Demands
To keep the system effective, regular updates are necessary as fraud evolves, including adjusting rules, enhancing models, and refreshing data to stay ahead of ever-changing threats.
The platform offers unparalleled scalability and adaptability, allowing the bank to adjust operations dynamically as transaction volumes grow or new risks emerge. This flexibility is essential for adapting to evolving market conditions, regulatory changes, and technological advancements. The platform will also produce maintenance analysis reports with clear results and actionable recommendations, enabling operators to easily understand the them and take appropriate actions. The ability to fine-tune and continuously optimize the system ensures that risk assessments remain accurate and aligned with the bank's evolving operational strategy.
And with these powerful tech enhancements in place, the platform provides the capabilities necessary to transform how the bank handles risk across its operations.
1. End-to-End Operational Workflow
The platform integrates the entire process into a single system, enabling the creation, testing, deployment, execution, and monitoring of essential components like metrics, rules, and decision flows. This cohesive framework ensures a seamless, efficient process from strategy formulation to implementation, with reusable components that minimize operational overhead and enable faster adaptation to emerging risks.
2. Low-Code Platform with Natural Language Processing (NLP)
The system features a low-code environment that uses natural language processing (NLP) to allow quick configuration and adjustments to the risk control strategies. This enables risk managers and analysts to swiftly fine-tune rules and decision parameters without requiring extensive technical expertise, ensuring agility in response to evolving challenges.
3. Internal and External Data Integration
The platform integrates data from both internal and external sources into a centralized data warehouse, providing a comprehensive 360-degree view of each customer’s risk profile. This integration spans across all risk scenarios, including AML, fraud prevention, credit risk, and transaction monitoring, ensuring a unified and actionable perspective for better decision-making.
The bank’s adoption of a proactive stance towards risk management marks a significant strategic evolution. TrustDecisions has impacted beyond achieving the bank's objectives, setting a precedent for other banks to follow. The bank’s risk control effectiveness is now considered the best among its peers.
Envisioning a Resilient, Intelligent Banking Infrastructure
The transformative journey of the bank is leading towards a future where strategic significance is paramount in the industry. This outlook is grounded in three core advancements that the bank has integrated:
Smart Risk Engine, the Bedrock of Intelligent Banking
The bank has developed a smart risk engine, an intelligent decision-making risk control engine that spans the entire risk experience and operation. This reflects a broader trend in the banking sector towards intelligent automation, where artificial intelligence and machine learning are becoming central to financial institutions' risk management strategies. Such engines cannot only detect but also predict potential risks, thereby preventing them before they manifest.
Advanced Monitoring System, a New Paradigm in Decision-Making Efficiency
In line with the bank's advancements, an advanced monitoring system has been implemented, which serves as a unified enterprise-level platform. This system enhances the efficiency of decision-making processes and provides real-time warning capabilities. Integrating such systems is critical for banks to remain vigilant against emerging threats and respond to market dynamics in real-time.
Distributed Architecture, the Framework for Global Agility
The bank’s embrace of a distributed architecture underscores its commitment to reliability, performance, and scalability. This type of infrastructure is designed to support vast global transaction volumes and allows for flexible deployment to meet the diverse demands of international operations. As banks like this one adapt to market changes, improve risk management, and navigate the challenges of globalization, a distributed architecture offers the resilience and adaptability needed to manage these complexities effectively. The strategic significance of these advancements cannot be overstated. They are not merely incremental improvements but are indicative of a shift towards a more resilient, intelligent, and global banking infrastructure. As the bank continues to evolve, these systems will play an integral role in shaping the financial landscape, enabling the institution to respond to the fast-paced changes in the market with agility and insight.
This transformative journey of the bank illustrates the potential of technology in revolutionizing banking operations. It serves as an inspiration for other banks to adopt similar strategies to stay competitive and thrive in a rapidly evolving industry.
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TrustDecision empowers businesses with AI-driven decision engine designed for fraud prevention, credit risk decisioning and ensure regulatory compliance. It leverages on machine learning models and big data capabilities to deliver real-time risk insights with accuracy and automate decision-making process to deliver maximum operation efficiency.
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