The banking sector faces an increasingly complex landscape that requires robust, scalable, and secure technology solutions. Oracle Cloud Infrastructure (OCI) offers a powerful environment specifically tailored for artificial intelligence (AI) and machine learning (ML) projects, which can significantly enhance various banking operations. This blog post explores the advantages of using OCI for AI and ML projects in banking, highlighted by a practical use case involving fraud detection.
The banking industry has already begun using OCI Generative AI services for fraud detection. The approach was chosen for its robust security features, high-performance computing, seamless data integration, scalability, and Oracle’s proven expertise in financial services, ensuring compliance and efficiency in detecting and preventing fraud.
OCI offers the following features and benefits for the banking industry:
In this example use case, a multinational bank struggles with the increasing sophistication of financial fraud, affecting customer trust and incurring significant losses. The bank needs an advanced solution to detect and prevent fraudulent transactions in real time, minimizing false positives that can disrupt genuine transactions.
As a solution, they implement an AI-driven fraud detection system using OCI using a both online and offline approach. Online and offline processing are essential to meet business requirements for transaction approval speed and detailed analysis. Online processing allows for real-time transaction approval or rejection, ensuring swift customer service. Offline processing enables in-depth analysis of transactions at a later time, improving fraud detection accuracy and refining models without impacting real-time operations. The two processing boxes represent these distinct yet complementary stages.
Online and offline processing use different models. Online processing requires a streamlined, real-time model optimized for speed to quickly approve or reject transactions. Offline processing, on the other hand, utilizes a more complex, detailed model for thorough analysis and deep dives into transaction data, allowing for continuous improvement and adaptation of the fraud detection system. The online processing model is typically called the “transactional model,” while the offline processing model is referred to as the “analytical model.”
To implement their solution, the bank uses the following steps:
As a result, the bank reduces fraudulent transactions within the first year of implementation, while improving the accuracy of real-time fraud detection.
The bank used the OCI Streaming service for online or mobile data to handle real-time data processing. Most of the transactional data processed is in real time. They used Oracle GoldenGate for data aggregation across different channels, ensuring seamless integration and management of diverse data sources.
This solution uses the following OCI Data Science features:
The Autonomous Database service’s built-in machine learning (ML) features helped to develop and train the ML models for fraud detection, utilizing the following Autonomous Database high-performance capabilities and built-in security features:
OCI’s services contributed to the success of the deployment with the following factors:
If any bank implements this solution with described Oracle Technologies, the following table provides a clear and quantifiable overview of the benefits and improvements offered by utilizing OCI for AI and ML solutions in the banking sector:
Metric |
Value or description |
Explanation |
Fraud reduction (%) |
20% reduction in fraudulent transactions in the first year |
The implementation of AI-driven fraud detection resulted in a 20% decrease in fraudulent transactions. |
False positive reduction (%) |
15% reduction in false positives |
AI models improved detection accuracy, reducing false positives by 15%. |
Data encryption (in use) |
100% |
All data is encrypted at rest and in transit, ensuring maximum security. |
Scalability (Instances) |
Scalable from 1 to thousands of GPU and CPU instances |
OCI offers flexibility to scale computing resources as needed. |
Data processing speed (Improvement) |
Two-times faster processing speed |
HPC options resulted in data processing being twice as fast. |
Integration time (Reduction) |
30% reduction in integration time |
Seamless data integration capabilities reduced the time needed for integrating heterogeneous data sources by 30%. |
Security compliance |
100% compliance with global and regional regulations |
OCI ensures full compliance with necessary regulations, crucial for the banking sector. |
Initial investment (Reduction) |
50% reduction in upfront investment costs |
Flexibility to scale without significant upfront investments led to a 50% reduction in initial costs. |
Model training time (Reduction) |
40% reduction in model training time |
Use of high-performance Compute instances and OCI Data Science reduced model training time by 40%. |
Transaction volume handling (Improvement) |
Capable of handling two times the peak transaction volumes |
The system can scale to manage double the peak transaction volumes compared to previous capabilities. |
Using the following Oracle products and services can help you achieve these results:
OCI provides a secure, scalable, and efficient platform that’s ideal for deploying sophisticated AI and ML solutions in the banking sector. By using OCI, banks can enhance their ability to combat fraud, improve customer service, and optimize operations, all while adhering to strict regulatory requirements.
This post provides an insight into how banks can use OCI to foster innovation and efficiency in the banking sector, particularly using AI and ML technologies, driving forward a new era of digital banking solutions.
For more information on Oracle Cloud Infrastructure’s capabilities and how it supports the banking industry, explore the following resources:
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