Bank Albilad is a Saudi Arabian financial institution established in 2004, headquartered in Riyadh. It is one of the rapidly growing banks in the Kingdom, offering a wide range of Sharia-compliant banking products and services. The bank is known for its focus on innovation and technology-driven solutions to enhance customer experience. In recent years, it has invested heavily in digital transformation, offering advanced online and mobile banking services.

Bank Albilad decided to bolster its entire fraud management framework. The Saudi financial institution sought for a cutting-edge solution with the capacity of real time fraud detection and prevention, guaranteeing compliance with the Saudi Central Bank’s fraud requirements. The ideal solution should additionally provide a best-in-class interface for alerts and case management, while also supporting advanced fraud monitoring though analytics and machine learning (ML) models, ultimately resulting in outstanding customer experiences and security. 

Given Bank Albilad’s goal to provide “banking with a piece of mind,” this special emphasis on effective fraud detection and prevention comes as no surprise. Over the years, Bank Albilad has established a legacy of trust, built on the Islamic principles of banking. The financial institution strives for excellence in the Islamic banking sector through constant innovation, impactful initiatives, and genuine Shariah compliant banking products.

Key Highlights

• 100% transactions assessed in real time, enabling faster and more informed risk-based decisions.

• 100% newly opened digital accounts are evaluated by the scoring system in real time.

• 30% increase in efficiency by agents investigating potentially fraudulent cases by adopting an integrated fraud prevention platform leading to significant savings allowing the team to focus on actual threats.

• 50% reduction on false positives

• 70% increase in fraud loss prevention

• 98% of transactions approved within seconds thanks to ML models and real-time fraud 

One step ahead of fraudsters

To outpace the ever-evolving landscape of fraud schemes, Bank Albilad has turned to the embedded AI and machine learning to hunt for fraudulent transactions in real time. SAS Fraud Management enables financial institutions to respond faster to new threats, reduce false positives, and make better-informed decisions for a better customer experience.

This end-to-end fraud detection and prevention solution supports multiple channels and lines of business for enterprisewide monitoring. It also simplifies data integration, which allowed Bank Albilad to combine all internal, external and third-party data to create better predictive models tuned to the institution’s unique needs. Bringing together this data on a single technology platform gives Bank Albilad the flexibility to scale up or out as business changes, while responding faster to new threats as they arise.

The implementation of the fully integrated SAS AI-driven framework enabled Bank Albilad to detect and prevent fraud both on transactional and customer account level. The bank can rapidly identify and close mule accounts at every phase from onboarding and afterwards, using business and AI-based insights. Plus, Bank Albilad is capable of detecting more complex patterns and prevent potential fraudulent transactions utilizing ML techniques.

Bank Albilad leveraged the SAS Fraud Management solution to optimize existing rules and minimize false positive alerts. This made it possible for the bank's compliance team to focus solely on investigating the most suspicious accounts and transactions. Moreover, the integration of AI-driven outputs with other components armed the compliance team with the essential interface for ensuring alignment, making informed decisions, and consistently monitoring and evaluating the models' outputs and performance.

Identifying and eliminating mule accounts

Bank Albilad is the first Saudi banking institution to implement ad-hoc network analysis using SAS network diagrams through the SAS Viya Visual Investigator. This feature helps Bank Albilad identify mule account communities by analysing customer relationships.

Using advanced network visualization techniques, the system enables the institution’s security experts to analyze network diagrams more efficiently to pinpoint suspicious connections and clusters of mule activity. This results in enhanced overall detection, streamlining the investigation process and reporting to regulatory bodies. 

Effectively addressing implementation challenges

Managing multiple parallel tracks and coordinating various teams posed significant challenges. Thanks to the seamless collaboration of all parties involved, Bank Albilad was able to meet its targets. Bridging the data gap across multiple channels was another key challenge.

The expertise of SAS in data mapping, combined with the dedication of the bank’s internal team, ensured a seamless data flow across integrated systems.  The project also faced tight timelines due to the Eid holidays. Despite these challenges, the team's commitment was instrumental in ensuring timely delivery.