Automated credit risk management process puts ABBANK at the forefront of Vietnam’s credit revolution

Historically, credit offerings from banks throughout Vietnam were perceived as complicated and cumbersome by customers, including those with the necessary financial history and means to obtain credit. Many, especially midmarket borrowers with limited credit history, viewed some banks as business inhibitors, not catalysts.

An Binh Commercial Joint Stock Bank (ABBANK) set out to change that. ABBANK is leading the charge to provide faster, easier-to-access credit services to consumer and corporate customers throughout Vietnam.

Since 1993, ABBANK has grown to become one of the 10 largest commercial banks in the country. It has more than 160 branches nationwide and provides a wide range of banking services across its digital platforms. ABBANK is transforming its banking services to improve access to financing for underserved populations and promote financial inclusion. This initiative is underpinned by the bank’s new credit decisioning system – driven by SAS Risk Modeling and SAS Intelligent Decisioning on SAS Viya 4 – which enables ABBANK to reach many more potential new customers.

Seizing the opportunity with SAS Risk Modeling and SAS Intelligent Decisioning

Recognizing this sizeable, untapped opportunity, ABBANK moved quickly and decisively to support this sector by adopting a data-led game plan to create a breakthrough credit decisioning system. Through improving credit access and risk analysis, the bank aimed to elevate customer experience and capture a new market share.

Featuring state-of-the-art analytics technologies, a solution encompassing SAS Risk Modeling and SAS Intelligent Decisioning on the cloud-native SAS Viya platform enables well-informed credit decisions, reduces turnaround times and provides an enhanced experience for ABBANK’s customers. It also supports responsible lending practices and mitigates potential risks, while maintaining a healthy loan portfolio.

Additionally, the solution was deployed to accelerate the many algorithms used in the risk modeling solution. As a high-performance, in-memory process and machine learning platform, the system is calibrated to identify potential risk hazards and associated costs faster. It is also easy to customize, enabling ABBANK to adapt to new banking industry policies, trading rules and processes.

Streamlining the credit risk management process

Addressing challenges related to ABBANK’s legacy credit systems, scoring models and manual processes was step one of the project. A thorough assessment deep dive of existing systems and processes was conducted to shape the transformation strategy. Step two involved finding the right partners. SAS was tasked with providing cutting-edge technologies and expertise for risk assessment and credit decisioning, while implementation was overseen by Thakral One.

Working together, SAS and Thakral One streamlined ABBANK’s credit risk management processes to enable faster decision making, precise risk assessment and constant updating of credit models from a unified model risk management platform.

Since robust risk evaluation and analysis processes underpin credit services, implementing the SAS solution was a top priority for ABBANK. Now the bank can develop and manage risk models in a more consistent, auditable, transparent and secure way.

“Advanced analytics of SAS Risk Modeling and SAS Intelligent Decisioning enabled us to have deeper lender insights,” says Mr. Pham Duy Hieu, Acting CEO of ABBANK. “By automating the credit risk management process, we could quickly evaluate loan applications and make better credit decisions. This reduced the likelihood of defaults and non-performing loans, thus reducing the overall risk.”

Successful outcomes improving bottom line

Based on clear key performance indicators (KPIs) set by ABBANK, deploying the SAS solution has made a difference in three main areas:

Reducing risk of credit loans. Using automated risk management solutions, ABBANK can now evaluate the risk of loan applications and make more accurate credit decisions faster to reduce the likelihood of defaults and nonperforming loans.

Better data for better analysis and outcomes. Available, accessible, high-quality data has significantly improved the bank’s risk analysis and reporting procedures. Prospective hazards can be identified faster and analyzed in depth to identify future trends and causes to further reduce risk.

Enhanced productivity leading to increased revenue. Streamlining credit management processes has enhanced efficiency and bottom-line results. Teams and colleagues now spend more time on applications that have a higher chance of resulting in deals. Deeper risk analysis also allows the bank to increase revenue while minimizing defaults.

Broadening customer reach and increasing revenue

Looking to the future, ABBANK expects to expand the credit decisioning system beyond its retail customer base and include small and medium enterprises to maximize the system’s versatile functionalities while broadening customer reach and increasing revenue.

While rebuilding the bank’s credit decisioning process is a critical part of ABBANK’s growth plan, more transformation projects are in the pipeline.

In its quest to serve an additional 2 million customers by 2025, ABBANK will introduce new data strategies and analytics tools to get closer to current customers and attract new ones. The insights will be used to develop seamless, enriched customer experiences across all its channels and platforms.

Ongoing automation of decision-making processes and accelerating outcomes is equally important as the bank strives to future-proof itself from disruptions and challenges down the road.

Staying strong in these areas is also essential to help the bank stay relevant and attractive to Vietnam’s large base of young consumers, many of whom are digital natives with high digital expectations. By blazing a trail on the credit front, ABBANK is well positioned to capture the attention and market share of the growing middle class and affluent population.