Analytics modernisation: How we helped a global bank transition from SAS-based risk models to a scalable and cost-effective open-source platform
Introduction
The client is a large, globally operating financial institution that has extensive operations across multiple regions and markets. It was heavily reliant on SAS-based risk models to manage and assess risk across its business units. However, these models were proving to be inefficient and increasingly difficult to maintain, leading to high operational costs and limited flexibility.The reliance on legacy technology also hindered the bank’s ability to scale its operations to meet the growing demands of real-time risk management. These models were not suited for handling the bank’s growing volume of risk data and provided limited flexibility in analytics, making it difficult to respond to real-time risk factors and emerging market trends.
It limited the client’s ability to adopt cutting-edge technologies and maintain competitiveness in a rapidly evolving regulatory landscape. It started impacting the bank’s operational agility. The manual processes associated with SAS models introduced delays in risk reporting and validation, limiting the bank’s ability to proactively manage risks across its various business units.This inefficiency also extended to customer offerings, where the bank was unable to extract actionable insights in real-time, hindering its capacity to provide personalised services like tailored loan offerings. In an increasingly data-driven industry, resolving this problem was essential to maintain regulatory compliance, optimise risk management, and enhance customer experience.
The situation
The client needed to transition to a modern, open-source platform capable of handling real-time data processing, improving operational efficiency, and reducing costs. We supported the client in establishing a more scalable and agile risk management framework, enabling the bank to make faster, data-driven decisions while ensuring compliance with global regulatory standards.
So, what did we do?
The Deloitte way
The approach centered on the modernisation of the bank's analytics capabilities, with a focus on transitioning from SAS to more scalable and cost-effective open-source platforms such as Python and PySpark.
Migration to open-source: We migrated the bank’s SAS-based models to open-source tools, which significantly reduced operational costs while increasing flexibility and scalability. This move allowed the institution to adopt solutions for faster processing and real-time risk monitoring.
Automated risk model validation: We automated the process of validating risk models, eliminating the need for manual intervention and ensuring continuous monitoring of key risk factors. This automation resulted in more accurate and timely risk assessments.
The impact
The first outcome was a substantial reduction in costs and a dramatic improvement in the scalability of the bank’s risk analytics capabilities:
Reduced costs: The migration to open-source tools resulted in a significant reduction in operational and licensing costs, freeing up resources for other critical functions.
Scalability and flexibility: The new analytics infrastructure could now easily scale to handle large datasets, allowing the bank to perform real-time analytics at a fraction of the cost. It could also identify and mitigate risks across multiple regions.
Automation of risk processes: By automating risk model validation and monitoring, the bank reduced human error and made its analytics more accurate and responsive.
Apart from the immediate cost savings and enhanced ability to analyse risk in real-time, the bank new infrastructure has enabled the bank future its analytics capabilities. It can now easily incorporate new tools and methodologies that were earlier hard to implement. It will also enable the client to better serve its customers through personalised offerings, such as tailored financial products, leading to higher customer satisfaction and ROI.
Drilling in success
Christine Ahn
