Cloud vs. On-Premises Credit Decision Engines, Part 1
Introduction
In today’s rapidly evolving financial landscape, the adoption of cloud-based technologies is revolutionising how credit decisions are made. Traditional on-premises credit decision engines are increasingly being replaced by their cloud-based counterparts, offering enhanced flexibility, scalability, and efficiency.
A SaaS credit decision engine not only presents a turnkey solution for financial institutions looking to streamline and enhance their credit decision-making processes, but promotes greater financial inclusion by providing sophisticated, data-driven insights that were previously unattainable.
In this series of two articles, we will explore the key benefits of transitioning to a cloud-based SaaS credit decision engine and how it stands to disrupt the status quo in the lending industry.
On-Premises Credit Decision Engines:
Infrastructure Requirements
Implementing an on-premises credit decision engine demands significant upfront investments in physical infrastructure. Financial institutions must procure and manage servers, network equipment, and storage solutions to support the system.
This includes allocating dedicated space for data centres, ensuring adequate cooling systems, and maintaining uninterrupted power supplies to prevent potential disruptions.
Additionally, there is a continuous need to upgrade hardware to keep up with technological advancements and processing demands, which further contributes to the high costs and complexity associated with on-premises solutions.
Deployment and Maintenance
Deploying an on-premises credit decision engine is a time-consuming process that often requires specialised technical expertise. Setting up the system involves not only the initial installation but also configuring and integrating it with existing enterprise infrastructure.
Ongoing maintenance is critical to ensure the system’s optimal performance, involving regular software updates, security patches, and system monitoring. These tasks necessitate a dedicated IT team to address any issues that may arise and to perform routine upkeep, adding to the operational overhead.
Scalability Concerns
Scalability is a significant challenge for on-premises credit decision engines. As the volume of data and the demand for real-time processing grow, expanding the system to accommodate these increases can be both costly and logistically challenging. Procurement cycles for new hardware can be lengthy, and integrating additional resources with existing systems is often complex.
This lack of flexibility can hinder an organisation’s ability to scale up or down quickly, in response to market demands or business growth. This potentially limits a lender’s competitive edge in a rapidly changing financial landscape.
Cloud-Based SaaS Credit Decision Engines:
Definition and Characteristics
A SaaS cloud-based credit decision engine is a technology platform hosted on cloud infrastructure that automates and enhances the process of credit decisioning. Unlike on-premises systems, cloud-based decision engines are accessible over the internet and provide robust, real-time data analytics capabilities.
These decision engines are characterised by their flexibility, enabling financial institutions to scale resources as needed, without the burden of managing physical hardware. The cloud offers high availability, business continuity features, and the ability to seamlessly integrate with various data sources and external services.
Additionally, cloud-based decision engines often come with built-in security measures and compliance features to protect sensitive financial data.
Key Technologies
Services such as AWS, Azure, and Google Cloud provide the foundational infrastructure that supports the scalability, reliability, and geographic distribution of these decision engines.
Several key technologies drive the effectiveness of cloud-based SaaS credit decision engines. These include:
- Artificial Intelligence (AI) and Machine Learning (ML): AI and ML algorithms are at the core of modern credit decision engines, enabling predictive analytics and real-time decision-making.
- Big Data Analytics: Leveraging big data technologies allows these decision engines to process and analyse vast amounts of data from various sources to gain actionable insights.
- APIs and Microservices: Facilitate seamless integration with existing systems, external data providers, and third-party services.
- Queuing Technologies e.g. Kafka or EventHub enable efficient, scalable, and real-time data streaming and processing for distributed systems.
Integration
Integrating a SaaS cloud-based credit decision engine is faster and more straightforward compared to on-premises implementations. Integration involves seamlessly connecting the decision engine with the financial institution’s existing systems.
Key steps include:
- Configuration: Setting up the subscription, including specifying performance requirements, data retention options, and networking preferences.
- Integration: Using APIs and middleware to connect the credit decision engine with customer data sources, enterprise resource planning (ERP) systems, customer relationship management (CRM) platforms, and other relevant services.
- Customisation: Tailoring the decision engine to fit specific business requirements, such as rule definitions, workflows, and user permissions.
- Ongoing Management: Continuous monitoring and management of the environment to ensure performance, security, and compliance with regulatory standards.
“The cloud-based approach significantly reduces the time and cost associated with deployment and integration, allowing financial institutions to leverage cutting-edge technology with minimal disruption to their existing operations.”
Benefits of Cloud-Based SaaS Credit Decision Engines
Cost Efficiency:
Initial Investment
Leveraging a cloud-based credit decision engine requires significantly lower upfront costs compared to on-premises solutions. Financial institutions do not need to invest in expensive hardware, data centre facilities, or extensive initial setup.
Instead, lenders can leverage a pay-as-you-go model, which allows them to allocate budget more efficiently and reduce capital expenditures.
Operational Expenses
Operating costs are also minimised with cloud-based solutions. The cloud provider handles maintenance, upgrades, and technical support, eliminating the need for a large in-house IT team.
Additionally, energy costs are reduced as institutions no longer need to power and cool large data centres.
Total Cost of Ownership
Over time, the total cost of ownership (TCO) for a cloud-based engine is significantly lower than that of on-premises systems. The combination of reduced initial investment, lower operational expenses, and the scalable nature of cloud services results in substantial long-term cost savings.
This financial efficiency enables institutions to allocate resources to other strategic initiatives.
Scalability and Flexibility:
Elastic Scaling
Cloud-based SaaS credit decision engines offer elastic scaling, which means they can automatically scale up or down based on demand. This ensures that financial institutions can handle varying workloads without performance issues or unnecessary expenditures on idle resources.
Institutions can easily adjust their computational capacity to match business requirements.
Adaptability to Business Needs
The inherent flexibility of cloud solutions allows institutions to swiftly adapt to changing business needs and market conditions. Whether it’s adding new functionality, integrating with third-party services, or expanding to new markets, cloud-based decision engines can be tailored and deployed rapidly.
This adaptability keeps institutions competitive and responsive.
Handling Peak Loads
During periods of peak activity, such as promotional campaigns or financial reporting periods, cloud-based decision engines can effortlessly handle surges in data processing and transaction volumes.
This capability ensures consistent performance and reliability, preventing delays or system failures that could impact customer satisfaction and business operations.
Accessibility and Collaboration:
Remote Access
Cloud-based SaaS credit decision engines provide secure remote access, enabling employees to work from anywhere with an internet connection. This flexibility supports modern work environments, where remote work is increasingly common, and allows institutions to attract talent from a broader geographic area.
Collaboration Across Geographies
Teams spread across different locations can collaborate seamlessly with cloud-based systems. Real-time data access and shared platforms enhance communication and coordination between departments, partners, and stakeholders, fostering a more cohesive and efficient workflow.
Improved User Experience
The user experience is enhanced with intuitive interfaces and real-time data accessibility provided by cloud-based decision engines. The ability to access and analyse data quickly and accurately leads to better decision-making and a more responsive service for clients.
About the Author
Jason Kretzmann is our Chief Technology Officer and has been with ADEPT Decisions since inception in 2015, playing a key role in designing, developing and managing the ADEPT Decisions Platform.
About ADEPT Decisions
We disrupt the status quo in the lending industry by providing clients with customer decisioning, credit risk consulting and advanced analytics to level the playing field, promote financial inclusion and support a new generation of financial products.
