With financial institutions under pressure to deliver faster, more personalised lending decisions, how are your solutions helping to strike the balance between automation and human judgment?
At Aryza, we see the balance between automation and human judgment as essential rather than a trade-off. Our decisioning technology is designed to remove friction from the lending process by automatically collecting data from internal and external sources, including Credit Reference Agencies and Open Banking providers. Where speed is the priority, configurable rules allow lenders to set clear criteria for auto-acceptance or auto-decline, ensuring consistent and rapid outcomes.
Equally, when a more nuanced view is needed, the same data can be presented to an underwriter in a clear, accessible format. This enables them to make informed, unbiased decisions based on a comprehensive view of the applicant’s circumstances. In this way, automation enhances efficiency and consistency, while human judgment brings context and flexibility, supporting fairer, faster, and more personalised lending.
What’s the most underused or misunderstood data source in mortgage lending today, and what could the industry be doing differently with it?
One of the most overlooked data sources in mortgage lending today is Open Banking. While the technology has been available for some time, many lenders continue to rely heavily on traditional credit bureau data. This creates blind spots when assessing applications, especially for groups like first-time buyers moving from rented accommodation, younger borrowers, or recent migrants with thin credit files, but who can clearly demonstrate an ability to pay.
Open Banking provides a much clearer, real-time view of affordability by showing consistent rent payments, household expenditure, and overall financial behaviour. For lenders, this presents an opportunity to make more inclusive and better-informed decisions, expanding access to credit while still managing risk effectively. By embracing this data source more fully, the industry could unlock both fairer outcomes for applicants and stronger lending portfolios.
As consumer expectations around privacy and data security evolve, how are you building trust while still enabling intelligent decision-making?
As an ISO27001-accredited organisation, we place information security at the core of our solutions. Every automated decision made through our technology is recorded and fully auditable, enabling lenders to evidence not only the outcome but also the data and rules that informed it.
Our role as a B2B provider is to give lenders the tools to demonstrate fairness, consistency, and compliance. By equipping them with transparent and secure decisioning processes, we help them build trust and confidence with their consumers.
How do you see AI & machine learning transforming risk assessment and credit modelling over the next two years, and what needs to happen first?
AI and machine learning are already transforming this field, and their influence will only grow stronger over the next two years. These technologies allow lenders to utilise real-time data, automate workflows, and go far beyond the constraints of traditional scorecards. The outcome is the ability to detect subtle trends, identify emerging risks, and suggest customised terms or repayment plans, with some leveraging generative AI to produce underwriting narratives that aid decision-making.
The critical factor, however, is explainability. Models must be transparent, auditable, and compliant with local regulations to ensure fairness and consistency. Lenders will need to demonstrate not only that decisions are accurate, but that they are equitable and defensible to regulators. Getting this balance right is what will unlock the full potential of AI in credit risk while maintaining consumer and regulatory trust.
In an ecosystem where so many systems need to talk to each other, how are you tackling the challenge of interoperability and real-time integration?
Interoperability is one of the biggest challenges in modern lending, where so many systems and data sources need to connect seamlessly. Our approach involves using decisioning technology as an orchestration layer, ensuring interactions with third-party providers occur in the correct order and at the right time, guided by its highly configurable rules engine.
By consuming data as quickly as external sources can provide it, we enable decisions to be made almost instantaneously. This creates a smooth, real-time flow of information that supports faster, more consistent outcomes while reducing operational friction for lenders and improving the experience for borrowers.
