• Michael Fife, the Vice President of Sales, Professional Services & Delivery at Provenir


• 07.07.2025 08:30 am
#ConsumerCredit #CreditRisk

Over the past year, the consumer credit landscape has experienced notable shifts influenced by economic conditions, regulatory changes, and evolving consumer behaviors. This has given rise to a significant market opportunity for credit decisioning technology platforms. As a result, the space has become crowded, and the vendor landscape rife with complexity for banks and fintechs to discern. However, key capabilities are emerging as key differentiators and imperatives to help organizations nimbly navigate today’s dynamic credit landscape. 

Alternative lending models, such as Buy Now, Pay Later (BNPL) services have gained traction. While they offer consumers flexible payment options, concerns have arisen regarding potential overextension and financial instability among users. This trend has prompted policymakers in the UK and the US to seek regulatory oversight over the largely unregulated BNPL industry.

These developments have compelled banks and financial institutions to adapt their strategies to navigate the evolving credit environment. Credit decisioning platforms also must keep up as they are crucial in helping organizations make the right calls at every turn across the customer lifecycle, to ensure organizations meet both their revenue and risk management goals. 

Given the evolving economy, the time is now for financial institutions to evaluate whether their tools – and specifically their risk decisioning platforms – are up to the challenge. The field of vendors offering solutions in this space is proliferating and for the uninitiated –seemingly with very little differentiation among them.  

The key is to lean in on the key capabilities that can help banks and fintechs succeed in navigating both the current market conditions. By doing so, a few key capabilities stand out as being the most critical:  

Ease and Agility in Data Ingestion

To make increasingly better decisions, it must be easy to orchestrate new data streams around a real-time decision and iterate as needed quickly. 

This requires both ease in setting up new integrations and in testing new data so that organizations can monitor efficacy in helping achieve credit decisioning goals – both in testing and production environments. 

This requires the ability to add data fields for a given product in a few minutes – as opposed to hours. Low code and no code user interfaces are bringing newfound ease and agility in this arena, providing the ability to manipulate and manage data fields, with the flexibility to pick which variables and attributes are desired. 

Continual performance monitoring

Credit risk models can’t be merely “set it and forget it”; they must be continually examined and adjusted to ensure underwriting decisions are aligned with revenue and risk management goals. To this end, it’s crucial to leverage the power of AI to deliver decision intelligence insights to understand and improve decisioning strategy performance.

This enables organizations to continually monitor and improve default rates in accordance with revenue goals. This monitoring should extend all the way down to each individual node in the decision workflow – such as fraud and identity checks, eligibility checks, etc., throughout each node in the underwriting process. This is particularly important given the rise of cash flow underwriting. 

Dialing in Credit Decisioning 

Today’s credit decisioning platforms must be up to the task of dialing in credit decisioning in a changing consumer credit landscape. Ease and agility in data ingestion and continual performance monitoring are emerging as key differentiators in the credit risk decisioning solution landscape, giving organizations the ability to course correct to meet their revenue and risk goals.