In the current challenging consumer credit landscape, the importance of flexible, agile data ingestion and ongoing performance modelling have never been more crucial.
Over the past 18 months, 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 busting at the seams making it challenging for banks and fintechs to sort out solutions. However, key capabilities are emerging as key differentiators and imperatives to help organizations nimbly navigate today’s dynamic credit landscape.
Consumer debt has reached unprecedented levels in the U.S. Total household debt increased by $93 billion to reach $18.04 trillion in the fourth quarter, according to the latest Quarterly Report on Household Debt and Credit. Aggregate delinquency rates increased from the previous quarter to 3.6 percent of outstanding debt in some stage of delinquency. Mortgage balances rose by $11 billion to $12.61 trillion at the end of December. Transition into serious delinquency, defined as 90 or more days past due, remained stable for mortgages, but increased for auto loans, credit cards, and HELOC balances. Auto loan balances saw an $11 billion increase to $1.66 trillion in the fourth quarter, while credit card balances increased by $45 billion from the previous quarter to reach $1.21 trillion at the end of December.
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.
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These developments have compelled banks and financial institutions to adapt their strategies to navigate the evolving credit environment. Results from our recent survey of nearly 200 key decision makers at financial services providers globally reveals that the ability to manage credit risk and prevent fraud effectively is a top priority. 49% of respondents identified managing credit risk as their biggest issue, and 48% cited detecting and preventing fraud as a primary concern.
Credit decisioning platforms 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:
Data Ingestion That is Flexible and Agile.
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.
To do this, organizations need the ability to add data fields for a given product in a few minutes – as opposed to hours. In the same survey, nearly 60% of respondents found it difficult to deploy and maintain risk decisioning models, while only 5% say it’s very easy to do so, potentially leading to inaccurate risk assessments, and missed revenue opportunities. 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.
Ongoing 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.
Credit Decisioning to Meet Revenue and Risk Goals
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.
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