Data and analytics: the cornerstones of a credit decisioning platform

The lending market is in a state of flux. A raft of new lenders are challenging traditional lenders with slick, new products and application methods.

However, the speed of change has regulators on the back foot – and it’s further fragmenting views of customers’ credit commitments.

The biggest shake-up has come from the popularity of Buy Now, Pay Later (BNPL) models. Limited regulation of BNPL services, and a lack of data sharing by BNPL lenders, has created a gap in our understanding of consumers’ borrowing. It’s become more difficult for lenders to make fast, accurate credit decisions.

In anticipation of regulation, some BNPL lenders are sharing customers’ transaction data with UK credit bureau. In this White Paper, we look at the benefits of using a cloud-based credit appraisal platform. We explore the opportunities that credit decision platforms open to lenders, including the ability to use artificial intelligence and machine learning to innovate, test scenarios, optimise – and reduce – risk.

Over the past few years, companies like Klarna, Clearpay and Laybuy have enjoyed explosive growth with Buy Now, Pay Later

However, despite their popularity, none of these lenders were sharing customers’ transaction data with credit-reference agencies. This left traditional lenders with an incomplete view of consumer’s borrowing.

Meanwhile, tech-savvy customers have come to expect almost-immediate credit decisions. The need for lenders to make accurate, fair credit appraisals is urgent. But what’s the best way to deal with this? Look to the cloud.

Download the whitepaper now >

Adaptability

Lenders must consider far more than someone's spending and borrowing history. The need for an insightful, rich data-driven credit decisioning engine has never been more urgent.

Agility

A modular, Cloud-based credit decisioning engine is easily customised. Artificial intelligence and machine learning help lenders find, model, optimise and deploy at speed.

Accuracy

Your analytics are only as good as the data you have. Combining and integrating more data sources into decisioning enables lenders to model scenarios and make fast, fair and justifiable decisions.

Minimise risk

Accurately predict the individuals, companies and even parts of your own business that pose the most risk.

In this whitepaper, we cover:

BNPL lending
The rise of Buy Now, Pay Later lending and to incorporate this into your risk analysis
Cloud-based decisioning platforms
The advantages of cloud-based decisioning platforms over in-house ones
Artificial intelligence
How artificial intelligence and machine learning can help lenders innovate and reduce risk
Customer focus
Creating an accurate, holistic view of customers

A sneak peek into...

Data and analytics: the cornerstones of a credit-decisioning platform

Foreword

The lending market is in an unprecedented state of flux. In the UK, this is being fuelled by post-Brexit disruptions, uncertainty caused by Covid, global supply-chain issues, and a cost-of-living crisis.

The rapid growth of slick, innovative lending products from new market entrants like Buy Now, Pay Later (BNPL) and FinTech companies is also increasing uncertainty for lenders. And digital services are changing customer expectations: increasingly, they expect instant decisions from lenders available around the clock.

It feels like there’s a constant need to develop smarter lending strategies that will grow your portfolio, and bring in new customers. But this can’t be at the cost of due diligence.

Britain’s banks have recently eased the affordability tests for mortgages. But the FCA has introduced new consumer duty regulations on other credit products like loans and credit cards.

The need for decisioning platforms capable of accurate, granular detail is critical. Lenders must predict, monitor and react to how people are spending. This means having the most holistic picture possible of a customer.

In this paper, we look at the issues facing credit decisioning, and the steps being taken to help lenders make fast, accurate decisions while minimising their risk exposure.