Both lenders and borrowers stand to benefit from a clearer picture of consumer credit risk. With new highly predictive credit scoring models achieving a finer separation between good and bad risk, lenders can make better lending decisions with reduced credit losses. And with this more accurate prediction of default behaviour comes the promise of fairer access to credit for all and the increased sophistication of risk-based, highly-personalised credit offerings.

As we move towards a sharper picture of consumer credit risk, here are the top four trends poised to reshape credit scoring.

Credit Scoring Trend #1: Explainable AI will be adopted to drive better and more transparent credit decisions

There is a growing call by consumers, lenders and regulators to understand how decisions are determined. The plummeting cost of computing and the unprecedented rise in data availability has encouraged the adoption of highly predictive artificial intelligence (AI) and machine learning (ML) techniques in credit scoring. As AI replaces many steps and stages in the credit assessment and decisioning process, lenders are grappling with the need to ensure the decisions made by AI are fair, ethical and unbiased.

This need for accountability is likely to be hard to measure if AI technology is allowed to become a “black box”, whereby information goes in, and a decision comes out without any detail on what informed the decision. The ability to explain how AI systems assess data and form decisions is critical to overcoming this challenge. Opening the black box and shedding light on the making of these decisions is what explainable AI (xAI) is all about.

Consumer risk decisions impact lives, so the ability to explain the reason behind the credit score is vital to building consumer engagement and enabling borrowers to see, understand and take control of their financial wellbeing.

There is a growing imperative for credit scoring models to make this leap into delivering transparent and easy-to-explain results while at the same time providing a step-up in performance and prediction. So far, the only consumer credit score in Australia to achieve this milestone is Equifax's latest generation credit score. Developed using an explainable AI machine learning modelling technique and incorporating recent data sets, Equifax One Score is not only highly accurate and stable, but it can also generate individual actionable reason codes.

Importantly, it is now possible for financial institutions to more easily explain their lending decisions to customers and approve more consumers for credit without taking on additional risk. It's also easier for consumers to capitalise on credit opportunities and forge a path to improved financial health.

With explainable AI in its infancy in the field of consumer risk modelling, widespread adoption of this technology will bring greater certainty to decision making and the tools for consumers to live their financial best.

Credit Scoring Trend #2: High-frequency data will build resilience in decision making

Businesses increasingly recognise that not extracting value from data means being left behind or leaving money on the table. Consumers have become savvier; they expect brands to know what is most relevant to them. The volume of data available to inform decisions continues to skyrocket. And with these highly diverse digital data sets comes the opportunity to improve credit scoring and better predict repayment behaviour. 

A significant learning from the pandemic has been the benefits of using granular data to build resilience into decision-making. And so, the push had begun to drill down beneath the surface of macro trends to understand digital data at a customer level. Innovative machine learning analytical techniques are being used to extract more value from existing data and a broad range of sources like current enquiry, geo-demographic and Buy Now Pay Later (BNPL).

With the help of machine learning algorithms, patterns are being discovered in these alternative data sources that can provide a red flag to fraudulent activity or inability to repay debt. Submitting multiple mobile applications to different telcos within a short period, for example, statistically indicates higher credit risk. 

For people with thin files or no credit history, alternative data shows significant potential as a tool for improving access to credit. With 20.6 million Australians estimated to own a smartphone, a limited mainstream credit history no longer has to be a stumbling block to obtaining a mortgage.

Open banking has made it possible to offer new consumer-permissioned data assets like bank transaction data alongside traditional credit bureau information. Equifax has recently built a predictive affordability solution based on transaction data, which searches for previously unrecognised positive and negative correlations in income and expense data.

Equifax's latest credit score was built using both recent and longer-term Repayment History Information (RHI) and up to five years of credit enquiry and default trends. So rather than being sensitive to any single data point that may be missing or not useful during times of economic upheaval, the model bases its risk assessment on a broad range of data and the length of time information has been in the credit file.

Open banking and CCR promises many more opportunities for granular data to help build up the stability and accuracy of credit scores.

Credit Scoring Trend #3: The democratisation of data will accelerate the speed of credit decisions

The way forward is to democratise this high-frequency data and insight: to provide enterprise-wide access to it so that everyone can understand the data and use it to expedite decision making. Organisations that open up access to data this way no longer have to wait around for others to create the insights they need.

While monthly data updates were once the norm, this pandemic's abrupt and rapid changes have necessitated a faster pace of data collection and analysis. People have come to look to more timely, high-frequency data that provides a view of what is happening now, not what has previously happened.

So data is being increasingly captured, processed and analysed on a weekly, daily or real-time basis. But high-frequency data alone does not guarantee speed to insight. When data is gathered and processed quickly, it might not have all the information a stakeholder is after or exist in a format that is needed to drive action.

Data that is collected at rapid speed and made accessible to all is the future of credit scoring. As an example, Equifax's new generation credit score dynamically updates as credit-related information becomes available, both positive and negative. With more dependence on positive data and less on current enquiry data in Equifax One Score, there should no longer be such a disjoint between the consumers and lenders requirements and expectations. A score that is common to both lenders and consumers champions a better customer experience and an improvement in financial literacy.

As consumers begin to understand and accept that credit scoring is a dependable assessment of their creditworthiness, they will increasingly learn to leverage the value of data to negotiate on credit rates and terms. Within this more transparent and effective ecosystem, lenders can personalise their engagement with customers, innovating on behalf of the consumer and driving fast decisions for profitable portfolio growth.

Credit Scoring Trend #4: The influential role of D&A professionals in credit risk will broaden

Risk analytics is changing, and data analytics (D&A) professionals will have to broaden their skill set to keep up. As the volume of data rises and new granular data sets emerge, advanced competencies will be required. It will no longer be enough to keep solving problems the same way. D&A professionals will need to be versatile. In addition to the technical proficiency of handling complex data, they will need to be critical thinkers, visualisers and good communicators.

How to get a better outcome for a credit decision? What other information can be used? How will my recommendation affect the bottom line of a business? These are the type of questions D&A professionals will need to answer every day. The new age D&A expert must be skilled at clearly connecting complex technical concepts to business outcomes.

Organisations will welcome the leadership of business scientists, chief data officers (CDO) and chief analytics officers (CAO) as D&A professionals play an increasingly vital role in the end-to-end formulation of credit risk solutions.

Useful reading: What it Means to Be a Business Scientist with Influence

Equifax One Score is the most predictive and reliable credit score in Australia. Its usefulness in helping lenders make underwriting and credit monitoring decisions brings immense value in an ever-changing environment where speed and accuracy are vital. Email us to find out more.
 

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