Scoring and analytics

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Scoring and analytics - What can it do for me?

Data is at the heart of everything Experian does and, over the last 30 years, it has developed expertise in the interpretation and use of credit bureau and clients’ customer data for every aspect of the client relationship.

How can we help you?

Analytics turns this data into information, which enables organisations to predict how applicants and customers will behave in the future. Experian builds market-leading credit bureau and custom scores to help clients make better, faster decisions and deliver their promise to their customers and their shareholders.

Key areas:

  • Customer Acquisition
    Application scoring in the origination process is used to predict how a potential customer will behave in the future. With this information, decisions can be made about whether to accept or decline an applicant. This view of each individual can help develop a picture of the potential value of an accepted applicant, to inform decisions about the product and terms offered.
  • Customer Management
    Behavioural scoring is used throughout the life of a customer relationship to inform management strategies for each customer, whether managing bad customers or extending the relationship with good customers. For organisations that have many relationships with their customers, customer-level scoring brings together the different aspects of the relationship into one complete picture.
  • Collections & Recoveries
    Behavioural scoring is also used to prioritise collections activities to maximise recoveries and reduce collections costs. An understanding of the customer drives collections and debt recovery activity.
  • Basel II
    The new Basel Capital Accord (Basel II) requires Banks to estimate Probability of Default (PD), Loss Given Default (LGD) and Exposure at Default (EAD) for groups of exposures. Experian has helped many organisations around the world build estimates of PD, LGD and EAD and embed them in their business processes.

Behavioural scoring is used throughout the life of a customer relationship to inform management strategies for each customer, these can include:

  • Propensity – will this customer take up offers of further products?
  • Customer value – how valuable is this customer to the organisation now and in the future?
  • Cross-sell/up-sell – which customers are the best candidates for offers of other products and services?
  • Attrition – will this customer stop using a credit facility in the future?
  • Indebtedness – how will the customer’s level of indebtedness affect their ability to honour this credit commitment or cope with a limit increase?
  • Risk and delinquency – will this customer continue to be delinquent and how much is at risk?
  • Potential recovery value – how much could be recovered from the account and will it cover the costs of the collections activity?
  • Bankruptcy – will this customer become bankrupt?

Decision Analytics scoring models

  • Experian builds generic and custom scoring models in markets all over the world.
  • Generic scoring models are built from Experian credit bureau data or are derived from knowledge of the credit industry tempered by the local market conditions.
  • Generic scores can be quicker to implement and do not require an archive of historical data for development, which makes them very useful in start-up situations.
  • Experian has built credit bureau scores in many countries around the world, covering all stages of the customer lifecycle to predict a wide variety of outcomes.
  • Custom scoring models are built using historical data supplied by the client and are tuned to the client’s customer profile. They are designed in consultation with the organisation to meet specific business objectives.

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A new approach to model development

  • Many organisations lack the data, resources or knowledge to build and maintain effective models. Experian has developed a new approach to the delivery and monitoring of models using a combination of pooled data, analytical techniques, software and consulting support.
  • Data pooling brings together data from client organisations and credit bureaux, using proprietary algorithms to align the profile of the pool to a client’s own data to produce a robust sample of data for development. The resulting model is more robust and predictive, enabling custom models to be implemented, especially for organisations with limited data.
  • Once scoring models are built, Experian undertakes detailed and regular monitoring to ensure that they continue to work optimally.
  • Consulting support enables clients to accelerate the use of a range of different scoring objectives.

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Case Study: Express Gifts

Business Challenge

Express Gifts use an automated decisioning system with credit scorecards for applicants applying for a credit account. This creates an accurate risk assessment to decide whether to accept or reject an applicant and to set an appropriate initial credit limit. With the changes in the market, Express Gifts recognised that its suite of scorecards were showing signs of deteriorating predictiveness and a redevelopment would provide more accurate decisioning for the organisation.

The Decision Analytics answer

Experian has worked closely with Express Gifts over the past five years, providing hosted application processing, credit bureau data and scorecards. Express Gifts were confident that Experian’s knowledge of its business, as well as over twenty years of experience in building risk models on a combination of bureau and application data, made it the best choice to build the new suite of scorecards.

The Benefits

  • ROI in 12 months based on the additional value delivered from the scorecards
  • Reduction in bad debt levels as more accurate decisions are made on applicants
  • Reduced exposure by declining higher risk applicants and setting more appropriate credit limits for accepted customers
  • Increased confidence in the decisions made with accurate scorecards for better risk management
  • Accurate assessment of scorecard value using initial Champion/Challenger strategy deployment

Quote

Andy Bragg, Head of Credit at Express Gifts said, “The scorecards developed by Experian have enabled us to make more reliable risk-based decisions on new applicants and have had a highly beneficial impact in reducing bad debt levels of new customers.”

“The partnership approach taken by Experian while building the scorecards ensured that our business needs were met, that the scorecards are truly representative of our applicant population and that they fit perfectly into our business process.

“Additionally, Experian continues to provide support to ensure the scorecards are performing as expected, giving us confidence that we are continuing to make the correct risk decisions on all applicants.”

Responsible Lending and Affordability

The paper starts by examining recent trends in indebtedness and provides an overview of the main factors driving the responsible lending debate in the UK. It then describes how an automated responsible lending solution can be delivered using a new generic mechanism for estimating disposable income and assessing consumer affordability. Illustrations of how this new responsible lending mechanism works are provided for both mortgage lending and unsecured lending in the prime sector.

Although this paper describes a UK-based responsible lending approach, the lessons learned from the UK have implications for many other developed - and developing - consumer credit markets.

Responsible lending in the unsecured non-prime sector is also discussed. The ability to deliver truly automated responsible lending decisions has implications for all consumer credit markets and some recommendations are given for how this approach could be applied outside the UK.

Modelling personal bankruptcy in the UK

This paper outlines the impact of this increase in individual insolvencies, the research undertaken by Experian and the solutions derived from the research, which are now being adopted by UK lenders to identify and reduce losses from personal bankruptcies.

High levels of consumer indebtedness and the new provisions around personal bankruptcy contained in the Enterprise Act of 2002 have resulted in significant increases in the number of individual bankrupts in the UK.

The issue has become a high profile topic in the press and is seen as a growing problem for the UK economy. Prompted by this increase and the growing concerns of UK lenders about the bad debt caused by bankruptcy, Experian has carried out extensive research into the issue.

The research focused on the differences between bankrupts and other delinquent customers and considered how credit bureau data could be utilised to better identify customers who will become bankrupt in the future. 

Over indebtedness and responsible lending

The paper outlines the indebtedness issue in the UK and details the results of the research, which has led to the development of a number of initiatives from Experian to support the responsible lending practices that are now being pursued by many UK lenders.

The last few years have seen an unprecedented increase in the demand and take up of consumer credit in the UK.  Whilst this consumer credit boom has, on the whole, been very positive for the UK economy, there is now increasing concern regarding overall levels of consumer indebtedness.

This has been fuelled by a number of high profile debt cases reported by the mainstream media. In response to these concerns, the UK Government has set up an interdepartmental Advisory Group to tackle the over indebtedness issue and to encourage consumer lending to be more ‘responsible’.

As the UK’s leading Credit Bureau, Experian, and its Decision Analytics business, has invested heavily in research and development in this area, specifically looking into ways of enabling lending organisations to extend appropriate levels of credit that ensure benefits to both the lender and the borrower.

It is clear in undertaking this research that the depth of consumer data is key to developing the powerful indicators of indebtedness and affordability needed, and that full data sharing, where the Credit Bureaux hold all available consumer credit data, is highly instrumental in this process.

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