Welcome
How mature is your credit risk assessment strategy?
Businesses today are under constant pressure to make quick, accurate, and consistent decisions for credit evaluations. The manual processes businesses rely on require weeks of employees’ time to process new applications for credit, screen for fraudulent credit applications, and report on compliance metrics. As a result, 85% of business decision makers are looking to invest in an integrated credit risk solution.
How is your organization navigating these pressures? Take our short self-assessment to find out.
The assessment will yield customized results and recommendations based on your responses and should take no more than 2 minutes to complete.
Questions
What are your organization’s plans regarding integrating analytics, automated decisioning, machine learning (ML), and/or artificial intelligence (AI) into your risk management strategy?
Questions
How confident is your organization in its current approach to evaluating credit risk?
By “credit risk,” we mean evaluating how credit-worthy an organization is to grant credit, financing, and/or capital. This includes decisioning.
Questions
How do you measure the success of your credit risk evaluation efforts? (Select all that apply.)
Questions
Which of the following challenges do you face with the data sources you use in evaluating risk data and making decisions? (Select all that apply.)
Questions
How interested are you in adopting an integrated credit risk solution that uses automated decisioning and/or advanced analytics to combine online and offline data into meaningful insights about customers’ identities and that is tailored to your business processes?
Questions
What outcomes do you anticipate from improving credit risk evaluation at your organization? (Select all that apply.)
Results Overview
Results Overview
- Strategy: Does your organization have a clear payment security strategy?
- Customer focus: How customer-focused is your company's risk/fraud strategy?
- Skills and technology: Is your organization investing in the right skills and technology?
- Governance: Does your organization have a strong governance practice?
Recommendations
Your maturity result: Beginner
Beginner
Your score means your credit risk management maturity is only at the beginning stage.
- Your investments in integrating analytics, automated decisioning, machine learning (ML), and/or artificial intelligence (AI) into your risk management strategy are in the early stages.
- You are not investing in credit risk management holistically; you are focused on individual components, but not the big picture.
- Data is siloed and not empowering decisioning.
Recommendations
- Reassess the accuracy of rule sets.
- Consolidate credit risk data used for decision from at least 50% of your channels.
- Start improving data quality.
Recommendations
Your maturity result: Intermediate
Intermediate
Your score means your credit risk management maturity has advanced to the intermediate stage.
- You have either already invested in integrating analytics, automated decisioning, machine learning (ML), and/or artificial intelligence (AI) into your risk management strategy or are planning to do so in the next 12 months.
- However, you are not necessarily holistically incorporating your organization into its strategy.
- You are exploring credit risk management with a methodical approach and expanding its impact in your organization.
Recommendations
- Consolidate credit risk data used for decision from at least 80% of your channels.
- Ascertain improved data quality.
- Use consortium data for decisioning.
- Build a framework for risk scoring model governance.
- Consider using both business owner data and business data for evaluating risk.
Recommendations
Your maturity result: Advanced
Advanced
Congratulations! Your score means that your credit risk management strategy is advanced.
- You use credit risk management to propel your business forward, advancing your business model and continuously evolving your strategy.
- You are expanding or upgrading your implementation of automated decisioning, machine learning (ML), and/or artificial intelligence (AI) into your risk management strategy.
- Credit risk management initiatives are pervasive throughout your organization.
Recommendations
- Expand ML to use both supervised and unsupervised algorithms.
- Ensure explainability of ML algorithms.
- Focus on agility of the business including how better risk scoring can improve sales, core operations, etc.
View your detailed results
Next steps
Download your results
Ready to get started?
To learn more, contact your Experian account executive or bisexperian@experian.com.
Methodology, Disclaimers and Disclosures
Methodology, Disclaimers and Disclosures
Methodology
Methodology
In this study, Forrester surveyed 165 respondents in the US to evaluate their current approaches to credit risk evaluations. Survey participants included decision makers at small and medium-size businesses (SMBs) and large enterprises involved in their organizations’ risk and credit services strategies. Respondents were offered a small incentive as a thank you for time spent on the survey. The study began in July 2019 and was completed in August 2019.
Disclaimers
Although we’ve taken great care to ensure the accuracy and completeness of this assessment, Experian and Forrester are unable to accept any legal responsibility for any actions taken on the basis of the information contained herein.
Disclosures
This interactive tool is commissioned by Experian and delivered by Forrester Consulting.