What to Consider when Implementing a Decision Engine

Introduction

In the previous article we examined the topic of best practice decision engine implementation. In this article we will review what are the many considerations that need to be made when implementing a decision engine?

Implementing a credit decision engine involves navigating through various commercial, credit risk, and technical considerations, in order to ensure its successful deployment and operation. Here’s an in-depth exploration of each category:

Commercial Considerations:

  • Market Positioning and Competitive Advantage
    • Value Proposition: Define how the credit decision engine will differentiate your offerings in the market (e.g., faster approvals, better terms)
  • Market Demand: Assess the market demand for automated credit decisioning solutions and tailor the decision engine to meet specific customer needs
  • Revenue Impact and Cost Management
    • Revenue Generation: Evaluate potential revenue streams from increased loan approvals, cross-selling opportunities, or premium services
    • Cost Efficiency: Calculate implementation costs, including software development, data integration, and training, versus expected operational savings and revenue gains
  • Partnerships and Vendor Selection
    • Technology Partners: Identify and collaborate with technology vendors or service providers with expertise in credit decisioning systems
    • Integration Support: Ensure compatibility and seamless integration with existing systems (e.g., loan origination platforms, CRM systems)
  • Regulatory Compliance and Risk Management
    • Compliance Framework: Establish a compliance strategy to adhere to regulatory requirements (e.g., GDPR) and mitigate legal risks associated with credit decisions
    • Risk Assessment: Conduct thorough risk assessments to understand potential risks related to data privacy, security breaches, and regulatory penalties
  • Customer Experience and Satisfaction
    • User Interface: Design an intuitive user interface for stakeholders (e.g., underwriters, customer service) to efficiently interact with the credit decision engine
    • Service Levels: Define service level agreements (SLAs) for response times and customer support to ensure a positive customer experience

Credit Risk Considerations:

  • Risk Assessment and Scoring Models
    • Credit Scoring Models: Select and implement appropriate scoring models (e.g., traditional credit scores, machine learning-based models) based on data availability and predictive accuracy
    • Model Validation: Validate models using historical data to ensure they accurately predict creditworthiness and risk
  • Decision Rules and Policies
    • Risk Appetite: Define risk appetite thresholds and policies to guide credit decisions (e.g., maximum allowable default rate, risk-adjusted pricing strategies)
    • Decision Automation: Automate decision-making processes while ensuring they align with risk management policies and regulatory requirements
  • Portfolio Management
    • Portfolio Diversification: Monitor and manage credit portfolios to ensure diversification and mitigate against concentration risk
    • Stress Testing: Conduct stress tests to assess portfolio resilience under adverse economic conditions and identify potential vulnerabilities
  • Monitoring and Reporting
    • Monitoring Tools: Implement tools to continuously monitor credit performance metrics (e.g., delinquency rates, loss ratios) and identify emerging risks
  • Reporting Capabilities: Develop robust reporting capabilities to provide insights into portfolio health, credit trends, and performance against targets

Technical Considerations:

  • Data Management and Integration
    • Data Sources: Identify and integrate diverse data sources (e.g., applicant information, credit bureau data, alternative data) to enhance decision-making accuracy
    • Data Quality: Ensure data quality through cleansing, validation, and normalisation processes to improve the reliability of credit assessments
  • Model Development and Deployment
    • Model Selection: Choose appropriate modelling techniques (e.g., logistic regression, decision trees, neural networks) based on data characteristics and business requirements
    • Model Deployment: Deploy models in a scalable and efficient manner, considering factors such as computational resources and real-time processing requirements
  • Scalability and Performance
    • System Architecture: Design a scalable architecture to handle increasing volumes of credit applications and transactions, without compromising performance
    • Response Time: Optimise algorithms and data processing workflows to ensure quick response times and seamless user experience
  • Security and Compliance
    • Data Security: Implement robust security measures (e.g., encryption, access controls) to protect sensitive customer information and comply with all data protection regulations
    • Compliance Audits: Conduct regular audits and assessments to ensure the credit decision engine meets regulatory requirements and internal policies
  • Integration with External Systems
    • API Design: Design APIs and interfaces for seamless integration with external systems (e.g., loan origination platforms, credit bureaux) to exchange data and decision outcomes
    • Legacy System Compatibility: Ensure compatibility with legacy systems to facilitate a smooth data flow and interoperability
Summary

It is best practice to address these commercial, credit risk, and technical considerations before and during the implementation of a credit decision engine. By doing so, credit grantors will build a robust and efficient system that enhances their decision-making capabilities.

By following these well-established best practices, lenders will also improve their risk management practices, and support business growth objectives, while ensuring regulatory compliance and maintaining customer trust.

About the Author

Stephen John Leonard is the founder of ADEPT Decisions and has held a wide range of roles in the banking and credit risk industry since 1985.