Benefits of Using a Credit Decision Engine for Originations

Introduction

In this article we will explore the key benefits of using a credit decision engine in the originations arena.

When comparing the use of a credit decision engine to a traditional, manual credit decision-making process (where no decision engine is used), the advantages of automated decisioning become rapidly apparent.

Here’s a detailed examination of the benefits of employing a credit decision engine over relying on a manual process for originations:

Benefits of a Credit Decision Engine

Efficiency and Speed

• Automated Processing vs. Manual WorkflowsCredit Decision Engine: Automates the entire credit decision-making process, from data collection to final approval or rejection. This automation drastically reduces the time required to process each application, often enabling instant or near-instant decisions. This fast Turnaround Time (TaT) enables the transformation of lenders to digital channels and the adoption of new products, such as BNPL.Manual Process: Relies on human underwriters to review applications, verify data, and make decisions, which can be time-consuming. This process can take days or even weeks, leading to delays in providing customers with a decision.

• ScalabilityCredit Decision Engine: Easily scales to handle high volumes of applications without a corresponding increase in operational costs or staffing. The decision engine can process thousands of applications simultaneously.Manual Process: Scaling up to handle more applications requires hiring and training more underwriters, leading to increased costs and potential bottlenecks during peak times.

Consistency and Accuracy

• Standardised Decision Criteria vs. Human VariabilityCredit Decision Engine: Ensures that the same rules and criteria are applied consistently to every application, reducing the risk of human error and bias. The decision-making process is uniform and objective.Manual Process: Human underwriters may apply criteria inconsistently due to personal biases, fatigue, or different interpretations of guidelines. This can lead to inconsistent decisions and potential fairness issues.

• Data-Driven vs. Intuition-Based DecisionsCredit Decision Engine: Leverages vast amounts of data, including credit scores, financial histories, and alternative data, to make informed decisions. The use of advanced algorithms and predictive models enhances accuracy.Manual Process: Relies heavily on the underwriter’s experience and judgment, which, while valuable, may not always be as precise or comprehensive as data-driven decisions. There’s also a higher risk of overlooking important data points.

Risk Management and Compliance

• Real-Time Risk Assessment vs. Delayed EvaluationCredit Decision Engine: Conducts real-time risk assessments using the most up-to-date data, allowing for immediate adjustments to credit terms or rejections based on the latest information.Manual Process: Risk evaluation may be delayed as it depends on the availability of underwriters and the time needed to analyse the data. This lag can lead to decisions being based on outdated or incomplete information.

• Automated Compliance vs. Manual ChecksCredit Decision Engine: Automatically integrates regulatory requirements into the decision-making process, ensuring compliance with laws and regulations. The decision engine can also generate audit trails for regulatory reporting.Manual Process: Requires underwriters to manually check for compliance with regulations, which is prone to human error. Ensuring consistent adherence to complex and ever-evolving regulations can be challenging.

• Fraud DetectionCredit Decision Engine: Utilises advanced algorithms to detect and prevent fraud by identifying suspicious patterns or anomalies in application data.Manual Process: Relies on the underwriter’s ability to spot inconsistencies, which may not always be effective, particularly in sophisticated fraud cases.

Cost Efficiency

• Lower Operational Costs vs. High Labour CostsCredit Decision Engine: Reduces the need for a large team of underwriters, leading to significant savings in labour costs. Operational overhead is minimised as the system requires less human intervention.Manual Process: Involves higher labour costs due to the need for a sizable team of underwriters. Additionally, manual processes often require more administrative support, further increasing costs.

• Reduced Credit Losses vs. Higher Default RatesCredit Decision Engine: By improving the accuracy of credit assessments and consistently applying risk criteria, the decision engine helps reduce defaults and delinquencies, leading to lower credit losses.Manual Process: The potential for human error and inconsistent decision-making can lead to higher default rates and increased credit losses.

Customer Experience

• Faster Decision Times vs. DelaysCredit Decision Engine: Provides customers with rapid decisions, often in real-time. This speed enhances the customer experience, as applicants appreciate quick responses and clarity on their application status. This results in a higher take-up rate.Manual Process: Delays in decision-making can frustrate customers, leading to dissatisfaction and potentially driving them to competitors who offer faster processing times. This is particularly so in the digital age.

• Transparency and CommunicationCredit Decision Engine: Offers clear explanations for credit decisions, helping customers understand the outcome and improving transparency. Automated notifications keep customers informed at every step.Manual Process: Communication may be slower, and the reasons for decisions might not be as clearly articulated, leading to confusion or dissatisfaction among applicants.

Flexibility and Customisation

• Dynamic Policy Adjustments vs. Static GuidelinesCredit Decision Engine: Enables financial institutions to rapidly adjust credit policies and decision criteria in response to changing market conditions or strategic goals. These adjustments can be implemented system-wide with minimal delay.Manual Process: Changing credit policies and ensuring their consistent application across all underwriters can be slow and cumbersome. It may take time for new guidelines to be fully understood and implemented by all staff.

• Tailored Credit Offers vs. One-Size-Fits-AllCredit Decision Engine: Enables the institution to offer personalised credit products based on individual customer profiles, improving the likelihood of acceptance and customer satisfaction.Manual Process: Customising offers to individual customers is more challenging and time-consuming, often leading to a more generic approach to credit offerings.

Advanced Analytics and Reporting

• Comprehensive Reporting vs. Limited InsightsCredit Decision Engine: Provides detailed analytics and reporting on application trends, decision outcomes, and portfolio performance. These insights help institutions optimise their strategies and make data-driven decisions.Manual Process: Reporting is typically less comprehensive and slower, as it relies on manual data entry and analysis. Insights may be limited, reducing the ability to respond quickly to emerging trends or issues.

• Continuous Improvement vs. Static ProcessesCredit Decision Engine: Continuously learns from new data and outcomes, refining its decision-making algorithms over time. This ongoing improvement enhances accuracy and performance.Manual Process: Improvement is dependent on human learning and training, which is slower and less systematic. Processes tend to be more static and less adaptable to new information or changing conditions.

Strategic Advantages

• Competitive Edge vs. Traditional MethodsCredit Decision Engine: Provides a competitive edge by enabling faster, more accurate, and customer-friendly credit decisions. Institutions using decision engines are often perceived as more innovative and efficient.Manual Process: Sticking to traditional methods may put an institution at a disadvantage, especially if competitors are using more advanced technology to enhance their credit origination processes.

• Innovation and Adaptation vs. Resistance to ChangeCredit Decision Engine: Facilitates the adoption of new technologies and innovative approaches in credit decision-making. It supports continuous adaptation to new market conditions, regulatory changes, and technological advancements.Manual Process: Institutions relying on manual processes may find it harder to innovate or adapt quickly. The inertia of established practices can make it difficult to implement changes or adopt new technologies.

?Portfolio Management and Risk Diversification

• Optimised Portfolio Management vs. Reactive AdjustmentsCredit Decision Engine: Enables real-time monitoring and management of the credit portfolio, allowing institutions to maintain a balanced and diversified portfolio. Proactive adjustments can be made based on ongoing analysis.Manual Process: Portfolio management is often more reactive, with adjustments made after problems are identified rather than in anticipation of them. This can lead to less optimal outcomes and increased risk exposure.

• Strategic Risk DiversificationCredit Decision Engine: Facilitates better risk diversification by accurately segmenting customers based on risk profiles and managing exposure accordingly.Manual Process: Risk diversification strategies may be less precise due to the limitations of manual analysis and the slower pace of decision-making.

Long-Term Sustainability:

• Sustainable Growth vs. Growth LimitationsCredit Decision Engine: Supports sustainable growth by enabling institutions to handle increasing volumes of credit applications efficiently and without compromising quality. The ability to scale operations smoothly contributes to long-term success.Manual Process: Growth is often constrained by the capacity of the underwriting team and the inefficiencies of manual processes. Scaling up may require significant investment in personnel, limiting growth potential.

Summary

In summary, the benefits of using a credit decision engine over a traditional, manual process for originations are extensive. A credit decision engine offers superior efficiency, consistency, risk management, cost savings, customer experience, and strategic flexibility.

In contrast, manual processes are slower, more prone to error, and less adaptable, ultimately putting institutions at a competitive disadvantage in a rapidly evolving and ever-increasing competitive financial landscape.

By investing in a credit decision engine, a lender will achieve a significant return on investment through increased profitability, reduced risk, and a stronger market presence.

About the Author

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

About ADEPT Decisions

We disrupt the status quo in the lending industry by providing clients with customer decisioning, credit risk consulting, predictive modelling and advanced analytics to level the playing field, promote financial inclusion and support a new generation of financial products.

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