Lay the foundations of your financial program with Noble’s Scorecard Builder

How you build your scorecard will vary depending on your business objectives, internal controls, and the credit product being built. Start by clearly defining the objective of your program. Continue by mapping the different scorecard use cases needed to support operations, aligning the varied flows with the different steps of your customer lifecycle – from onboarding through to underwriting, credit assignment and monitoring. Identify the mission-critical data points needed to evaluate the eligibility threshold of customers for your different financial products. Source the data providers that can enrich your proprietary data with 3rd-party data points that fill your data gaps, giving you a more informed view of your customers’ business performance. Then, develop a scorecard framework that builds on top of your selected data mix, applying weighting and grade handling mechanisms to produce a final score.

Once you develop your model, you will need to test and validate before deploying to a live environment. You have two options at this stage: either allocate significant internal development resources to deploy your models entirely in-house, or, leverage the intuitive tools provided by a platform like Noble to simplify the scorecard building process – empowering non-tech teams to easily deploy their modeled scorecards.

Scorecards lie at the core of your program decisioning. They are the rules whose outputs ultimately determine eligibility for your financial product. Your program will be made up of many scorecards, accounting for varied use cases, that align with the different risk-bearing steps of your customer journey.

Broadly speaking, scorecards are built into policies supporting the following use cases:

• Onboarding – verifying customer identity and credibility at the point of onboarding to your platform
• Underwriting – verifying customer eligibility for credit assignment based on financial performance indicators and historical performance
• Monitoring – ongoing customer management checks that detect changes to financial health or to ability to repay debt 

Risk ratings and scoring thresholds vary by client and risk assessing platform. At Noble, scoring  scales are standardized to a range of 1-100. As you build your model in Noble’s platform, you will have the flexibility to set weights to a total of any sum. But upon sending the configuration to production, these will be set to total 100.

There is no standard range within the Noble scale that is considered good or low-risk. These are entirely customizable and subject to your interpretation. From what we’ve observed in live client programs, though, most will flag a 0-60 score as risky, 61-75 as medium risk, and 75+ as low risk.

Scorecards are individual evaluation components. They produce a single numerical grade that positions customers relative to internal benchmarks, and offer a layered, nuanced format for evaluation through which rules can be normalized (to fit a certain scale) and weighted (to reflect each rule’s importance in the scorecard). 

Workflows are more dynamic evaluation tools that are built modularly to support complex decisioning. They are built from unlimited nodes and components that define the actions to be taken at any decisioning instance. Nodes can contain predefined scorecards within them; scores from these are utilized as inputs for decisioning or for segmenting customers according to risk bands, and trigger the execution of correlated corresponding actions. 

Workflows are mature tools that support end-to-end decisioning throughout the customer journey. Scorecards, on the other hand, do not enable the embedding or triggering of other decisioning components like workflows. When used together, scorecards and workflows deliver the ultimate flexibility in decisioning design.

When considering how to implement embedded financial programs, platforms are faced with a dilemma of choosing to build a decisioning engine in-house or to source one from a third-party vendor. Building, maintaining and scaling these operations in-house is complex, time-consuming, and heavily dependent on advanced development resources. Since domain knowledge is maintained by tech-savvy implementers, models cannot be easily reproduced or iterated on, and cross-organization transparency into decisioning is limited.

Combining internal controls with a third-party decisioning engine like Noble’s offers an alternative way to power embedded programs. In leveraging plug-and-play tools and visualizations like workflows, such engines empower non-tech stakeholders to build robust models faster, to adapt to changes and evolving business needs, and to gain full visibility into reason behind every automated decision made.

The workflows needed to support an embedded financial program will depend on the products you’re building, the various steps of your customer journey, and on your risk modeling.

Typically, they cover customer journey phases such as customer onboarding, underwriting (if offering credit), and ongoing monitoring. When building workflows, you have the option to either:

• Build all decisioning instances across the customer lifecycle into a single workflow
• Create separate workflows to represent each key step in your customer journey

Workflows provide a standardized framework to configure and execute on your risk policies. As such, they guide your entire risk operations — enabling you to determine how to review applications, how to segment customers, and what automated actions to take according to categories and risk bands.

Leveraging workflows will help you:

• Unify risk policies across the entire customer journey
• Reduce manual review cases and risk of human error by automating decisioning
• Dedicate more time and resources to your core business
• Deliver a better customer experience by increasing the accuracy and speed of decisioning