Product Updates

Simplify complex workflows with visual decisioning trees on Noble

LAST EDITED: May 04, 2023 | 5 min read

Avishai Steiner

One of the biggest challenges in managing complex financial operations is consolidating its many branches in one place. Decisioning blends data, rules, and predictive modeling to make smart decisions about who to onboard to your platform, who to qualify for credit offers, and how to monitor ongoing customer credibility. Keeping tabs of all of these individual decision components and of the varied milestones along the customer lifecycle is challenging in the absence of a consolidated view. Workflows are the decision trees that guide an entire risk management operation – containing within them the full breadth of checks built into your policies in a unified view. On Noble, workflows allow you to visualize decisioning across the entire customer journey in a single graphical interface.

Today, we are proud to announce the release of our embedded reporting

Consolidation

Visualize your business flow end-to-end in a single interface rather than manage fragmented policies that represent individual steps in your customer journey.

Self-Service

Configure your risk models independently via Noble’s intuitive platform to start mapping statuses and assigning credit on your terms.

Automation

Minimize manual review case volume and reduce human error by leveraging automated decisioning for low risk cases. Save your attention for high-risk edge cases that need it most.

Consolidation

Visualize your business flow end-to-end in a single interface rather than manage fragmented policies that represent individual steps in your customer journey.

Self-Service

Configure your risk models independently via Noble’s intuitive platform to start mapping statuses and assigning credit on your terms.

Automation

Minimize manual review case volume and reduce human error by leveraging automated decisioning for low risk cases. Save your attention for high-risk edge cases that need it most.

The Workflow Builder has been our most anticipated feature release by both clients and prospects. Up until now, our users were limited to managing business flows via a series of conditionally triggered policies. There was a burning need to evolve this into a consolidated alternative that would enable clients to build more complex onboarding and ongoing monitoring flows. The Workflow Builder both addresses this and continues to push forward the self-service agenda we’ve been prioritizing in product development – equipping clients with another key tool that gives them more independence, configuration flexibility, and control over their flows.

Tomer Biger

Noble Co-founder & CEO

Building workflows one node at a time

Workflows are made up of nodes that act as the branches to a decisioning tree. Each branch determines how a specific decisioning use case should be evaluated – whether at the customer onboarding, credit evaluation, or monitoring phase of the lifecycle. Workflow decisions can be segmented into categories and risk bands; this classification can then trigger other actions or workflows.

The following are the foundational elements that make up any workflow built on the Noble platform:

{{table--3}}

{{table--1}}

Program pillar

Dashboard highlights

Examples how to leverage insights

Customers

Customer data gives visibility into customer base growth over time, approval rates, time to decisioning, volume of credit line assignments, and rejection reasons.

  • Optimize on time to decisioning by simplifying cumbersome workflows
  • Leverage rejection reason insights to better target customers with offers; weed out unqualified customers before they enter your funnel

Data sources

Keep track of total reports pulled per provider and which provider products you’re leveraging most.

  • Identify underutilized data sources and where to optimize on use of provider reports
  • Better manage spend by gaining clear visibility into volume of pulled reports from providers on a pay as you go plan

Evaluations

Monitor usage of the Noble platform by keeping tabs on the number of assessments run, missing component rates, and use over time.

  • Leverage learnings from past evaluations to improve the quality of your credit modeling

Scorecards

An overview of evaluation results and averages scores, as well as a granular review of all customer evaluations and data capture results.

  • Leverage insights on common data capture fails to optimize scorecard build; add fall-back mechanisms to account for identified data gaps
{{table--2}}

Scorecard building blocks

Significance

Data

A mix of proprietary and third-party data points that provide criteria for evaluation

VLookups

Scaling mechanisms applied to numeric range or non-numeric data within a rule, allowing for normalized grading of that data

Rules

Functions that leverage grade handling and weighting to apply calculations to the fetched raw data

Weights

Establish the relative importance of one data point over the rest of the data points in any given rule

Section

A group of rules with a united categorization, used to aggregate rules that serve the same use case (i.e. verifying business financials vs. identity)

Grade

The calculated score or numerical output of a rule, used to indicate customer creditworthiness

Variants

Alternate versions of the same scorecard that can be triggered depending on circumstance and use case

Variant Workflows

Logic that defines the conditions where scorecard variants (in the case of their existence) should be triggered

Labels

Score-based classification tags that label customer eligibility based on program scoring thresholds

Benchmark

A figure that shows the distribution of one score relative to the rest in the same label category, allowing you to benchmark one customer relative to a given cohort

{{table--3}}

Operational Element

Definition

Knockouts

Workflow nodes designed to filter out unqualified applications from the workflow, categorizing them as unfit for the specific product use case under evaluation.

Splits

Workflow nodes that split a decisioning tree into branches. These can be conditional (true/false) splits into two branches, or multi-branch for cases like customer segmentation where more than two cases apply.

Actions

Workflow nodes that order the execution of an operation  at the end of a path or at the point of a split; these can trigger a change in status or journey stage, an approval or rejection, an assignment of credit, or another workflow or scorecard.

Data

The mix of proprietary and third-party data points that provide criteria for workflow nodes to assess customer credibility.

Calculations

Numerical, logical, and/or string manipulations that are applied to  raw data and built into workflow nodes.

Pre-built scorecards
and workflows

Scorecards and workflows built for other use cases and saved in the system can be called upon at an action node of any other workflow.

Operationalize complex decisioning

Financial models are rarely simple, linear, or one-size-fits-all. Workflows on Noble support the operationalization of all.

Visual

Visualize complex logic and multi-layered conditional structures. 

Flexible

Configure simple or nested conditions, and comparisons against static values or dynamic parameters.

Smart

Reduce inconclusions and error rates by driving decisioning even in cases of missing data.

Visual

Visualize complex logic and multi-layered conditional structures. 

Flexible

Configure simple or nested conditions, and comparisons against static values or dynamic parameters.

Smart

Reduce inconclusions and error rates by driving decisioning even in cases of missing data.

Get started with Reporting

Noble traces every assessment run in-platform through a comprehensive audit trail that records reason for every decision made. This equips operators with full transparency into automated decision making and deep insights into the strengths and inefficiencies of their modeling and customer journey mapping.

Built for cost optimization

Data retrieval occurs gradually on the platform and is pulled sequentially. When a workflow reaches a node, only the data for that specific node is fetched. In cases where a workflow is terminated early, data used at a later stage of the flow will not be fetched for the unqualified applicant. This data fetching logic optimizes on cost by complementing the pay-as-you-go mechanism enabled with most of our data provider partners. It ensures reports are only pulled in cases where there is still value to evaluating a customer, thus reducing wasteful usage of report quotas. 

‍

Frequently asked questions

What is a scorecard?

A scorecard is a tool used to evaluate a customer’s eligibility for your financial program. It uses data from both third-party and proprietary sources, variables, and custom calculations to build models that verify customer identity, determine risk thresholds, underwrite for credit, and/or determine credit line assignment.

How do I build a credit scorecard?

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.

What is the purpose of a scorecard?

The purpose of a scorecard is to evaluate customer eligibility for the financial product you are building. The scorecard formula applied by your business determines its risk tolerance threshold.

What are the different use cases for a scorecard?

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 

Learn more

Can scorecards be used for non-credit programs?

Scorecards are used to power any financial program – beyond just lending. Since they assess risk at varied points throughout the customer lifecycle, they can be tailored to support the build of any digital or embedded financial product.

What is considered a good or low-risk score?

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.

How does scoring relate to decisioning and assignment?

Scoring determines the grade that ultimately guides decisioning and assignment for any financial product. The grade will automatically assign your customer to a risk band and route him accordingly to the rules, or scenarios, in your workflow that align with his determined profile. Scoring serves as the basis for rejection or approval decisioning, and for assignment of credit where offered.

Frequently asked questions

What is a credit workflow?

A credit workflow concentrates credit management policies, tasks, and decisions along the customer lifecycle into a single digital work process. It is an operational tool that tracks and reports on every customer decisioning instance, from onboarding through to monitoring.

What is the relationship between scorecards and workflows?

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.

Why should I leverage a decisioning engine to power my embedded finance programs rather than build in-house?

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.

Related articles

What are the building blocks of a stellar credit underwriting project?What are the building blocks of a stellar credit underwriting project?
Insights

What are the building blocks of a stellar credit underwriting project?

Tamara Wa'ri
Tamara Wa'ri
Product Manager @ Noble

From onboarding to decisioning and customer management, credit underwriting involves a wide range of processes. This article describes how a single platform can help you overcome the complexities of building, managing, and scaling your credit products.

What are the building blocks of a stellar credit underwriting project?

Tamara Wa'ri
Tamara Wa'ri
Product Manager @ Noble

From onboarding to decisioning and customer management, credit underwriting involves a wide range of processes. This article describes how a single platform can help you overcome the complexities of building, managing, and scaling your credit products.

The three data pillars for building a comprehensive credit operationThe three data pillars for building a comprehensive credit operation
Insights

The three data pillars for building a comprehensive credit operation

Ziv Shabat
Ziv Shabat
VP Analytics

In credit underwriting, the challenge isn’t necessarily finding data about your potential borrowers but rather knowing how to prioritize the most revealing data points, or combinations, to determine and quantify the risk in extending credit within budget.

The three data pillars for building a comprehensive credit operation

Ziv Shabat
Ziv Shabat
VP Analytics

In credit underwriting, the challenge isn’t necessarily finding data about your potential borrowers but rather knowing how to prioritize the most revealing data points, or combinations, to determine and quantify the risk in extending credit within budget.

How to drive accuracy and save time in KYB and credit underwritingHow to drive accuracy and save time in KYB and credit underwriting
Insights

How to drive accuracy and save time in KYB and credit underwriting

Tamara Wa'ri
Tamara Wa'ri
Product Manager @ Noble

As you develop your credit offering, the smallest building blocks can sometimes become huge stumbling blocks. This article shares insights on challenges our partners faced while introducing credit-based products and how we helped overcome them.

How to drive accuracy and save time in KYB and credit underwriting

Tamara Wa'ri
Tamara Wa'ri
Product Manager @ Noble

As you develop your credit offering, the smallest building blocks can sometimes become huge stumbling blocks. This article shares insights on challenges our partners faced while introducing credit-based products and how we helped overcome them.

We wrote the code, you take the credit.

Power customer risk management processes for any embedded financial product with Noble.

Book Demo