What are the Oraculi credit risk rule settings?

Understanding Oraculi credit risk rules in Lendsqr

Oraculi credit risk rules are the foundation of how lending decisions are made within Lendsqr. They define both how a borrower is evaluated and what loan offer is presented if the borrower passes the required checks.

A complete credit risk rule is made up of two primary components. The first is the settings, which control how the entire model behaves. The second is the individual settings within each credit risk rule, which define the specific rules used to evaluate borrowers.

In addition to these, there is also the offer setting, which determines the loan terms presented to the borrower after they successfully pass the decision checks.

All checks performed during decisioning are recorded and displayed under a user’s loan profile as Decision Data. This provides transparency into how each decision was made and allows lenders to review the outcome of each credit risk rule.

Structure of an Oraculi credit risk rule

An Oraculi credit risk rule is structured to separate evaluation logic from outcome configuration.

The credit risk rule settings handle the overall flow of the model, including how rules are arranged and how they interact with each other. The credit risk rules themselves contain the rules and data checks used to assess borrower eligibility.

The offer settings come into play after a borrower passes the defined checks. These settings determine what loan offer is presented, including the amount, tenure, and other terms.

This separation ensures that lenders can independently manage how decisions are made and how offers are structured.

Understanding credit risk rules

Credit risk rules are system-level components used to implement risk assessment rules for a loan product. Each rule evaluates a specific type of data or behavior and contributes to the overall decision outcome.

Lenders can combine multiple credit risk rules within a single decision model to create a layered and robust evaluation process.

Karma

The Karma credit risk rule connects to a large private blacklist database of known bad actors and chronic defaulters. It is designed to prevent high-risk individuals from accessing loans.

Karma can be configured to check multiple identifiers such as email addresses, phone numbers, BVN, and IP addresses. This allows lenders to detect fraudulent or high-risk users across different data points.

This rule is available free of charge to Lendsqr lenders and can also be accessed via API by non-Lendsqr lenders.

Ecosystem

The Ecosystem credit risk rule checks borrower activity across the entire Lendsqr data ecosystem. It uses the BVN as the primary identifier to retrieve this information.

This rule helps lenders determine whether a borrower has outstanding obligations with other lenders on the platform. For example, it can identify if a borrower currently owes another lender within the ecosystem.

Ecosystem functions similarly to a credit bureau but provides deeper insights specific to Lendsqr’s network. It is available free of charge to Lendsqr lenders.

Loci

The Loci credit risk rule enables lenders to create rules based on high-frequency behavioral data.

It allows you to track how often a borrower makes certain changes, such as updating their income or submitting multiple loan requests within a short period. This type of velocity data is useful for identifying suspicious or risky behavior.

Loci includes a wide range of data points and provides flexibility in defining dynamic rules. It is also free for Lendsqr lenders.

Scoring

The Scoring credit risk rule uses a traditional weighted scoring approach to evaluate borrowers.

Lenders assign numeric values to different data points, such as employment type, income level, or repayment history. Each parameter contributes to a total score.

Borrowers whose total score exceeds a predefined threshold proceed to the next stage of evaluation. Those who fall below the threshold are filtered out.

This rule allows lenders to apply structured risk models and is particularly useful for creating standardised credit policies.

Credit bureau

The Credit Bureau credit risk rule allows lenders to retrieve real-time credit data from licensed credit bureaus in Nigeria.

Lenders can configure one or multiple bureaus to run checks simultaneously. Currently supported bureaus include CRC, CreditRegistry, and First Central.

Unlike other rules, this service is not free and is charged on a per-use basis. It is typically used for deeper credit verification and regulatory compliance.

Credit risk rules summary table

Rule	Description	Availability
Karma	Taps into one of the largest private blacklist databases of bad actors and chronic defaulters — protecting billions of loans for lenders each month. Can be configured to check emails, phone numbers, BVNs, IP addresses, and more.	Free for Lendsqr lenders API access for others
Ecosystem	Checks the entire Lendsqr data ecosystem to see if a borrower meets certain criteria during decisioning. Uses BVN as the primary key to look up borrower activity with other lenders on the platform — similar to credit bureau data but more comprehensive. Read more about the Ecosystem data dictionary.	Free for Lendsqr lenders API access for others
Loci	Allows lenders to write smart rules against high-speed velocity data. Instantly check how often a borrower has made changes to their information — such as loan requests or net income updates. Includes over 80 data points.	Free for Lendsqr lenders API access for others
Scoring	A traditional weighted, risk-adjusted scoring method where lenders assign numeric weights to data points from loan requests, Ecosystem, Loci, and more. Borrowers whose total score exceeds the configured threshold proceed to the next rule.	Free for Lendsqr lenders API access for others
Credit bureau	Retrieves real-time credit data from any of our licensed credit bureaus. Lenders can configure a single bureau or run multiple simultaneously. Currently supports CRC, CreditRegistry, and First Central for Nigerian lenders and Transunion, Equifax for non-Nigerian lenders.	Charged per use

Read more: 5 types of lending model

Credit risk rule settings

Credit risk rule settings control how all rules within the model operate together. These settings define the flow, behavior, and logic of the entire decisioning process.

Within the credit risk rule settings, you can configure the sequence of rules. This determines the order in which checks are performed. For example, you may choose to run a blacklist check before performing scoring or credit bureau checks.

You can also define key attributes within each rule. For instance, in the Scoring rule, you can set a minimum score threshold that a borrower must meet to proceed.

Another important configuration is the continue on failure condition. This determines whether a borrower proceeds to the next rule after failing a check.

If this setting is set to false, the borrower is immediately disqualified upon failing that rule. If set to true, the borrower continues to the next rule despite the failure. This allows for more flexible decisioning strategies.

These settings give lenders full control over how strict or flexible their credit risk rules are.

Step by step guide to configuring credit risk rules settings

Follow the steps below to configure credit risk rules settings effectively.

Step 1: Access the credit risk rules configuration

Log in to your admin console and navigate to the Credit Risk Rules section under Product Management.

Select the model you want to configure or create a new one if needed. Ensure that you are working on the correct model linked to your loan product.

Step 2: Define the module sequence

Within the settings, arrange the credit risk modules in the desired order.

This sequence determines how borrower checks are executed. Place critical checks, such as fraud detection or blacklist verification, early in the sequence to filter out high-risk users quickly.

Step 3: Configure module-specific attributes

Open each module and define its specific parameters.

For example, in the scoring module, set the minimum score threshold. In other modules, define the criteria that determine whether a borrower passes or fails.

Ensure that each configuration aligns with your risk policy.

Step 4: Set continue on failure conditions

For each module, decide whether borrowers should proceed if they fail the check.

Set this option to false if the module represents a strict requirement. Set it to true if the module is part of a broader evaluation where failure does not immediately disqualify the borrower.

Step 5: Save and review the credit risk rule

After configuring all modules and settings, save your changes.

Review the entire model to ensure that the sequence, thresholds, and conditions align with your intended credit risk logic.

Common errors and how to fix them

One common issue is incorrect module sequencing. Placing less critical checks before essential ones can lead to inefficient decisioning. To fix this, reorder modules so that high-impact checks occur first.

Another issue is setting unrealistic thresholds, especially in the scoring module. If thresholds are too high, many eligible borrowers may be rejected. If too low, risk exposure increases. Regularly review and adjust thresholds based on performance data.

Misuse of the continue on failure setting is also common. Setting it incorrectly can either make the model too strict or too lenient. Review each module’s role and adjust this setting accordingly.

Failure to save configurations is another frequent problem. Always confirm that changes are saved before exiting the setup interface.

Lastly, relying too heavily on a single module can weaken the model. A balanced combination of modules provides more reliable decision outcomes.

Best practices for configuring redit risk rules

Start with a clear definition of your risk strategy before configuring the model. This ensures that all modules and settings align with your business objectives.

Use multiple modules to create a layered decisioning approach. Combining fraud checks, behavioral data, and scoring models leads to more accurate assessments.

Regularly review decision data from user loan profiles. This helps you understand how your model is performing and identify areas for improvement.

Test your model with different borrower scenarios before deploying it. This allows you to validate logic and ensure consistent outcomes.

Finally, keep your model as simple as possible while still meeting your requirements. Overly complex models can be difficult to manage and may introduce unintended errors.

Conclusion

Oraculi credit risk rules provide a powerful and flexible framework for evaluating borrower eligibility and determining loan offers within Lendsqr.

By combining credit risk modules, configuring module settings, and defining offer parameters, lenders can create tailored decisioning systems that align with their risk appetite.

Following the correct setup process, avoiding common errors, and applying best practices ensures that your credit risk rule remains effective, efficient, and scalable as your lending operations grow.