Getting Started with Lean Insights APIs
Section 1: Prerequisites
The integration steps are completed by following the:
- Open Banking Integration Guide
- Making the call of any Insight group endpoints is done after the
entity.data.refresh.updatedwebhook has triggered with aFINISHEDstatus, for more detail, see the data workflow guide.
Section 2: Getting Started with Get Income API
The Get Income API extracts income insights from a user's bank transactions. The API identifies salary and non-salary income, calculates stability metrics, and provides month-by-month breakdowns. Please refer to detailed guideline here.
Section 3: Insight Groups Overview
Five Insight Groups are available, each returning structured data objects. Where noted, key parameters within a group are configurable to match your credit policy.
| # | Insight Group | Description |
|---|---|---|
| 1 | Account Controls | Account pre-check signals and configurable thresholds to determine eligibility on any connected account, returning a simple eligible/not-eligible verdict. |
| 2 | Credit Obligations | A structured view of borrowing activity: credit repayments, loan disbursements, and identified lenders. |
| 3 | Cashflow Patterns | Recurring inflow and outflow transactions grouped into structured cashflow patterns, revealing stable inflows and outflows and their frequency over time. |
| 4 | Spending Insights | Enriched and aggregated spend analytics sliced by expense category, merchant, and geography. |
| 5 | Credit Assessments | Aggregated income, expense, and balance data with pre-computed credit indicators: DBR, affordability ratio, net cashflow, installment capacity, calibrated to your policy. |
Section 4: Insight Groups
Account Controls
API Reference: View Documentation →
An easy check to know whether a connected account is the right one to act on. Account Controls is Lean's account eligibility layer, a configurable set of checks that runs on a connected account and returns a simple verdict before any downstream process begins, so you avoid spending on dead leads (bureau calls, salary checks, full underwriting) or pushing users through broken flows because of an invalid account connection.
The check is set up into two parts:
Signals
Gives an overview of the signals detected from the connected account where Inflow (Money In) and Outflow (Money Out):
- Salaried Income: if inflow transactions were detected as salary
- Non-Salary Income: if inflow transactions were detected as non-salary
- Credit Obligations: if outflow transactions were detected as credit repayments
- Loan Disbursement: if inflow transactions were detected as loan disbursements
- Expense Account: if at least 5 outflow transactions were detected per month
Thresholds
Serves as configurable threshold gates as per your defined parameters to check for minimum qualifying criteria over:
- minimum account age (default: 3 months)
- minimum average salaried income (default: 1,000 SAR)
- minimum average non-salaried income (default: 500 SAR)
- minimum current balance (default: 3,000 SAR)
This shows a final verdict as per these thresholds, and based on the criteria, you can choose to use this for:
| Use Case | What It Enables | Example |
|---|---|---|
| Pre-Qualification Gating | Run a lightweight eligibility check before triggering any downstream cost such as bureau calls, income checks, or full underwriting | If a user does not meet the minimum threshold for salary, reject them early on |
| Primary Bank Account Detection | Verify whether the user connected the required account; prompt reconnection or end the user journey as per final verdict, signals, and thresholds | A user connected an account with no salary income, use the signals for a clear user experience to reconnect the correct account |
Example Response
{
"status": "OK",
"results_id": "d982e1fd-aaf0-41fe-9f11-a4f67f4dc918",
"message": "Data successfully retrieved",
"timestamp": "2026-06-29T12:07:48Z",
"type": "account-controls",
"insights": {
"account_controls": {
"signals": {
"account_age_months": 14,
"is_current_account": true,
"has_salary_income": true,
"has_non_salary_income": true,
"has_inflows": true,
"has_loan_repayments": true,
"has_loan_disbursements": false,
"is_expense_account": true
},
"thresholds": {
"meets_minimum_average_salary": true,
"meets_minimum_average_non_salary": true,
"meets_minimum_account_age": true,
"meets_minimum_current_balance": false
},
"verdict": {
"eligible": false
}
}
}
}Credit Obligations
API Reference: View Documentation →
A structured, clean view of a customer's borrowing activity in terms of what they owe, to whom, and what type of facility it is. Lean identifies specific lender names (Al Rajhi, Tamara, SNB, Emirates NBD, etc.) and inflow category (BNPL, personal finance loan, auto loan, mortgage, etc.).
The response is set up into two parts:
Repayments
Every debit transaction going toward a credit product, broken down by identified lender name and tagged with one of the following credit types.
| Credit Type |
|---|
| Cash Loan |
| Auto Loan |
| Mortgage |
| Business Loan |
| Student Loan |
| BNPL |
Disbursements
Credit inflows from lending institutions, useful for identifying recent loan activity or refinancing candidates
Based on what's detected, you can choose to use this for:
| Use Case | What It Enables | Example |
|---|---|---|
| Liability Detection | Identify active credit facilities, the number of lenders, and the types of credit held (bank vs BNPL) | A user holds three active BNPL facilities alongside a personal loan; factor all four into your risk view |
| Cash Loan Detection | Identify recent loan disbursements, borrowing frequency, and total borrowed amounts | A user received two loan disbursements in the last month; flag for a closer affordability review |
Example Response
{
"status": "OK",
"results_id": "6cdf799f-6beb-4fb5-a9de-bf07771c8da3",
"message": "Data successfully retrieved",
"meta": null,
"timestamp": "2026-06-04T07:19:41.702056182Z",
"status_detail": null,
"type": "credit-obligations",
"insights": {
"credit_obligations": {
"repayments": {
"overall": {
"transaction_count": 6,
"total_amount": {
"currency": "SAR",
"amount": 2410.61
},
"average_monthly_amount": {
"currency": "SAR",
"amount": 66.96
},
"average_monthly_amount_active": {
"currency": "SAR",
"amount": 602.65
},
"average_monthly_transactions": 0.17,
"active_months": 4,
"lender_count": 2,
"lender_organizations": [
"TABBY",
"Tamara"
]
},
"breakdown": [
{
"lender_name": "TABBY",
"credit_type": "BNPL",
"transaction_count": 3,
"total_amount": {
"currency": "SAR",
"amount": 1497.92
},
"average_monthly_amount": {
"currency": "SAR",
"amount": 41.61
},
"average_monthly_amount_active": {
"currency": "SAR",
"amount": 748.96
},
"first_transaction_date": "2025-07-27",
"last_transaction_date": "2025-08-26",
"active_months": 2
},
{
"lender_name": "Tamara",
"credit_type": "BNPL",
"transaction_count": 3,
"total_amount": {
"currency": "SAR",
"amount": 912.69
},
"average_monthly_amount": {
"currency": "SAR",
"amount": 25.35
},
"average_monthly_amount_active": {
"currency": "SAR",
"amount": 304.23
},
"first_transaction_date": "2025-06-27",
"last_transaction_date": "2025-09-26",
"active_months": 3
}
]
},
"disbursements": {
"overall": {
"transaction_count": 1,
"total_amount": {
"currency": "AED",
"amount": 44482.5
},
"average_monthly_amount": {
"currency": "AED",
"amount": 1235.63
},
"average_monthly_amount_active": {
"currency": "AED",
"amount": 44482.5
},
"average_monthly_transactions": 0.03,
"active_months": 1,
"lender_count": 1,
"lender_organizations": [
"tamweelaloula"
]
},
"breakdown": [
{
"lender_name": "tamweelaloula",
"transaction_count": 1,
"total_amount": {
"currency": "SAR",
"amount": 44482.5
},
"average_monthly_amount": {
"currency": "SAR",
"amount": 1235.63
},
"average_monthly_amount_active": {
"currency": "SAR",
"amount": 44482.5
},
"first_transaction_date": "2025-07-17",
"last_transaction_date": "2025-07-17",
"active_months": 1
}
]
}
}
}
}Cashflow Patterns
API Reference: View Documentation →
Surfaces the recurring financial behaviors that define how your users manage their money. This group detects recurring transactions on both inflows and outflows, revealing frequency, expected timing, and stability.
For each pattern detected, you get:
- The frequency (weekly, monthly, quarterly, annually)
- The expected day of month or week the payment lands
- The minimum/maximum interval between occurrences
- Whether the pattern is ongoing, recently started, or has stopped
Each pattern is also tagged with a primary and secondary expense category; you can find the full list of categories in the Expense Taxonomy section below.
Based on what's detected, you can choose to use this for things such as:
| Use Case | What It Enables | Example |
|---|---|---|
| Regular Expense Detection | Surface regular financial commitments: subscriptions, allowances, recurring bills | Identify a monthly rent payment and factor it into affordability alongside detected credit obligations |
| Pattern Stability Assessment | Distinguish ongoing patterns from stopped or recently started ones | A previously ongoing salary credit stops appearing; flag the account for review |
Example Response
{
"status": "OK",
"results_id": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
"message": "Data successfully retrieved",
"timestamp": "2026-03-01T12:00:00Z",
"type": "cashflow-patterns",
"insights": {
"cashflow_patterns": [
{
"credit_debit_indicator": "DEBIT",
"primary_category": "Health & Wellbeing",
"secondary_category": "Gym",
"counter_party": "Warehouse Gym",
"total_amount": {
"currency": "SAR",
"amount": 3000
},
"transaction_count": 3,
"monthly_totals": [
{
"month": "2026-01",
"amount": {
"currency": "SAR",
"amount": 1000
},
"transaction_count": 1
},
{
"month": "2026-02",
"amount": {
"currency": "SAR",
"amount": 1000
},
"transaction_count": 1
},
{
"month": "2026-03",
"amount": {
"currency": "SAR",
"amount": 1000
},
"transaction_count": 1
}
],
"regularity": {
"days_of_month_numbers": [
2
],
"days_of_month_names": [
"Sunday"
],
"interval": {
"minimum_days": 27,
"maximum_days": 33
},
"average_interval_days": 30,
"frequency": "MONTHLY",
"activity_status": "ONGOING"
}
},
{
"credit_debit_indicator": "DEBIT",
"primary_category": "Health & Wellbeing",
"secondary_category": "Gym",
"counter_party": "Gym XX",
"total_amount": {
"currency": "SAR",
"amount": 3000
},
"transaction_count": 3,
"monthly_totals": [
{
"month": "2026-01",
"amount": {
"currency": "SAR",
"amount": 1000
},
"transaction_count": 1
},
{
"month": "2026-02",
"amount": {
"currency": "SAR",
"amount": 1000
},
"transaction_count": 1
},
{
"month": "2026-03",
"amount": {
"currency": "SAR",
"amount": 1000
},
"transaction_count": 1
}
],
"regularity": {
"days_of_month_numbers": [
2
],
"days_of_month_names": [
"Monday"
],
"interval": {
"minimum_days": 27,
"maximum_days": 33
},
"average_interval_days": 30,
"frequency": "WEEKLY",
"activity_status": "ONGOING"
}
}
]
}
}Spending Insights
API Reference: View Documentation →
Know exactly where, on what, and with whom your users are spending. Debit transactions are enriched with primary and secondary spend category, specific merchant name, and city, backed by a tiered categorization covering 18 primary and 48 secondary categories, mapped to hundreds of merchants across UAE and KSA. Lean offers this as a commercial intelligence layer to guide clients' commercial and promotional strategy.
The output is sliced three ways to provide spend totals, average monthly spend, transaction frequency, and active months:
- By Category
- By Merchant
- By Location
You can find the full list of primary and secondary expense categories in the Expense Taxonomy section.
Based on what's detected, you can choose to use this for:
| Use Case | What It Enables | Example |
|---|---|---|
| Promotion Strategy & Merchant Acquisition | Identify where spend is concentrated to onboard the right merchant partners or build targeted promotions | Spend is concentrated at a handful of grocery merchants; prioritize those for a cashback partnership |
| Geographic Expansion | Understand city-level spending concentration to prioritize merchant acquisition and localized promotions | Spend activity is highest in Jeddah; prioritize merchant acquisition there first |
| Targeted Post-Purchase Conversion | Detect recent purchases at supported merchants and immediately surface a loan installment offer | A user just purchased at a supported electronics retailer; surface an installment offer for that purchase |
| Spending Dashboards & Personal Finance Management | Power financial health products, budgeting tools, and spending category breakdowns | Show a user their top three spend categories for the month |
Example Response
{
"status": "OK",
"results_id": "d982e1fd-aaf0-41fe-9f11-a4f67f4dc918",
"message": "Data successfully retrieved",
"timestamp": "2026-01-08T12:07:48Z",
"type": "behaviors",
"insights": {
"behaviors": {
"by_category": {
"detected_primary_category_count": 3,
"detected_secondary_category_count": 3,
"transaction_count": 6,
"breakdown": [
{
"primary_category": "Retail",
"secondary_category": "Clothing",
"merchant_names": [
"Zara"
],
"merchant_count": 1,
"total_amount": {
"currency": "SAR",
"amount": 200
},
"average_transaction_amount": {
"currency": "SAR",
"amount": 200
},
"transaction_count": 1,
"average_monthly_transactions": 0.06,
"average_monthly_transactions_active": 1,
"average_monthly_amount": {
"currency": "SAR",
"amount": 11.76
},
"average_monthly_amount_active": {
"currency": "SAR",
"amount": 200
},
"first_transaction_date": "2025-03-10T00:00:00Z",
"last_transaction_date": "2025-03-10T00:00:00Z",
"active_months": 1
},
{
"primary_category": "Transportation",
"secondary_category": "Fuel",
"merchant_names": [
"Shell"
],
"merchant_count": 1,
"total_amount": {
"currency": "SAR",
"amount": 220
},
"average_transaction_amount": {
"currency": "SAR",
"amount": 110
},
"transaction_count": 2,
"average_monthly_transactions": 0.12,
"average_monthly_transactions_active": 1,
"average_monthly_amount": {
"currency": "SAR",
"amount": 12.94
},
"average_monthly_amount_active": {
"currency": "SAR",
"amount": 110
},
"first_transaction_date": "2025-01-20T00:00:00Z",
"last_transaction_date": "2025-02-20T00:00:00Z",
"active_months": 2
},
{
"primary_category": "Food & Dining",
"secondary_category": "Restaurants",
"merchant_names": [
"Albaik"
],
"merchant_count": 1,
"total_amount": {
"currency": "SAR",
"amount": 110
},
"average_transaction_amount": {
"currency": "SAR",
"amount": 55
},
"transaction_count": 2,
"average_monthly_transactions": 0.12,
"average_monthly_transactions_active": 1,
"average_monthly_amount": {
"currency": "SAR",
"amount": 6.47
},
"average_monthly_amount_active": {
"currency": "SAR",
"amount": 55
},
"first_transaction_date": "2025-01-10T00:00:00Z",
"last_transaction_date": "2025-02-10T00:00:00Z",
"active_months": 2
}
]
},
"by_merchant": {
"detected_merchant_count": 3,
"transaction_count": 6,
"breakdown": [
{
"merchant_name": "Albaik",
"transaction_count": 2,
"total_amount": {
"currency": "SAR",
"amount": 110
},
"average_transaction_amount": {
"currency": "SAR",
"amount": 55
},
"average_monthly_transactions": 0.12,
"average_monthly_transactions_active": 1,
"average_monthly_amount": {
"currency": "SAR",
"amount": 6.47
},
"average_monthly_amount_active": {
"currency": "SAR",
"amount": 55
},
"first_transaction_date": "2025-01-10T00:00:00Z",
"last_transaction_date": "2025-02-10T00:00:00Z",
"active_months": 2
},
{
"merchant_name": "Zara",
"transaction_count": 1,
"total_amount": {
"currency": "SAR",
"amount": 200
},
"average_transaction_amount": {
"currency": "SAR",
"amount": 200
},
"average_monthly_transactions": 0.06,
"average_monthly_transactions_active": 1,
"average_monthly_amount": {
"currency": "SAR",
"amount": 11.76
},
"average_monthly_amount_active": {
"currency": "SAR",
"amount": 200
},
"first_transaction_date": "2025-03-10T00:00:00Z",
"last_transaction_date": "2025-03-10T00:00:00Z",
"active_months": 1
},
{
"merchant_name": "Shell",
"transaction_count": 2,
"total_amount": {
"currency": "SAR",
"amount": 220
},
"average_transaction_amount": {
"currency": "SAR",
"amount": 110
},
"average_monthly_transactions": 0.12,
"average_monthly_transactions_active": 1,
"average_monthly_amount": {
"currency": "SAR",
"amount": 12.94
},
"average_monthly_amount_active": {
"currency": "SAR",
"amount": 110
},
"first_transaction_date": "2025-01-20T00:00:00Z",
"last_transaction_date": "2025-02-20T00:00:00Z",
"active_months": 2
}
]
},
"by_location": {
"detected_location_count": 2,
"transaction_count": 6,
"breakdown": [
{
"location": "Riyadh",
"transaction_count": 3,
"total_amount": {
"currency": "SAR",
"amount": 350
},
"average_transaction_amount": {
"currency": "SAR",
"amount": 116.67
},
"average_monthly_transactions": 0.18,
"average_monthly_transactions_active": 1.5,
"average_monthly_amount": {
"currency": "SAR",
"amount": 20.59
},
"average_monthly_amount_active": {
"currency": "SAR",
"amount": 175
},
"first_transaction_date": "2025-01-10T00:00:00Z",
"last_transaction_date": "2025-03-10T00:00:00Z",
"active_months": 2
},
{
"location": "Jeddah",
"transaction_count": 2,
"total_amount": {
"currency": "SAR",
"amount": 180
},
"average_transaction_amount": {
"currency": "SAR",
"amount": 90
},
"average_monthly_transactions": 0.12,
"average_monthly_transactions_active": 2,
"average_monthly_amount": {
"currency": "SAR",
"amount": 10.59
},
"average_monthly_amount_active": {
"currency": "SAR",
"amount": 180
},
"first_transaction_date": "2025-02-10T00:00:00Z",
"last_transaction_date": "2025-02-20T00:00:00Z",
"active_months": 1
}
]
}
}
}
}Taxonomy of Categories (primary & secondary)
Debit transactions are enriched with a primary and secondary expense category from the following list.
| Primary Category | Secondary Category |
|---|---|
| Credit Obligations | Credit Card |
| Credit Obligations | Cash Loan |
| Credit Obligations | Auto Loan |
| Credit Obligations | Mortgage |
| Credit Obligations | Business Loan |
| Credit Obligations | Student Loan |
| Credit Obligations | BNPL |
| Education | Tuition Fees |
| Education | Course |
| Education | Certification |
| Insurance | |
| Government | Government Fees |
| Government | Taxes |
| Groceries | |
| Health & Wellbeing | Medical Services |
| Health & Wellbeing | Gym & Fitness |
| Health & Wellbeing | Wellness Services |
| Health & Wellbeing | Pharmacy Spend |
| Rent | |
| Transportation | Fuel |
| Transportation | Car Maintenance |
| Transportation | Parking & Tolls |
| Transportation | Ride-Hailing |
| Transportation | Public Transport |
| Bills & Utilities | Gas |
| Bills & Utilities | Electricity & Water |
| Bills & Utilities | Internet |
| Bills & Utilities | Cellular Plan |
| Banks Fees & Charges | |
| Entertainment | Movies & Cinemas |
| Entertainment | Gaming |
| Entertainment | Events & Tickets |
| Entertainment | Streaming Platforms |
| Entertainment | Activities |
| Investments | Crypto |
| Investments | Stock Trading |
| Charity | |
| Restaurants & Dining | Restaurants |
| Restaurants & Dining | Cafes & Coffee Shops |
| Restaurants & Dining | Fast Food |
| Restaurants & Dining | Food Delivery |
| Retail | General Shopping |
| Retail | Clothing & Fashion |
| Retail | Home & Living |
| Retail | Electronics |
| Travel | Flights |
| Travel | Hotels/Accomodation |
| Travel | Travel Activities |
| Travel | Visa Fees |
| Transfer | Transfer to Self |
| Transfer | International Transfers |
| Transfer | Transfer to Others |
| Transfer | Cash Withdrawals |
| Other |
Credit Assessments
API Reference: View Documentation →
Lean's fastest path to open banking-powered credit decisions. A fully configurable group that takes raw transaction data and hands back a decision-ready output, computed and calibrated against your policy, ready to plug in and act on. Every feature that feeds into the computed indicators is also returned on its own, so you can use the computed indicators directly or work from the broken-down inputs yourself.
The output covers four layers:
Computed Indicators
Two credit ratios that can be used, each calibrated against a configured ceiling, with a maximum monthly installment returned for each:
DBR (Debt Burden Ratio)
average monthly credit obligations ÷ contributed monthly income
Affordability Ratio
(average monthly essential expenses + average monthly credit obligations) ÷ contributed monthly income
Income
Configurable income sources, contribution factors, and aggregation method, combined into a single monthly income figure:
- Choose which income categories are factored in (salary, gig income, investments, etc.)
- Choose how much of each category is factored in, and how it's calculated: the minimum monthly value across the analysis period, or the average monthly value across the period
Expenses
Expenses are broken down into:
- Credit Obligations
- Essential Spend
- Discretionary Spend
You can configure which expense categories get factored as essential spend categories and can then feed into the affordability ratio.
Balance
Average, minimum, and maximum balance over the analysis period.
Configuration Summary
| # | Category | Parameter | Description |
|---|---|---|---|
| 1 | Income Sources | included_other_income_sources | Non-employment income categories to include in calculations (e.g. GIG_ECONOMY, INVESTMENTS) |
| 2 | Income Weighting | contribution_factor | How much each income source contributes to calculations, per your credit policy |
| 3 | Expense Categories | defined_essential_categories | Categories classified as essential drive the affordability ratio |
| 4 | DBR Ceiling | dbr_ceiling | Maximum acceptable DBR per your credit policy |
| 5 | Affordability Ceiling | affordability_ceiling | Maximum acceptable affordability ratio per your credit policy |
Based on what's computed, you can choose to use this for:
| Use Case | What It Enables | Example |
|---|---|---|
| Automated Affordability Assessment | Pre-computed DBR and affordability ratios already reflecting your credit policy | A user's DBR comes back at 28% against a 35% ceiling; auto-approve without manual review |
| Installment Capacity | The exact maximum monthly installment before breaching your defined thresholds | Use the returned maximum monthly installment directly as the offer cap for a new loan |
| Full Financial Picture | Configured features for income, expenses, and balance in a single structured output | Pull income, expenses, and balance in one call |
Example Response
{
"status": "OK",
"results_id": "b2c3d4e5-f6a7-8901-bcde-f12345678901",
"message": "Data successfully retrieved",
"timestamp": "2026-03-01T12:00:00Z",
"type": "credit-assessments",
"insights": {
"credit_assessments": {
"income": {
"employment_income": {
"breakdown": [
{
"source": "EMPLOYMENT",
"total_amount": {
"currency": "SAR",
"amount": 31200
},
"average_monthly_amount": {
"currency": "SAR",
"amount": 5200
},
"minimum_monthly_amount": {
"currency": "SAR",
"amount": 5000
},
"maximum_monthly_amount": {
"currency": "SAR",
"amount": 5500
},
"contribution_factor": 1,
"contributed_monthly_income": {
"currency": "SAR",
"amount": 5200,
"method": "AVERAGE"
}
}
]
},
"other_income": {
"breakdown": [
{
"source": "GIG_ECONOMY",
"total_amount": {
"currency": "SAR",
"amount": 3000
},
"average_monthly_amount": {
"currency": "SAR",
"amount": 500
},
"minimum_monthly_amount": {
"currency": "SAR",
"amount": 200
},
"maximum_monthly_amount": {
"currency": "SAR",
"amount": 800
},
"contribution_factor": 0.5,
"contributed_monthly_income": {
"currency": "SAR",
"amount": 250,
"method": "AVERAGE"
}
}
]
},
"total_contributed_monthly_income": {
"currency": "SAR",
"amount": 5450
}
},
"expenses": {
"average_monthly_credit_repayments": {
"currency": "SAR",
"amount": 900
},
"average_monthly_essential_expenses": {
"currency": "SAR",
"amount": 2200
},
"average_monthly_discretionary_expenses": {
"currency": "SAR",
"amount": 1300
},
"average_monthly_total_expenses": {
"currency": "SAR",
"amount": 4400
},
"defined_essential_categories": [
"RENT",
"TRANSPORTATION"
]
},
"balance": {
"average_balance": {
"currency": "SAR",
"amount": 4200
},
"minimum_balance": {
"currency": "SAR",
"amount": 800
},
"maximum_balance": {
"currency": "SAR",
"amount": 7200
}
},
"computed_indicators": {
"average_monthly_net_cashflow": {
"currency": "SAR",
"amount": 1050
},
"dbr": {
"rate": 0.165,
"ceiling": 0.5,
"maximum_monthly_installment": {
"currency": "SAR",
"amount": 1825
}
},
"affordability": {
"rate": 0.569,
"ceiling": 0.7,
"maximum_monthly_installment": {
"currency": "SAR",
"amount": 2349
}
}
}
}
}
}Section 5: Custom Insights
Beyond the standard catalog, custom insight groups can be built to fit requirements not covered out of the box. Reach out to the Lean account team to discuss custom requirements.
Updated 2 days ago