Drives smarter credit & faster collection

This solution leverages Machine Learning & Operations Research to design and manage your customer credit terms, balancing risk and reward to maximise profitability through 360’ consideration of all factors at play such cost of debt, seasonality, profit margins, cash cycle risks among other. It is all about moving beyond one-size-fits-all credit limits to create dynamic, customer-specific policies that protect your margins while driving sales growth.

By establishing data-driven credit limits and terms, the module directly accelerates cash flow and shortens the cash conversion cycle. It automates the credit decision-making process, freeing up valuable analyst time from manual reviews and enabling a focus on exceptions.

Ideal for

B2B companies with heavy reliance on credit selling and diverse customer base; having complex credit management processes, and consistent exposure to bad debts and delayed collections.

Manufacturing

Financial Services

• Increased Sales & Competitiveness: Enables strategic extension of credit to creditworthy customers, helping to win and retain key accounts.

• Improved Cash Flow: Accelerates cash collections by focusing efforts on high-risk or overdue accounts and optimizing payment terms.

• Reduced Bad Debt & Write-Offs: Proactively identifies and mitigates customer default risk through data-driven credit decisions.

• Lower Working Capital Requirements: Optimizes the levels of capital tied up in accounts receivable, freeing up cash for other uses.

• Enhanced Customer Relationships: Facilitates fair, consistent, and transparent credit policies, strengthening B2B partnerships.

• Lower Financing Costs: Reduced reliance on external borrowing due to improved internal cash flow.

• Strengthened Risk Management: Provides a structured framework to assess and monitor customer credit risk, reducing exposure to losses.

• Higher Operational Efficiency: Automates credit analysis and monitoring processes, reducing manual effort and administrative costs.

Business Value

Wholesale & Distribution

Multi layer Solution Framework

Advanced

Core

Trade Credit Navigator

MODULES

• During solution design, modules are selected based on client needs and readiness.

• Core modules are prerequisites for advanced modules.

• Module scope varies based on complexity, objectives & deliverables.

A BI module that module that provides a comprehensive & in depth reporting of the trade credit/receivables landscape. Tactical dashboards display financial ratios, payment behavior, exposure levels, portfolio quality, and external risk signals in order to highlight emerging risks and trends for finance leadership. Operational dashboards provide alerts and real-time reporting to guide collection operations.

Credit Health Analysis

• Executive credit health dashboard summarizing portfolio quality, exposure concentrations, aging trends, and high-risk accounts.

• Operational collections dashboard with real-time payment behavior, delinquency alerts, and aging analysis

• Risk tiering & segmentation of customers based on behavioral, financial, and external risk factors.

• Trend and deterioration analysis highlighting emerging risks, payment slowdowns, and portfolio shifts.

• Credit score & external risk integration (e.g., bureau, ratings, market signals) consolidated into unified views.

• Cash collection effectiveness reporting including DSO, CEI, and promise-to-pay performance.

• Financial ratio analysis reports covering liquidity, leverage, profitability, and credit strength indicators.

Core

TN-01

Key Deliverables

Methodologies

Descriptive

• Data ETL & Reconciliation

• Behavioral Analytics

• Financial Ratio Computation

• Anomaly Detection

• Exposure Monitoring Logic

• Real-Time Alerting Framework

• BI Visualization Layer

• Role-Based Access Control (RBAC)

By analyzing historical payment patterns, invoice aging, business volume, and payment incentives, it segments customers based on their actual payment performance (e.g., prompt, slow, strategic late-payers). The output empowers businesses to proactively tailor collection strategies, dynamically adjust credit limits, and accurately forecast cash inflows, thereby strengthening customer relationships while directly reducing Days Sales Outstanding (DSO) and bad debt exposure.

Customer Payment Behaviour Analysis

• Customer Payment Risk Scorecard: A quantifiable risk rating (e.g., High/Medium/Low) for each customer based on their payment history.

• Customer Payment Segmentation: A clear grouping of customers into categories such as "Prompt Payers," "Slow Payers," and "High-Risk."

• Early Warning System: Automated alerts flagging customers who are beginning to deviate from their established payment patterns.

• Collection Action Prioritization List: A dynamic, ranked list of accounts that require immediate collection efforts for maximum impact.

• Customer-Specific Insights Report: Detailed analysis for key accounts.

• Portfolio-Wide Trend Analysis: A report identifying trends across your entire customer base.

• Root Cause Insight Report: An analysis that moves beyond "what" is happening to "why," identifying factors (e.g., specific invoice disputes, service issues, economic cycles) driving payment behavior changes.

• Behavioral Drift Analysis: Tracks and highlights significant changes in a customer's established payment pattern over time,.

Core

TN-02

Key Deliverables

Methodologies

Descriptive & Diagnostic

• Cohort Analysis

• Time-Series Analysis

• Cluster Analysis

• Multivariate Regression

• Trend Analysis and Forecasting

• Statistical Risk Scoring

This module provides a comprehensive framework to stress-test and quantify the risk embedded within a company's credit policy. By simulating the impact of different economic scenarios, customer concentration, and changes to credit terms (like credit limits and payment windows), it evaluates the potential effects on key financial metrics such as bad debt expense, Days Sales Outstanding (DSO), and working capital requirements.

Credit Policy Risk Analysis

• Credit Policy Stress Test Report: Quantifies the potential impact of economic downturns or customer defaults on bad debt and cash flow.

• Customer Concentration Risk Analysis: Identifies over-reliance on a few large customers and models the financial impact if one were to default.

• Credit Term Optimization Scenario: Models the risk/return trade-off of changing standard credit terms (e.g., net 30 to net 45) on sales and DSO.

• Bad Debt Exposure Forecast: A predictive estimate of potential write-offs based on current customer portfolio risk and economic indicators.

• Policy Exception Impact Dashboard: Tracks and analyzes the aggregate risk of all individual credit limit exceptions granted to customers.

• Risk-Adjusted Return on Credit: Calculates the profitability of sales to specific customers or segments after factoring in their cost of risk.

Core

TN-03

Key Deliverables

Methodologies

Diagnostic

• Monte Carlo Simulation

• Scenario & Sensitivity Analysis

• Concentration Risk Modeling (e.g., Herfindahl-Hirschman Index)

• Probability of Default (PD) & Loss Given Default (LGD) Modeling

• Credit Scoring & Segmentation

• Regression Analysis

• Value at Risk (VaR) for Trade Receivables

• Cluster Analysis

An intelligent scoring engine that uses machine-learning algorithms to evaluate customer and counterparty creditworthiness with greater accuracy and speed. It analyzes financial ratios, historical payment behavior, transaction patterns, and external risk data to generate dynamic credit scores and risk tiers. The model continuously learns from new data, improving predictive performance and enabling treasury and finance teams to make faster, more informed credit decisions while reducing exposure and bad-debt risk.

ML-based Credit Scoring Model

• Dynamic credit scores generated using machine-learning algorithms.

• Risk tier classifications (e.g., low/medium/high risk) with score explanations.

• Feature-level insights showing key drivers influencing each score.

• Probability of default (PD) estimates and other predictive risk metrics.

• Customer-level credit profiles combining behavioral, financial, and external data.

• Model accuracy reports including backtesting, performance metrics, and drift detection.

• Real-time scoring API for on-demand credit assessments across systems.

• Batch scoring capability for periodic portfolio-wide credit evaluations.

• Alerting & notifications for score deterioration or emerging high-risk accounts.

• Regulatory-friendly audit trails covering inputs, score outcomes, and model decisions.

Advanced

TN-04

Key Deliverables

Methodologies

Diagnostic

• Supervised Machine Learning Models (e.g., gradient boosting, random forests, logistic regression)

• Feature Engineering & Selection

• Behavioral Pattern Analysis

• Financial Ratio Modeling

• External Risk Signal Integration

• Anomaly & Outlier Detection

• Model Training, Validation & Cross-Validation

• Backtesting & Performance Monitoring

• Model Drift Detection & Recalibration

• Explainability Techniques (SHAP/feature importance)

• Real-Time Scoring Pipeline Architecture

An analytics-driven module that recommends the most effective credit terms for each customer based on payment behavior, risk profile, and financial impact. It evaluates scenarios such as early-payment discounts, extended terms, and dynamic credit limits to optimize cash conversion cycles while balancing revenue, risk, and customer relationships. By quantifying trade-offs and simulating outcomes, the module enables finance teams to set credit terms that maximize liquidity, minimize bad-debt exposure, and support strategic growth

Optimal Credit Terms Calculator

• Customer-specific credit term recommendations based on payment behavior, risk, and financial impact.

• Scenario comparison dashboard evaluating alternative terms (e.g., 30/45/60 days, discounts, dynamic limits).

• Financial impact analysis quantifying effects on CCC, working capital, and liquidity.

• Risk-adjusted term modeling incorporating credit scores, probability of default, and exposure thresholds.

• Sensitivity analysis showing how changes in terms affect DSO, cashflow timing, and risk.

• Discount/early-payment incentive optimizer calculating ROI and adoption impact.

• Portfolio-level credit terms heatmap highlighting inefficiencies and standardization opportunities.

• Actionable recommendations report for finance leadership and credit teams.

• API outputs to push optimal terms to ERP, billing, or credit decisioning systems.

Advanced

TN-03

Key Deliverables

Methodologies

Diagnostic

• Credit Risk Scoring Integration

• Time-Value-of-Money Modeling

• Scenario & Sensitivity Analysis

• Optimization Algorithms

• Dynamic Discount Modeling

• Working Capital Impact Modeling

• Monte Carlo Simulation

• Financial Ratio & DSO Analysis

• API-Driven Decision Engine Architecture

An intelligence-driven module that designs and recommends the most effective collection strategies for each customer segment. By analyzing payment behavior, risk levels, communication outcomes, and historical collection performance, it identifies the optimal timing, channel, and action sequence to maximize recoveries. The module continuously learns from results, enabling finance and collections teams to improve efficiency, reduce DSO, and prioritize efforts where they have the greatest impact.

Collection Strategy Optimizer

• Customer-level strategy recommendations tailored by risk, behavior, and delinquency.

• Strategy playbooks outlining optimal actions, channels, and timing (emails, calls, reminders, escalations).

• Prioritized collections worklists ranked by recovery likelihood, risk, and expected impact.

• Performance impact analysis quantifying expected improvements in DSO, CEI, and recovery rates.

• Real-time adaptive strategy updates based on new customer data and results.

• Collections operations dashboard showing workloads, outcomes, bottlenecks, and optimization opportunities.

• Integration-ready outputs for CRM, ERP, and collections workflow tools.

Advanced

TN-03

Key Deliverables

Methodologies

Diagnostic

• Behavioral Segmentation Algorithms

• Predictive Payment & Delinquency Modeling

• A/B and Multivariate Testing Frameworks

• Recovery Probability Scoring

• Workflow Optimization Algorithms

• Real-Time Adaptive Learning Loops

• Prioritization & Ranking Models

• Integration-Ready Decision Engine Architecture