FAQ

Reklaim is a purpose-built commercial credit intelligence platform developed for the cannabis industry.  The platform combines A/R aging, trade payment history, licensing, regulatory, public filings, UCC filings, liens, judgments, operational indicators, tax situations, and other authorized sources into a unified predictive credit framework.  Unlike traditional credit reporting that primarily describes what already happened, Reklaim identifies forward-looking risk patterns and financial conditions before a payment problem becomes visible through conventional methods.

Reklaim helps operators make more informed decisions regarding trade credit, customer onboarding, credit limits, account monitoring, collections prioritization, and overall portfolio risk management.

Virtually every major industry relies on established commercial credit reporting agencies and decades of accumulated trade payment data.  Cannabis operators face a unique challenge.  Federal regulatory complexities have historically limited the participation of traditional commercial credit reporting infrastructure within the cannabis ecosystem.  As a result, many credit decisions are still made using a combination of A/R aging reports, trade references, searching public records, prior relationships, and management judgment.  While these methods are valuable, they are backward-looking indicators.  Reklaim provides the cannabis industry with a purpose-built credit intelligence framework that incorporates industry-specific data sources and payment behaviors where traditional credit models fall short.

Our predictive models begin with trade payment behavior and A/R aging, extending constructively well beyond these basic foundation.  The platform evaluates all available quantitative and qualitative information:

This process is Multi-Source Data Fusion:  Integration of multiple independent sources in a unified predictive framework.  One simple focused objective:  Produce a comprehensive view of credit risk, more so than any individual data source can provide on its own.  Our north star:  Total situational awareness.  The resulting Reklaim platform outputs:  Immediately informative, proactive, practical recommendations for Reklaim accountholders in their daily financial operations.

Traditional credit reports generally summarize historical information.  Reklaim combines historical information with predictive analytics designed to identify & statistically characterize future economic outcomes.

For example:  Traditional reporting may identify that a customer paid slowly last quarter.  Reklaim determines whether that customer’s overall risk profile is improving, deteriorating, or remaining stable over the next 30, 60, or 90 days.  This distinction is critical.  Credit professionals do not merely need to know what happened yesterday.  They need to identify, detect, quantify, characterize, monitor, and alert what is most likely to happen next.

Reklaim operates as a protected contributory intelligence network.  Participating organizations contribute authorized commercial trade information, which Reklaim combines with numerous other data sources.  As the network grows, and more customers and payment histories are added, risk patterns are identified and reliable accuracy is achieved.

Network effect:  Every additional data contributor increases the overall intelligence available to the system while simultaneously strengthening the predictive capabilities available to all authorized participants.  In short, the network becomes increasingly valuable as coverage is expanded.

At the core of the platform is Reklaim’s proprietary Ensemble Multi-Layer Feedforward Neural Net architecture (E-MLFN™).  This ensemble model combines multiple independent predictive models into a single decision framework.  Rather than relying upon a single algorithm, multiple specialized models evaluate different aspects of credit risk simultaneously, each contributing unique insights, and the combined result is more accurate than any advanced model operating in isolation.

Think of an ensemble model as a panel of experts.  An expert in payment behavior, another in licensing, compliance, public records, legal filings, and customer network behavior.  Each expert model independently evaluates available data, and results are merged using advanced statistical data-fusion to produce the most robust final credit assessment.  The objective is not simply more data; the objective is measurably better-informed decisions.

One of Reklaim’s most unique capabilities is dynamic credit limit optimization.  The predictive models evaluate how changing credit exposure affects projected risk.  By recursively testing credit amounts within the model framework, a recommended credit limit is output that balances growth opportunities vs acceptable risk tolerance.

Credit risk does not end when an invoice is issued.  By continuously evaluating changing conditions (licensing, regulatory actions, UCCs, tax liens, legal filings, payment changes, operational indicators, etc), allows credit managers with early detection of developing concerns, enabling proactive mitigations rather than less-effective reactive remediation efforts.

Systemic Trust is fundamental to Reklaim.  Commercial data is designed to be protected through multiple layers of security, access controls, and data governance procedures.  Data contributors are immediately de-identified upon receipt, prior to use in the Reklaim mathematical models.  Customer lists, proprietary commercial relationships, pricing details, and trade terms are NEVER disclosed to any other participants.  Reklaim enables actionable industry intelligence while securely protecting the confidentiality of ALL participating organizations and their contributed reports and digital assets.  As a matter of certification, Reklaim is currently earning its SOC2 compliance status prior to product launch.

Not every risk factor is equally important for every company.  Bayesian Models dynamically adjust the influence each model receives for the specific company evaluated.  Licensing & regulatory factors carry greater importance for newly established operators.  Network payment behavior carries greater weightings for mature operators with extensive history.

Many predictive systems generate scores.  Reklaim’s Calibration Layer converts raw outputs into statistically validated Probability of Default (PD).  In practical terms, calibration helps ensure that a company with X% default probability behaves like an X% probability risk group over time; this enables consistent credit decisions, accurate monitoring, and reliable credit limit recommendations.  PD describes likelihood of a predefined adverse credit event during a future period, such as severe delinquency, payment default, write-off, or bankruptcy.  PD is widely used and established in commercial trade credit risk because it converts complex reporting into practical assessment of future risk.

Traditional models ask a binary question:  Will a company default?  Survival Analysis answers a more useful question:  When is a company most likely to experience financial distress?  Reklaim provides how much credit is reasonable to extend to a customer.  You benefit directly from an operator’s own A/R history combined with other industry operators doing business with many of the same customers.