What makes KM2 different from traditional collections approaches are the implementation of our own unique, innovative, tested and proven methods.

Jackdaw™ Analytics

Taking Identification to Predictive Confidence

Jackdaw™ Analytics is our unique analytics process, that is able to identify accounts where there is a high likelihood that the credit abuse was deliberate, first-party fraud, not merely financial hardship. Jackdaw™ advanced analytics from KM2 has been refined over many years to address the issues inherent in identifying wilful or opportunistic fraud and misappropriated credit, using the data to back-propagate onboarding and account management tools.

Jackdaw™ Analytics analyses your historical data to:

  • Provide customised feedback on your portfolio characteristics
  • Identify with confidence those accounts likely to be fraudulent
  • Refine your fraud definitions to be the most appropriate for your organisation
  • Strategy for the collection of fraudulently obtained debts
  • Inform account onboarding measures to reduce the incidence of First-Party Fraud
  • Inform account management measures to further manage out First-Party Fraud
  • Provide clearly defined KPI’s to report to regulators on progress in First-Party Fraud management
  • Give management a confident propensity-to-pay score for all accounts
  • Build a long-term and adaptable analytics capability

Contextual Evaluation Theory

The art of behavioural psychology applied to undermine fabricated realities and actuate assentive responses.

CET™ is a radical new collections approach which is proven to achieve 30%+ recoveries where other approaches have failed.

To do this, CET™:

  • Examines the evidence from the Jackdaw™ Analytics
  • Tests the resolve of the debtor
  • Breaks the inconsistent story provided
  • But all in a completely non-confrontational manner.

CET™ does this through:

  • Determining and breaking the absence of an experimental anchor
  • Removing the ability to rationalise
  • Providing the means to resolve and close


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